Update Eigen3 to 3.1BETA

132 files changed, 23908 insertions, 5 deletions

diff --git a/extern/Eigen3/Eigen/CMakeLists.txt b/extern/Eigen3/Eigen/CMakeLists.txt
new file mode 100644
index 00000000000..a92dd6f6caf
--- /dev/null
+++ b/extern/Eigen3/Eigen/CMakeLists.txt
@@ -0,0 +1,19 @@
+include(RegexUtils)
+test_escape_string_as_regex()
+
+file(GLOB Eigen_directory_files "*")
+
+escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
+
+foreach(f ${Eigen_directory_files})
+  if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/src")
+    list(APPEND Eigen_directory_files_to_install ${f})
+  endif()
+endforeach(f ${Eigen_directory_files})
+
+install(FILES
+  ${Eigen_directory_files_to_install}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen COMPONENT Devel
+  )
+
+add_subdirectory(src)
diff --git a/extern/Eigen3/Eigen/CholmodSupport b/extern/Eigen3/Eigen/CholmodSupport
new file mode 100644
index 00000000000..0e1ff48e033
--- /dev/null
+++ b/extern/Eigen3/Eigen/CholmodSupport
@@ -0,0 +1,40 @@
+#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
+#define EIGEN_CHOLMODSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+  #include <cholmod.h>
+}
+
+/** \ingroup Support_modules
+  * \defgroup CholmodSupport_Module CholmodSupport module
+  *
+  * This module provides an interface to the Cholmod library which is part of the <a href="http://www.cise.ufl.edu/research/sparse/SuiteSparse/">suitesparse</a> package.
+  * It provides the two following main factorization classes:
+  * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
+  * - class CholmodDecomposiiton: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).
+  *
+  * For the sake of completeness, this module also propose the two following classes:
+  * - class CholmodSimplicialLLT
+  * - class CholmodSimplicialLDLT
+  * Note that these classes does not bring any particular advantage compared to the built-in
+  * SimplicialLLT and SimplicialLDLT factorization classes.
+  *
+  * \code
+  * #include <Eigen/CholmodSupport>
+  * \endcode
+  */
+
+#include "src/misc/Solve.h"
+#include "src/misc/SparseSolve.h"
+
+#include "src/CholmodSupport/CholmodSupport.h"
+
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_CHOLMODSUPPORT_MODULE_H
+
diff --git a/extern/Eigen3/Eigen/Core b/extern/Eigen3/Eigen/Core
index 5d803490005..5e93d6b73f2 100644
--- a/extern/Eigen3/Eigen/Core
+++ b/extern/Eigen3/Eigen/Core
@@ -142,7 +142,7 @@
 #endif
 
 // MSVC for windows mobile does not have the errno.h file
-#if !(defined(_MSC_VER) && defined(_WIN32_WCE))
+#if !(defined(_MSC_VER) && defined(_WIN32_WCE)) && !defined(__ARMCC_VERSION)
 #define EIGEN_HAS_ERRNO
 #endif
 
diff --git a/extern/Eigen3/Eigen/IterativeLinearSolvers b/extern/Eigen3/Eigen/IterativeLinearSolvers
new file mode 100644
index 00000000000..315c2dd1ee7
--- /dev/null
+++ b/extern/Eigen3/Eigen/IterativeLinearSolvers
@@ -0,0 +1,40 @@
+#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
+#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
+
+#include "SparseCore"
+#include "OrderingMethods"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \ingroup Sparse_modules
+  * \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
+  *
+  * This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a squared matrix, usually very large and sparse.
+  * Those solvers are accessible via the following classes:
+  *  - ConjugateGradient for selfadjoint (hermitian) matrices,
+  *  - BiCGSTAB for general square matrices.
+  *
+  * These iterative solvers are associated with some preconditioners:
+  *  - IdentityPreconditioner - not really useful
+  *  - DiagonalPreconditioner - also called JAcobi preconditioner, work very well on diagonal dominant matrices.
+  *  - IncompleteILUT - incomplete LU factorization with dual thresholding
+  *
+  * Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport.
+  *
+  * \code
+  * #include <Eigen/IterativeLinearSolvers>
+  * \endcode
+  */
+
+#include "src/misc/Solve.h"
+#include "src/misc/SparseSolve.h"
+
+#include "src/IterativeLinearSolvers/IterativeSolverBase.h"
+#include "src/IterativeLinearSolvers/BasicPreconditioners.h"
+#include "src/IterativeLinearSolvers/ConjugateGradient.h"
+#include "src/IterativeLinearSolvers/BiCGSTAB.h"
+#include "src/IterativeLinearSolvers/IncompleteLUT.h"
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
diff --git a/extern/Eigen3/Eigen/OrderingMethods b/extern/Eigen3/Eigen/OrderingMethods
new file mode 100644
index 00000000000..1e2d87452e5
--- /dev/null
+++ b/extern/Eigen3/Eigen/OrderingMethods
@@ -0,0 +1,23 @@
+#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
+#define EIGEN_ORDERINGMETHODS_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \ingroup Sparse_modules
+  * \defgroup OrderingMethods_Module OrderingMethods module
+  *
+  * This module is currently for internal use only.
+  *
+  *
+  * \code
+  * #include <Eigen/OrderingMethods>
+  * \endcode
+  */
+
+#include "src/OrderingMethods/Amd.h"
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_ORDERINGMETHODS_MODULE_H
diff --git a/extern/Eigen3/Eigen/PaStiXSupport b/extern/Eigen3/Eigen/PaStiXSupport
new file mode 100644
index 00000000000..1dbb0941b75
--- /dev/null
+++ b/extern/Eigen3/Eigen/PaStiXSupport
@@ -0,0 +1,34 @@
+#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
+#define EIGEN_PASTIXSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <complex.h>
+extern "C" {
+#include <pastix_nompi.h>
+#include <pastix.h>
+}
+
+#ifdef complex
+#undef complex
+#endif
+
+/** \ingroup Support_modules
+  * \defgroup PaStiXSupport_Module PaStiXSupport module
+  *
+  * \code
+  * #include <Eigen/PaStiXSupport>
+  * \endcode
+  */
+
+#include "src/misc/Solve.h"
+#include "src/misc/SparseSolve.h"
+
+#include "src/PaStiXSupport/PaStiXSupport.h"
+
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_PASTIXSUPPORT_MODULE_H
diff --git a/extern/Eigen3/Eigen/PardisoSupport b/extern/Eigen3/Eigen/PardisoSupport
new file mode 100644
index 00000000000..6585edb7410
--- /dev/null
+++ b/extern/Eigen3/Eigen/PardisoSupport
@@ -0,0 +1,26 @@
+#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
+#define EIGEN_PARDISOSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <mkl_pardiso.h>
+
+#include <unsupported/Eigen/SparseExtra>
+
+/** \ingroup Support_modules
+  * \defgroup PardisoSupport_Module PardisoSupport module
+  *
+  * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers
+  *
+  * \code
+  * #include <Eigen/PardisoSupport>
+  * \endcode
+  */
+
+#include "src/PardisoSupport/PardisoSupport.h"
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_PARDISOSUPPORT_MODULE_H
diff --git a/extern/Eigen3/Eigen/SparseCholesky b/extern/Eigen3/Eigen/SparseCholesky
new file mode 100644
index 00000000000..5f82742f7d8
--- /dev/null
+++ b/extern/Eigen3/Eigen/SparseCholesky
@@ -0,0 +1,30 @@
+#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
+#define EIGEN_SPARSECHOLESKY_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \ingroup Sparse_modules
+  * \defgroup SparseCholesky_Module SparseCholesky module
+  *
+  * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian) matrices.
+  * Those decompositions are accessible via the following classes:
+  *  - SimplicialLLt,
+  *  - SimplicialLDLt
+  *
+  * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module.
+  *
+  * \code
+  * #include <Eigen/SparseCholesky>
+  * \endcode
+  */
+
+#include "src/misc/Solve.h"
+#include "src/misc/SparseSolve.h"
+
+#include "src/SparseCholesky/SimplicialCholesky.h"
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SPARSECHOLESKY_MODULE_H
diff --git a/extern/Eigen3/Eigen/SparseCore b/extern/Eigen3/Eigen/SparseCore
new file mode 100644
index 00000000000..41d28c92824
--- /dev/null
+++ b/extern/Eigen3/Eigen/SparseCore
@@ -0,0 +1,66 @@
+#ifndef EIGEN_SPARSECORE_MODULE_H
+#define EIGEN_SPARSECORE_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <vector>
+#include <map>
+#include <cstdlib>
+#include <cstring>
+#include <algorithm>
+
+/** \ingroup Sparse_modules
+  * \defgroup SparseCore_Module SparseCore module
+  *
+  * This module provides a sparse matrix representation, and basic associatd matrix manipulations
+  * and operations.
+  *
+  * See the \ref TutorialSparse "Sparse tutorial"
+  *
+  * \code
+  * #include <Eigen/SparseCore>
+  * \endcode
+  *
+  * This module depends on: Core.
+  */
+
+namespace Eigen {
+
+/** The type used to identify a general sparse storage. */
+struct Sparse {};
+
+}
+
+#include "src/SparseCore/SparseUtil.h"
+#include "src/SparseCore/SparseMatrixBase.h"
+#include "src/SparseCore/CompressedStorage.h"
+#include "src/SparseCore/AmbiVector.h"
+#include "src/SparseCore/SparseMatrix.h"
+#include "src/SparseCore/MappedSparseMatrix.h"
+#include "src/SparseCore/SparseVector.h"
+#include "src/SparseCore/CoreIterators.h"
+#include "src/SparseCore/SparseBlock.h"
+#include "src/SparseCore/SparseTranspose.h"
+#include "src/SparseCore/SparseCwiseUnaryOp.h"
+#include "src/SparseCore/SparseCwiseBinaryOp.h"
+#include "src/SparseCore/SparseDot.h"
+#include "src/SparseCore/SparsePermutation.h"
+#include "src/SparseCore/SparseAssign.h"
+#include "src/SparseCore/SparseRedux.h"
+#include "src/SparseCore/SparseFuzzy.h"
+#include "src/SparseCore/ConservativeSparseSparseProduct.h"
+#include "src/SparseCore/SparseSparseProductWithPruning.h"
+#include "src/SparseCore/SparseProduct.h"
+#include "src/SparseCore/SparseDenseProduct.h"
+#include "src/SparseCore/SparseDiagonalProduct.h"
+#include "src/SparseCore/SparseTriangularView.h"
+#include "src/SparseCore/SparseSelfAdjointView.h"
+#include "src/SparseCore/TriangularSolver.h"
+#include "src/SparseCore/SparseView.h"
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SPARSECORE_MODULE_H
+
diff --git a/extern/Eigen3/Eigen/SuperLUSupport b/extern/Eigen3/Eigen/SuperLUSupport
new file mode 100644
index 00000000000..09aa1b6cad0
--- /dev/null
+++ b/extern/Eigen3/Eigen/SuperLUSupport
@@ -0,0 +1,49 @@
+#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
+#define EIGEN_SUPERLUSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#ifdef EMPTY
+#define EIGEN_EMPTY_WAS_ALREADY_DEFINED
+#endif
+
+typedef int int_t;
+#include <slu_Cnames.h>
+#include <supermatrix.h>
+#include <slu_util.h>
+
+// slu_util.h defines a preprocessor token named EMPTY which is really polluting,
+// so we remove it in favor of a SUPERLU_EMPTY token.
+// If EMPTY was already defined then we don't undef it.
+
+#if defined(EIGEN_EMPTY_WAS_ALREADY_DEFINED)
+# undef EIGEN_EMPTY_WAS_ALREADY_DEFINED
+#elif defined(EMPTY)
+# undef EMPTY
+#endif
+
+#define SUPERLU_EMPTY (-1)
+
+namespace Eigen { struct SluMatrix; }
+
+/** \ingroup Support_modules
+  * \defgroup SuperLUSupport_Module SuperLUSupport module
+  *
+  * \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined because it is too polluting.
+  *
+  * \code
+  * #include <Eigen/SuperLUSupport>
+  * \endcode
+  */
+
+#include "src/misc/Solve.h"
+#include "src/misc/SparseSolve.h"
+
+#include "src/SuperLUSupport/SuperLUSupport.h"
+
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SUPERLUSUPPORT_MODULE_H
diff --git a/extern/Eigen3/Eigen/UmfPackSupport b/extern/Eigen3/Eigen/UmfPackSupport
new file mode 100644
index 00000000000..aac361e3f17
--- /dev/null
+++ b/extern/Eigen3/Eigen/UmfPackSupport
@@ -0,0 +1,30 @@
+#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
+#define EIGEN_UMFPACKSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <umfpack.h>
+}
+
+/** \ingroup Support_modules
+  * \defgroup UmfPackSupport_Module UmfPackSupport module
+  *
+  *
+  *
+  *
+  * \code
+  * #include <Eigen/UmfPackSupport>
+  * \endcode
+  */
+
+#include "src/misc/Solve.h"
+#include "src/misc/SparseSolve.h"
+
+#include "src/UmfPackSupport/UmfPackSupport.h"
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_UMFPACKSUPPORT_MODULE_H
diff --git a/extern/Eigen3/Eigen/src/CMakeLists.txt b/extern/Eigen3/Eigen/src/CMakeLists.txt
new file mode 100644
index 00000000000..c326f374d9e
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/CMakeLists.txt
@@ -0,0 +1,7 @@
+file(GLOB Eigen_src_subdirectories "*")
+escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
+foreach(f ${Eigen_src_subdirectories})
+  if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" )
+    add_subdirectory(${f})
+  endif()
+endforeach()
diff --git a/extern/Eigen3/Eigen/src/Cholesky/CMakeLists.txt b/extern/Eigen3/Eigen/src/Cholesky/CMakeLists.txt
new file mode 100644
index 00000000000..d01488b41a8
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Cholesky/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Cholesky_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Cholesky_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Cholesky COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/Cholesky/LLT_MKL.h b/extern/Eigen3/Eigen/src/Cholesky/LLT_MKL.h
new file mode 100644
index 00000000000..64daa445cf7
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Cholesky/LLT_MKL.h
@@ -0,0 +1,102 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *     LLt decomposition based on LAPACKE_?potrf function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_LLT_MKL_H
+#define EIGEN_LLT_MKL_H
+
+#include "Eigen/src/Core/util/MKL_support.h"
+#include <iostream>
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename Scalar> struct mkl_llt;
+
+#define EIGEN_MKL_LLT(EIGTYPE, MKLTYPE, MKLPREFIX) \
+template<> struct mkl_llt<EIGTYPE> \
+{ \
+  template<typename MatrixType> \
+  static inline typename MatrixType::Index potrf(MatrixType& m, char uplo) \
+  { \
+    lapack_int matrix_order; \
+    lapack_int size, lda, info, StorageOrder; \
+    EIGTYPE* a; \
+    eigen_assert(m.rows()==m.cols()); \
+    /* Set up parameters for ?potrf */ \
+    size = m.rows(); \
+    StorageOrder = MatrixType::Flags&RowMajorBit?RowMajor:ColMajor; \
+    matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
+    a = &(m.coeffRef(0,0)); \
+    lda = m.outerStride(); \
+\
+    info = LAPACKE_##MKLPREFIX##potrf( matrix_order, uplo, size, (MKLTYPE*)a, lda ); \
+    info = (info==0) ? Success : NumericalIssue; \
+    return info; \
+  } \
+}; \
+template<> struct llt_inplace<EIGTYPE, Lower> \
+{ \
+  template<typename MatrixType> \
+  static typename MatrixType::Index blocked(MatrixType& m) \
+  { \
+    return mkl_llt<EIGTYPE>::potrf(m, 'L'); \
+  } \
+  template<typename MatrixType, typename VectorType> \
+  static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
+  { return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } \
+}; \
+template<> struct llt_inplace<EIGTYPE, Upper> \
+{ \
+  template<typename MatrixType> \
+  static typename MatrixType::Index blocked(MatrixType& m) \
+  { \
+    return mkl_llt<EIGTYPE>::potrf(m, 'U'); \
+  } \
+  template<typename MatrixType, typename VectorType> \
+  static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \
+  { \
+    Transpose<MatrixType> matt(mat); \
+    return llt_inplace<EIGTYPE, Lower>::rankUpdate(matt, vec.conjugate(), sigma); \
+  } \
+};
+
+EIGEN_MKL_LLT(double, double, d)
+EIGEN_MKL_LLT(float, float, s)
+EIGEN_MKL_LLT(dcomplex, MKL_Complex16, z)
+EIGEN_MKL_LLT(scomplex, MKL_Complex8, c)
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_LLT_MKL_H
diff --git a/extern/Eigen3/Eigen/src/CholmodSupport/CMakeLists.txt b/extern/Eigen3/Eigen/src/CholmodSupport/CMakeLists.txt
new file mode 100644
index 00000000000..814dfa61313
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/CholmodSupport/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_CholmodSupport_SRCS "*.h")
+
+INSTALL(FILES 
+  ${Eigen_CholmodSupport_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/CholmodSupport COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/CholmodSupport/CholmodSupport.h b/extern/Eigen3/Eigen/src/CholmodSupport/CholmodSupport.h
new file mode 100644
index 00000000000..a06c429f054
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/CholmodSupport/CholmodSupport.h
@@ -0,0 +1,594 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_CHOLMODSUPPORT_H
+#define EIGEN_CHOLMODSUPPORT_H
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename Scalar, typename CholmodType>
+void cholmod_configure_matrix(CholmodType& mat)
+{
+  if (internal::is_same<Scalar,float>::value)
+  {
+    mat.xtype = CHOLMOD_REAL;
+    mat.dtype = CHOLMOD_SINGLE;
+  }
+  else if (internal::is_same<Scalar,double>::value)
+  {
+    mat.xtype = CHOLMOD_REAL;
+    mat.dtype = CHOLMOD_DOUBLE;
+  }
+  else if (internal::is_same<Scalar,std::complex<float> >::value)
+  {
+    mat.xtype = CHOLMOD_COMPLEX;
+    mat.dtype = CHOLMOD_SINGLE;
+  }
+  else if (internal::is_same<Scalar,std::complex<double> >::value)
+  {
+    mat.xtype = CHOLMOD_COMPLEX;
+    mat.dtype = CHOLMOD_DOUBLE;
+  }
+  else
+  {
+    eigen_assert(false && "Scalar type not supported by CHOLMOD");
+  }
+}
+
+} // namespace internal
+
+/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
+  * Note that the data are shared.
+  */
+template<typename _Scalar, int _Options, typename _Index>
+cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
+{
+  typedef SparseMatrix<_Scalar,_Options,_Index> MatrixType;
+  cholmod_sparse res;
+  res.nzmax   = mat.nonZeros();
+  res.nrow    = mat.rows();;
+  res.ncol    = mat.cols();
+  res.p       = mat.outerIndexPtr();
+  res.i       = mat.innerIndexPtr();
+  res.x       = mat.valuePtr();
+  res.sorted  = 1;
+  if(mat.isCompressed())
+  {
+    res.packed  = 1;
+  }
+  else
+  {
+    res.packed  = 0;
+    res.nz = mat.innerNonZeroPtr();
+  }
+
+  res.dtype   = 0;
+  res.stype   = -1;
+  
+  if (internal::is_same<_Index,int>::value)
+  {
+    res.itype = CHOLMOD_INT;
+  }
+  else
+  {
+    eigen_assert(false && "Index type different than int is not supported yet");
+  }
+
+  // setup res.xtype
+  internal::cholmod_configure_matrix<_Scalar>(res);
+  
+  res.stype = 0;
+  
+  return res;
+}
+
+template<typename _Scalar, int _Options, typename _Index>
+const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>& mat)
+{
+  cholmod_sparse res = viewAsCholmod(mat.const_cast_derived());
+  return res;
+}
+
+/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
+  * The data are not copied but shared. */
+template<typename _Scalar, int _Options, typename _Index, unsigned int UpLo>
+cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<SparseMatrix<_Scalar,_Options,_Index>, UpLo>& mat)
+{
+  cholmod_sparse res = viewAsCholmod(mat.matrix().const_cast_derived());
+  
+  if(UpLo==Upper) res.stype =  1;
+  if(UpLo==Lower) res.stype = -1;
+
+  return res;
+}
+
+/** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix.
+  * The data are not copied but shared. */
+template<typename Derived>
+cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
+{
+  EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  typedef typename Derived::Scalar Scalar;
+
+  cholmod_dense res;
+  res.nrow   = mat.rows();
+  res.ncol   = mat.cols();
+  res.nzmax  = res.nrow * res.ncol;
+  res.d      = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
+  res.x      = mat.derived().data();
+  res.z      = 0;
+
+  internal::cholmod_configure_matrix<Scalar>(res);
+
+  return res;
+}
+
+/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
+  * The data are not copied but shared. */
+template<typename Scalar, int Flags, typename Index>
+MappedSparseMatrix<Scalar,Flags,Index> viewAsEigen(cholmod_sparse& cm)
+{
+  return MappedSparseMatrix<Scalar,Flags,Index>
+         (cm.nrow, cm.ncol, reinterpret_cast<Index*>(cm.p)[cm.ncol],
+          reinterpret_cast<Index*>(cm.p), reinterpret_cast<Index*>(cm.i),reinterpret_cast<Scalar*>(cm.x) );
+}
+
+enum CholmodMode {
+  CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt
+};
+
+
+/** \ingroup CholmodSupport_Module
+  * \class CholmodBase
+  * \brief The base class for the direct Cholesky factorization of Cholmod
+  * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
+  */
+template<typename _MatrixType, int _UpLo, typename Derived>
+class CholmodBase : internal::noncopyable
+{
+  public:
+    typedef _MatrixType MatrixType;
+    enum { UpLo = _UpLo };
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef MatrixType CholMatrixType;
+    typedef typename MatrixType::Index Index;
+
+  public:
+
+    CholmodBase()
+      : m_cholmodFactor(0), m_info(Success), m_isInitialized(false)
+    {
+      cholmod_start(&m_cholmod);
+    }
+
+    CholmodBase(const MatrixType& matrix)
+      : m_cholmodFactor(0), m_info(Success), m_isInitialized(false)
+    {
+      cholmod_start(&m_cholmod);
+      compute(matrix);
+    }
+
+    ~CholmodBase()
+    {
+      if(m_cholmodFactor)
+        cholmod_free_factor(&m_cholmodFactor, &m_cholmod);
+      cholmod_finish(&m_cholmod);
+    }
+    
+    inline Index cols() const { return m_cholmodFactor->n; }
+    inline Index rows() const { return m_cholmodFactor->n; }
+    
+    Derived& derived() { return *static_cast<Derived*>(this); }
+    const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was succesful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    Derived& compute(const MatrixType& matrix)
+    {
+      analyzePattern(matrix);
+      factorize(matrix);
+      return derived();
+    }
+    
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const internal::solve_retval<CholmodBase, Rhs>
+    solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      eigen_assert(rows()==b.rows()
+                && "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::solve_retval<CholmodBase, Rhs>(*this, b.derived());
+    }
+    
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const internal::sparse_solve_retval<CholmodBase, Rhs>
+    solve(const SparseMatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      eigen_assert(rows()==b.rows()
+                && "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::sparse_solve_retval<CholmodBase, Rhs>(*this, b.derived());
+    }
+    
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      * 
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    {
+      if(m_cholmodFactor)
+      {
+        cholmod_free_factor(&m_cholmodFactor, &m_cholmod);
+        m_cholmodFactor = 0;
+      }
+      cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
+      m_cholmodFactor = cholmod_analyze(&A, &m_cholmod);
+      
+      this->m_isInitialized = true;
+      this->m_info = Success;
+      m_analysisIsOk = true;
+      m_factorizationIsOk = false;
+    }
+    
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& matrix)
+    {
+      eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+      cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
+      cholmod_factorize(&A, m_cholmodFactor, &m_cholmod);
+      
+      this->m_info = Success;
+      m_factorizationIsOk = true;
+    }
+    
+    /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations.
+     *  See the Cholmod user guide for details. */
+    cholmod_common& cholmod() { return m_cholmod; }
+    
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal */
+    template<typename Rhs,typename Dest>
+    void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
+    {
+      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
+      const Index size = m_cholmodFactor->n;
+      eigen_assert(size==b.rows());
+
+      // note: cd stands for Cholmod Dense
+      cholmod_dense b_cd = viewAsCholmod(b.const_cast_derived());
+      cholmod_dense* x_cd = cholmod_solve(CHOLMOD_A, m_cholmodFactor, &b_cd, &m_cholmod);
+      if(!x_cd)
+      {
+        this->m_info = NumericalIssue;
+      }
+      // TODO optimize this copy by swapping when possible (be carreful with alignment, etc.)
+      dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols());
+      cholmod_free_dense(&x_cd, &m_cholmod);
+    }
+    
+    /** \internal */
+    template<typename RhsScalar, int RhsOptions, typename RhsIndex, typename DestScalar, int DestOptions, typename DestIndex>
+    void _solve(const SparseMatrix<RhsScalar,RhsOptions,RhsIndex> &b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
+    {
+      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
+      const Index size = m_cholmodFactor->n;
+      eigen_assert(size==b.rows());
+
+      // note: cs stands for Cholmod Sparse
+      cholmod_sparse b_cs = viewAsCholmod(b);
+      cholmod_sparse* x_cs = cholmod_spsolve(CHOLMOD_A, m_cholmodFactor, &b_cs, &m_cholmod);
+      if(!x_cs)
+      {
+        this->m_info = NumericalIssue;
+      }
+      // TODO optimize this copy by swapping when possible (be carreful with alignment, etc.)
+      dest = viewAsEigen<DestScalar,DestOptions,DestIndex>(*x_cs);
+      cholmod_free_sparse(&x_cs, &m_cholmod);
+    }
+    #endif // EIGEN_PARSED_BY_DOXYGEN
+    
+    template<typename Stream>
+    void dumpMemory(Stream& s)
+    {}
+    
+  protected:
+    mutable cholmod_common m_cholmod;
+    cholmod_factor* m_cholmodFactor;
+    mutable ComputationInfo m_info;
+    bool m_isInitialized;
+    int m_factorizationIsOk;
+    int m_analysisIsOk;
+};
+
+/** \ingroup CholmodSupport_Module
+  * \class CholmodSimplicialLLT
+  * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
+  * using the Cholmod library.
+  * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Thefore, it has little practical interest.
+  * The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
+  * X and B can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  *
+  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
+  *
+  * \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLLT
+  */
+template<typename _MatrixType, int _UpLo = Lower>
+class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> >
+{
+    typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT> Base;
+    using Base::m_cholmod;
+    
+  public:
+    
+    typedef _MatrixType MatrixType;
+    
+    CholmodSimplicialLLT() : Base() { init(); }
+
+    CholmodSimplicialLLT(const MatrixType& matrix) : Base()
+    {
+      init();
+      compute(matrix);
+    }
+
+    ~CholmodSimplicialLLT() {}
+  protected:
+    void init()
+    {
+      m_cholmod.final_asis = 0;
+      m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+      m_cholmod.final_ll = 1;
+    }
+};
+
+
+/** \ingroup CholmodSupport_Module
+  * \class CholmodSimplicialLDLT
+  * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
+  * using the Cholmod library.
+  * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Thefore, it has little practical interest.
+  * The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
+  * X and B can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  *
+  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
+  *
+  * \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLDLT
+  */
+template<typename _MatrixType, int _UpLo = Lower>
+class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> >
+{
+    typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT> Base;
+    using Base::m_cholmod;
+    
+  public:
+    
+    typedef _MatrixType MatrixType;
+    
+    CholmodSimplicialLDLT() : Base() { init(); }
+
+    CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
+    {
+      init();
+      compute(matrix);
+    }
+
+    ~CholmodSimplicialLDLT() {}
+  protected:
+    void init()
+    {
+      m_cholmod.final_asis = 1;
+      m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+    }
+};
+
+/** \ingroup CholmodSupport_Module
+  * \class CholmodSupernodalLLT
+  * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
+  * using the Cholmod library.
+  * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
+  * The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
+  * X and B can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  *
+  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
+  *
+  * \sa \ref TutorialSparseDirectSolvers
+  */
+template<typename _MatrixType, int _UpLo = Lower>
+class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> >
+{
+    typedef CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT> Base;
+    using Base::m_cholmod;
+    
+  public:
+    
+    typedef _MatrixType MatrixType;
+    
+    CholmodSupernodalLLT() : Base() { init(); }
+
+    CholmodSupernodalLLT(const MatrixType& matrix) : Base()
+    {
+      init();
+      compute(matrix);
+    }
+
+    ~CholmodSupernodalLLT() {}
+  protected:
+    void init()
+    {
+      m_cholmod.final_asis = 1;
+      m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
+    }
+};
+
+/** \ingroup CholmodSupport_Module
+  * \class CholmodDecomposition
+  * \brief A general Cholesky factorization and solver based on Cholmod
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
+  * using the Cholmod library. The sparse matrix A must be selfajoint and positive definite. The vectors or matrices
+  * X and B can be either dense or sparse.
+  *
+  * This variant permits to change the underlying Cholesky method at runtime.
+  * On the other hand, it does not provide access to the result of the factorization.
+  * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  *
+  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
+  *
+  * \sa \ref TutorialSparseDirectSolvers
+  */
+template<typename _MatrixType, int _UpLo = Lower>
+class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> >
+{
+    typedef CholmodBase<_MatrixType, _UpLo, CholmodDecomposition> Base;
+    using Base::m_cholmod;
+    
+  public:
+    
+    typedef _MatrixType MatrixType;
+    
+    CholmodDecomposition() : Base() { init(); }
+
+    CholmodDecomposition(const MatrixType& matrix) : Base()
+    {
+      init();
+      compute(matrix);
+    }
+
+    ~CholmodDecomposition() {}
+    
+    void setMode(CholmodMode mode)
+    {
+      switch(mode)
+      {
+        case CholmodAuto:
+          m_cholmod.final_asis = 1;
+          m_cholmod.supernodal = CHOLMOD_AUTO;
+          break;
+        case CholmodSimplicialLLt:
+          m_cholmod.final_asis = 0;
+          m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+          m_cholmod.final_ll = 1;
+          break;
+        case CholmodSupernodalLLt:
+          m_cholmod.final_asis = 1;
+          m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
+          break;
+        case CholmodLDLt:
+          m_cholmod.final_asis = 1;
+          m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+          break;
+        default:
+          break;
+      }
+    }
+  protected:
+    void init()
+    {
+      m_cholmod.final_asis = 1;
+      m_cholmod.supernodal = CHOLMOD_AUTO;
+    }
+};
+
+namespace internal {
+  
+template<typename _MatrixType, int _UpLo, typename Derived, typename Rhs>
+struct solve_retval<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
+  : solve_retval_base<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
+{
+  typedef CholmodBase<_MatrixType,_UpLo,Derived> Dec;
+  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve(rhs(),dst);
+  }
+};
+
+template<typename _MatrixType, int _UpLo, typename Derived, typename Rhs>
+struct sparse_solve_retval<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
+  : sparse_solve_retval_base<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs>
+{
+  typedef CholmodBase<_MatrixType,_UpLo,Derived> Dec;
+  EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve(rhs(),dst);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_CHOLMODSUPPORT_H
diff --git a/extern/Eigen3/Eigen/src/Core/Assign_MKL.h b/extern/Eigen3/Eigen/src/Core/Assign_MKL.h
new file mode 100644
index 00000000000..f96b0eb229a
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/Assign_MKL.h
@@ -0,0 +1,221 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin()
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_ASSIGN_VML_H
+#define EIGEN_ASSIGN_VML_H
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename Op> struct vml_call
+{ enum { IsSupported = 0 }; };
+
+template<typename Dst, typename Src, typename UnaryOp>
+class vml_assign_traits
+{
+  private:
+    enum {
+      DstHasDirectAccess = Dst::Flags & DirectAccessBit,
+      SrcHasDirectAccess = Src::Flags & DirectAccessBit,
+
+      StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
+      InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
+                : int(Dst::Flags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
+                : int(Dst::RowsAtCompileTime),
+      InnerMaxSize  = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
+                    : int(Dst::Flags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
+                    : int(Dst::MaxRowsAtCompileTime),
+      MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
+
+      MightEnableVml =  vml_call<UnaryOp>::IsSupported && StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess
+                     && Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1,
+      MightLinearize = MightEnableVml && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit),
+      VmlSize = MightLinearize ? MaxSizeAtCompileTime : InnerMaxSize,
+      LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD,
+      MayEnableVml = MightEnableVml && LargeEnough,
+      MayLinearize = MayEnableVml && MightLinearize
+    };
+  public:
+    enum {
+      Traversal = MayLinearize ? LinearVectorizedTraversal
+                : MayEnableVml ? InnerVectorizedTraversal
+                : DefaultTraversal
+    };
+};
+
+template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling,
+         int VmlTraversal = vml_assign_traits<Derived1, Derived2, UnaryOp>::Traversal >
+struct vml_assign_impl
+  : assign_impl<Derived1, Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn>
+{
+};
+
+template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling>
+struct vml_assign_impl<Derived1, Derived2, UnaryOp, Traversal, Unrolling, InnerVectorizedTraversal>
+{
+  typedef typename Derived1::Scalar Scalar;
+  typedef typename Derived1::Index Index;
+  static inline void run(Derived1& dst, const CwiseUnaryOp<UnaryOp, Derived2>& src)
+  {
+    // in case we want to (or have to) skip VML at runtime we can call:
+    // assign_impl<Derived1,Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn>::run(dst,src);
+    const Index innerSize = dst.innerSize();
+    const Index outerSize = dst.outerSize();
+    for(Index outer = 0; outer < outerSize; ++outer) {
+      const Scalar *src_ptr = src.IsRowMajor ?  &(src.nestedExpression().coeffRef(outer,0)) :
+                                                &(src.nestedExpression().coeffRef(0, outer));
+      Scalar *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer));
+      vml_call<UnaryOp>::run(src.functor(), innerSize, src_ptr, dst_ptr );
+    }
+  }
+};
+
+template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling>
+struct vml_assign_impl<Derived1, Derived2, UnaryOp, Traversal, Unrolling, LinearVectorizedTraversal>
+{
+  static inline void run(Derived1& dst, const CwiseUnaryOp<UnaryOp, Derived2>& src)
+  {
+    // in case we want to (or have to) skip VML at runtime we can call:
+    // assign_impl<Derived1,Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn>::run(dst,src);
+    vml_call<UnaryOp>::run(src.functor(), dst.size(), src.nestedExpression().data(), dst.data() );
+  }
+};
+
+// Macroses
+
+#define EIGEN_MKL_VML_SPECIALIZE_ASSIGN(TRAVERSAL,UNROLLING) \
+  template<typename Derived1, typename Derived2, typename UnaryOp> \
+  struct assign_impl<Derived1, Eigen::CwiseUnaryOp<UnaryOp, Derived2>, TRAVERSAL, UNROLLING, Specialized>  {  \
+    static inline void run(Derived1 &dst, const Eigen::CwiseUnaryOp<UnaryOp, Derived2> &src) { \
+      vml_assign_impl<Derived1,Derived2,UnaryOp,TRAVERSAL,UNROLLING>::run(dst, src); \
+    } \
+  };
+
+EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,NoUnrolling)
+EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,CompleteUnrolling)
+EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,InnerUnrolling)
+EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearTraversal,NoUnrolling)
+EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearTraversal,CompleteUnrolling)
+EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,NoUnrolling)
+EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,CompleteUnrolling)
+EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,InnerUnrolling)
+EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearVectorizedTraversal,CompleteUnrolling)
+EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearVectorizedTraversal,NoUnrolling)
+EIGEN_MKL_VML_SPECIALIZE_ASSIGN(SliceVectorizedTraversal,NoUnrolling)
+
+
+#if !defined (EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1)
+#define  EIGEN_MKL_VML_MODE VML_HA
+#else
+#define  EIGEN_MKL_VML_MODE VML_LA
+#endif
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE)     \
+  template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > {               \
+    enum { IsSupported = 1 };                                                    \
+    static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& /*func*/,        \
+                            int size, const EIGENTYPE* src, EIGENTYPE* dst) {    \
+      VMLOP(size, (const VMLTYPE*)src, (VMLTYPE*)dst);                           \
+    }                                                                            \
+  };
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE)  \
+  template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > {               \
+    enum { IsSupported = 1 };                                                    \
+    static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& /*func*/,        \
+                            int size, const EIGENTYPE* src, EIGENTYPE* dst) {    \
+      MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE;                                    \
+      VMLOP(size, (const VMLTYPE*)src, (VMLTYPE*)dst, vmlMode);                  \
+    }                                                                            \
+  };
+
+#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE)       \
+  template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > {               \
+    enum { IsSupported = 1 };                                                    \
+    static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& func,        \
+                          int size, const EIGENTYPE* src, EIGENTYPE* dst) {      \
+      EIGENTYPE exponent = func.m_exponent;                                      \
+      MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE;                                    \
+      VMLOP(&size, (const VMLTYPE*)src, (const VMLTYPE*)&exponent,               \
+                        (VMLTYPE*)dst, &vmlMode);                                \
+    }                                                                            \
+  };
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP)                   \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vs##VMLOP, float, float)             \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vd##VMLOP, double, double)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP)                \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vc##VMLOP, scomplex, MKL_Complex8)   \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vz##VMLOP, dcomplex, MKL_Complex16)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP)                        \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP)                         \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP)
+
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP)                \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vms##VMLOP, float, float)         \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmd##VMLOP, double, double)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP)             \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmc##VMLOP, scomplex, MKL_Complex8)  \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmz##VMLOP, dcomplex, MKL_Complex16)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(EIGENOP, VMLOP)                     \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP)                      \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP)
+
+
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sin,  Sin)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(asin, Asin)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(cos,  Cos)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(acos, Acos)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(tan,  Tan)
+//EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs,  Abs)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(exp,  Exp)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(log,  Ln)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sqrt, Sqrt)
+
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr)
+
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmspowx_, float, float)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdpowx_, double, double)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcpowx_, scomplex, MKL_Complex8)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzpowx_, dcomplex, MKL_Complex16)
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_ASSIGN_VML_H
diff --git a/extern/Eigen3/Eigen/src/Core/CMakeLists.txt b/extern/Eigen3/Eigen/src/Core/CMakeLists.txt
new file mode 100644
index 00000000000..2346fc2bbca
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/CMakeLists.txt
@@ -0,0 +1,10 @@
+FILE(GLOB Eigen_Core_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Core_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core COMPONENT Devel
+  )
+
+ADD_SUBDIRECTORY(products)
+ADD_SUBDIRECTORY(util)
+ADD_SUBDIRECTORY(arch)
diff --git a/extern/Eigen3/Eigen/src/Core/GeneralProduct.h b/extern/Eigen3/Eigen/src/Core/GeneralProduct.h
new file mode 100644
index 00000000000..4fbe1f14bbe
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/GeneralProduct.h
@@ -0,0 +1,628 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_GENERAL_PRODUCT_H
+#define EIGEN_GENERAL_PRODUCT_H
+
+namespace Eigen { 
+
+/** \class GeneralProduct
+  * \ingroup Core_Module
+  *
+  * \brief Expression of the product of two general matrices or vectors
+  *
+  * \param LhsNested the type used to store the left-hand side
+  * \param RhsNested the type used to store the right-hand side
+  * \param ProductMode the type of the product
+  *
+  * This class represents an expression of the product of two general matrices.
+  * We call a general matrix, a dense matrix with full storage. For instance,
+  * This excludes triangular, selfadjoint, and sparse matrices.
+  * It is the return type of the operator* between general matrices. Its template
+  * arguments are determined automatically by ProductReturnType. Therefore,
+  * GeneralProduct should never be used direclty. To determine the result type of a
+  * function which involves a matrix product, use ProductReturnType::Type.
+  *
+  * \sa ProductReturnType, MatrixBase::operator*(const MatrixBase<OtherDerived>&)
+  */
+template<typename Lhs, typename Rhs, int ProductType = internal::product_type<Lhs,Rhs>::value>
+class GeneralProduct;
+
+enum {
+  Large = 2,
+  Small = 3
+};
+
+namespace internal {
+
+template<int Rows, int Cols, int Depth> struct product_type_selector;
+
+template<int Size, int MaxSize> struct product_size_category
+{
+  enum { is_large = MaxSize == Dynamic ||
+                    Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD,
+         value = is_large  ? Large
+               : Size == 1 ? 1
+                           : Small
+  };
+};
+
+template<typename Lhs, typename Rhs> struct product_type
+{
+  typedef typename remove_all<Lhs>::type _Lhs;
+  typedef typename remove_all<Rhs>::type _Rhs;
+  enum {
+    MaxRows  = _Lhs::MaxRowsAtCompileTime,
+    Rows  = _Lhs::RowsAtCompileTime,
+    MaxCols  = _Rhs::MaxColsAtCompileTime,
+    Cols  = _Rhs::ColsAtCompileTime,
+    MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::MaxColsAtCompileTime,
+                                           _Rhs::MaxRowsAtCompileTime),
+    Depth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::ColsAtCompileTime,
+                                        _Rhs::RowsAtCompileTime),
+    LargeThreshold = EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+  };
+
+  // the splitting into different lines of code here, introducing the _select enums and the typedef below,
+  // is to work around an internal compiler error with gcc 4.1 and 4.2.
+private:
+  enum {
+    rows_select = product_size_category<Rows,MaxRows>::value,
+    cols_select = product_size_category<Cols,MaxCols>::value,
+    depth_select = product_size_category<Depth,MaxDepth>::value
+  };
+  typedef product_type_selector<rows_select, cols_select, depth_select> selector;
+
+public:
+  enum {
+    value = selector::ret
+  };
+#ifdef EIGEN_DEBUG_PRODUCT
+  static void debug()
+  {
+      EIGEN_DEBUG_VAR(Rows);
+      EIGEN_DEBUG_VAR(Cols);
+      EIGEN_DEBUG_VAR(Depth);
+      EIGEN_DEBUG_VAR(rows_select);
+      EIGEN_DEBUG_VAR(cols_select);
+      EIGEN_DEBUG_VAR(depth_select);
+      EIGEN_DEBUG_VAR(value);
+  }
+#endif
+};
+
+
+/* The following allows to select the kind of product at compile time
+ * based on the three dimensions of the product.
+ * This is a compile time mapping from {1,Small,Large}^3 -> {product types} */
+// FIXME I'm not sure the current mapping is the ideal one.
+template<int M, int N>  struct product_type_selector<M,N,1>              { enum { ret = OuterProduct }; };
+template<int Depth>     struct product_type_selector<1,    1,    Depth>  { enum { ret = InnerProduct }; };
+template<>              struct product_type_selector<1,    1,    1>      { enum { ret = InnerProduct }; };
+template<>              struct product_type_selector<Small,1,    Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<1,    Small,Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Small,Small,Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Small, Small, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
+template<>              struct product_type_selector<Small, Large, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
+template<>              struct product_type_selector<Large, Small, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
+template<>              struct product_type_selector<1,    Large,Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<1,    Large,Large>  { enum { ret = GemvProduct }; };
+template<>              struct product_type_selector<1,    Small,Large>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Large,1,    Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Large,1,    Large>  { enum { ret = GemvProduct }; };
+template<>              struct product_type_selector<Small,1,    Large>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Small,Small,Large>  { enum { ret = GemmProduct }; };
+template<>              struct product_type_selector<Large,Small,Large>  { enum { ret = GemmProduct }; };
+template<>              struct product_type_selector<Small,Large,Large>  { enum { ret = GemmProduct }; };
+template<>              struct product_type_selector<Large,Large,Large>  { enum { ret = GemmProduct }; };
+template<>              struct product_type_selector<Large,Small,Small>  { enum { ret = GemmProduct }; };
+template<>              struct product_type_selector<Small,Large,Small>  { enum { ret = GemmProduct }; };
+template<>              struct product_type_selector<Large,Large,Small>  { enum { ret = GemmProduct }; };
+
+} // end namespace internal
+
+/** \class ProductReturnType
+  * \ingroup Core_Module
+  *
+  * \brief Helper class to get the correct and optimized returned type of operator*
+  *
+  * \param Lhs the type of the left-hand side
+  * \param Rhs the type of the right-hand side
+  * \param ProductMode the type of the product (determined automatically by internal::product_mode)
+  *
+  * This class defines the typename Type representing the optimized product expression
+  * between two matrix expressions. In practice, using ProductReturnType<Lhs,Rhs>::Type
+  * is the recommended way to define the result type of a function returning an expression
+  * which involve a matrix product. The class Product should never be
+  * used directly.
+  *
+  * \sa class Product, MatrixBase::operator*(const MatrixBase<OtherDerived>&)
+  */
+template<typename Lhs, typename Rhs, int ProductType>
+struct ProductReturnType
+{
+  // TODO use the nested type to reduce instanciations ????
+//   typedef typename internal::nested<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
+//   typedef typename internal::nested<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
+
+  typedef GeneralProduct<Lhs/*Nested*/, Rhs/*Nested*/, ProductType> Type;
+};
+
+template<typename Lhs, typename Rhs>
+struct ProductReturnType<Lhs,Rhs,CoeffBasedProductMode>
+{
+  typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested;
+  typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested;
+  typedef CoeffBasedProduct<LhsNested, RhsNested, EvalBeforeAssigningBit | EvalBeforeNestingBit> Type;
+};
+
+template<typename Lhs, typename Rhs>
+struct ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode>
+{
+  typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested;
+  typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested;
+  typedef CoeffBasedProduct<LhsNested, RhsNested, NestByRefBit> Type;
+};
+
+// this is a workaround for sun CC
+template<typename Lhs, typename Rhs>
+struct LazyProductReturnType : public ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode>
+{};
+
+/***********************************************************************
+*  Implementation of Inner Vector Vector Product
+***********************************************************************/
+
+// FIXME : maybe the "inner product" could return a Scalar
+// instead of a 1x1 matrix ??
+// Pro: more natural for the user
+// Cons: this could be a problem if in a meta unrolled algorithm a matrix-matrix
+// product ends up to a row-vector times col-vector product... To tackle this use
+// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x);
+
+namespace internal {
+
+template<typename Lhs, typename Rhs>
+struct traits<GeneralProduct<Lhs,Rhs,InnerProduct> >
+ : traits<Matrix<typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> >
+{};
+
+}
+
+template<typename Lhs, typename Rhs>
+class GeneralProduct<Lhs, Rhs, InnerProduct>
+  : internal::no_assignment_operator,
+    public Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1>
+{
+    typedef Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> Base;
+  public:
+    GeneralProduct(const Lhs& lhs, const Rhs& rhs)
+    {
+      EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+      Base::coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum();
+    }
+
+    /** Convertion to scalar */
+    operator const typename Base::Scalar() const {
+      return Base::coeff(0,0);
+    }
+};
+
+/***********************************************************************
+*  Implementation of Outer Vector Vector Product
+***********************************************************************/
+
+namespace internal {
+template<int StorageOrder> struct outer_product_selector;
+
+template<typename Lhs, typename Rhs>
+struct traits<GeneralProduct<Lhs,Rhs,OuterProduct> >
+ : traits<ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs> >
+{};
+
+}
+
+template<typename Lhs, typename Rhs>
+class GeneralProduct<Lhs, Rhs, OuterProduct>
+  : public ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs>
+{
+  public:
+    EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
+
+    GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
+    {
+      EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+    }
+
+    template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
+    {
+      internal::outer_product_selector<(int(Dest::Flags)&RowMajorBit) ? RowMajor : ColMajor>::run(*this, dest, alpha);
+    }
+};
+
+namespace internal {
+
+template<> struct outer_product_selector<ColMajor> {
+  template<typename ProductType, typename Dest>
+  static EIGEN_DONT_INLINE void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha) {
+    typedef typename Dest::Index Index;
+    // FIXME make sure lhs is sequentially stored
+    // FIXME not very good if rhs is real and lhs complex while alpha is real too
+    const Index cols = dest.cols();
+    for (Index j=0; j<cols; ++j)
+      dest.col(j) += (alpha * prod.rhs().coeff(j)) * prod.lhs();
+  }
+};
+
+template<> struct outer_product_selector<RowMajor> {
+  template<typename ProductType, typename Dest>
+  static EIGEN_DONT_INLINE void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha) {
+    typedef typename Dest::Index Index;
+    // FIXME make sure rhs is sequentially stored
+    // FIXME not very good if lhs is real and rhs complex while alpha is real too
+    const Index rows = dest.rows();
+    for (Index i=0; i<rows; ++i)
+      dest.row(i) += (alpha * prod.lhs().coeff(i)) * prod.rhs();
+  }
+};
+
+} // end namespace internal
+
+/***********************************************************************
+*  Implementation of General Matrix Vector Product
+***********************************************************************/
+
+/*  According to the shape/flags of the matrix we have to distinghish 3 different cases:
+ *   1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine
+ *   2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine
+ *   3 - all other cases are handled using a simple loop along the outer-storage direction.
+ *  Therefore we need a lower level meta selector.
+ *  Furthermore, if the matrix is the rhs, then the product has to be transposed.
+ */
+namespace internal {
+
+template<typename Lhs, typename Rhs>
+struct traits<GeneralProduct<Lhs,Rhs,GemvProduct> >
+ : traits<ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs> >
+{};
+
+template<int Side, int StorageOrder, bool BlasCompatible>
+struct gemv_selector;
+
+} // end namespace internal
+
+template<typename Lhs, typename Rhs>
+class GeneralProduct<Lhs, Rhs, GemvProduct>
+  : public ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs>
+{
+  public:
+    EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct)
+
+    typedef typename Lhs::Scalar LhsScalar;
+    typedef typename Rhs::Scalar RhsScalar;
+
+    GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
+    {
+//       EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::Scalar, typename Rhs::Scalar>::value),
+//         YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+    }
+
+    enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
+    typedef typename internal::conditional<int(Side)==OnTheRight,_LhsNested,_RhsNested>::type MatrixType;
+
+    template<typename Dest> void scaleAndAddTo(Dest& dst, Scalar alpha) const
+    {
+      eigen_assert(m_lhs.rows() == dst.rows() && m_rhs.cols() == dst.cols());
+      internal::gemv_selector<Side,(int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
+                       bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)>::run(*this, dst, alpha);
+    }
+};
+
+namespace internal {
+
+// The vector is on the left => transposition
+template<int StorageOrder, bool BlasCompatible>
+struct gemv_selector<OnTheLeft,StorageOrder,BlasCompatible>
+{
+  template<typename ProductType, typename Dest>
+  static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
+  {
+    Transpose<Dest> destT(dest);
+    enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
+    gemv_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
+      ::run(GeneralProduct<Transpose<const typename ProductType::_RhsNested>,Transpose<const typename ProductType::_LhsNested>, GemvProduct>
+        (prod.rhs().transpose(), prod.lhs().transpose()), destT, alpha);
+  }
+};
+
+template<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if;
+
+template<typename Scalar,int Size,int MaxSize>
+struct gemv_static_vector_if<Scalar,Size,MaxSize,false>
+{
+  EIGEN_STRONG_INLINE  Scalar* data() { eigen_internal_assert(false && "should never be called"); return 0; }
+};
+
+template<typename Scalar,int Size>
+struct gemv_static_vector_if<Scalar,Size,Dynamic,true>
+{
+  EIGEN_STRONG_INLINE Scalar* data() { return 0; }
+};
+
+template<typename Scalar,int Size,int MaxSize>
+struct gemv_static_vector_if<Scalar,Size,MaxSize,true>
+{
+  #if EIGEN_ALIGN_STATICALLY
+  internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0> m_data;
+  EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
+  #else
+  // Some architectures cannot align on the stack,
+  // => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
+  enum {
+    ForceAlignment  = internal::packet_traits<Scalar>::Vectorizable,
+    PacketSize      = internal::packet_traits<Scalar>::size
+  };
+  internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?PacketSize:0),0> m_data;
+  EIGEN_STRONG_INLINE Scalar* data() {
+    return ForceAlignment
+            ? reinterpret_cast<Scalar*>((reinterpret_cast<size_t>(m_data.array) & ~(size_t(15))) + 16)
+            : m_data.array;
+  }
+  #endif
+};
+
+template<> struct gemv_selector<OnTheRight,ColMajor,true>
+{
+  template<typename ProductType, typename Dest>
+  static inline void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
+  {
+    typedef typename ProductType::Index Index;
+    typedef typename ProductType::LhsScalar   LhsScalar;
+    typedef typename ProductType::RhsScalar   RhsScalar;
+    typedef typename ProductType::Scalar      ResScalar;
+    typedef typename ProductType::RealScalar  RealScalar;
+    typedef typename ProductType::ActualLhsType ActualLhsType;
+    typedef typename ProductType::ActualRhsType ActualRhsType;
+    typedef typename ProductType::LhsBlasTraits LhsBlasTraits;
+    typedef typename ProductType::RhsBlasTraits RhsBlasTraits;
+    typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest;
+
+    ActualLhsType actualLhs = LhsBlasTraits::extract(prod.lhs());
+    ActualRhsType actualRhs = RhsBlasTraits::extract(prod.rhs());
+
+    ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs())
+                                  * RhsBlasTraits::extractScalarFactor(prod.rhs());
+
+    enum {
+      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
+      // on, the other hand it is good for the cache to pack the vector anyways...
+      EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime==1,
+      ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
+      MightCannotUseDest = (Dest::InnerStrideAtCompileTime!=1) || ComplexByReal
+    };
+
+    gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
+
+    bool alphaIsCompatible = (!ComplexByReal) || (imag(actualAlpha)==RealScalar(0));
+    bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
+    
+    RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
+
+    ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
+                                                  evalToDest ? dest.data() : static_dest.data());
+    
+    if(!evalToDest)
+    {
+      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      int size = dest.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      #endif
+      if(!alphaIsCompatible)
+      {
+        MappedDest(actualDestPtr, dest.size()).setZero();
+        compatibleAlpha = RhsScalar(1);
+      }
+      else
+        MappedDest(actualDestPtr, dest.size()) = dest;
+    }
+
+    general_matrix_vector_product
+      <Index,LhsScalar,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsBlasTraits::NeedToConjugate>::run(
+        actualLhs.rows(), actualLhs.cols(),
+        actualLhs.data(), actualLhs.outerStride(),
+        actualRhs.data(), actualRhs.innerStride(),
+        actualDestPtr, 1,
+        compatibleAlpha);
+
+    if (!evalToDest)
+    {
+      if(!alphaIsCompatible)
+        dest += actualAlpha * MappedDest(actualDestPtr, dest.size());
+      else
+        dest = MappedDest(actualDestPtr, dest.size());
+    }
+  }
+};
+
+template<> struct gemv_selector<OnTheRight,RowMajor,true>
+{
+  template<typename ProductType, typename Dest>
+  static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
+  {
+    typedef typename ProductType::LhsScalar LhsScalar;
+    typedef typename ProductType::RhsScalar RhsScalar;
+    typedef typename ProductType::Scalar    ResScalar;
+    typedef typename ProductType::Index Index;
+    typedef typename ProductType::ActualLhsType ActualLhsType;
+    typedef typename ProductType::ActualRhsType ActualRhsType;
+    typedef typename ProductType::_ActualRhsType _ActualRhsType;
+    typedef typename ProductType::LhsBlasTraits LhsBlasTraits;
+    typedef typename ProductType::RhsBlasTraits RhsBlasTraits;
+
+    typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(prod.lhs());
+    typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(prod.rhs());
+
+    ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs())
+                                  * RhsBlasTraits::extractScalarFactor(prod.rhs());
+
+    enum {
+      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
+      // on, the other hand it is good for the cache to pack the vector anyways...
+      DirectlyUseRhs = _ActualRhsType::InnerStrideAtCompileTime==1
+    };
+
+    gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
+
+    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(),
+        DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
+
+    if(!DirectlyUseRhs)
+    {
+      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      int size = actualRhs.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      #endif
+      Map<typename _ActualRhsType::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
+    }
+
+    general_matrix_vector_product
+      <Index,LhsScalar,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsBlasTraits::NeedToConjugate>::run(
+        actualLhs.rows(), actualLhs.cols(),
+        actualLhs.data(), actualLhs.outerStride(),
+        actualRhsPtr, 1,
+        dest.data(), dest.innerStride(),
+        actualAlpha);
+  }
+};
+
+template<> struct gemv_selector<OnTheRight,ColMajor,false>
+{
+  template<typename ProductType, typename Dest>
+  static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
+  {
+    typedef typename Dest::Index Index;
+    // TODO makes sure dest is sequentially stored in memory, otherwise use a temp
+    const Index size = prod.rhs().rows();
+    for(Index k=0; k<size; ++k)
+      dest += (alpha*prod.rhs().coeff(k)) * prod.lhs().col(k);
+  }
+};
+
+template<> struct gemv_selector<OnTheRight,RowMajor,false>
+{
+  template<typename ProductType, typename Dest>
+  static void run(const ProductType& prod, Dest& dest, typename ProductType::Scalar alpha)
+  {
+    typedef typename Dest::Index Index;
+    // TODO makes sure rhs is sequentially stored in memory, otherwise use a temp
+    const Index rows = prod.rows();
+    for(Index i=0; i<rows; ++i)
+      dest.coeffRef(i) += alpha * (prod.lhs().row(i).cwiseProduct(prod.rhs().transpose())).sum();
+  }
+};
+
+} // end namespace internal
+
+/***************************************************************************
+* Implementation of matrix base methods
+***************************************************************************/
+
+/** \returns the matrix product of \c *this and \a other.
+  *
+  * \note If instead of the matrix product you want the coefficient-wise product, see Cwise::operator*().
+  *
+  * \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*()
+  */
+template<typename Derived>
+template<typename OtherDerived>
+inline const typename ProductReturnType<Derived, OtherDerived>::Type
+MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
+{
+  // A note regarding the function declaration: In MSVC, this function will sometimes
+  // not be inlined since DenseStorage is an unwindable object for dynamic
+  // matrices and product types are holding a member to store the result.
+  // Thus it does not help tagging this function with EIGEN_STRONG_INLINE.
+  enum {
+    ProductIsValid =  Derived::ColsAtCompileTime==Dynamic
+                   || OtherDerived::RowsAtCompileTime==Dynamic
+                   || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
+    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
+    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
+  };
+  // note to the lost user:
+  //    * for a dot product use: v1.dot(v2)
+  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
+    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
+    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
+  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
+#ifdef EIGEN_DEBUG_PRODUCT
+  internal::product_type<Derived,OtherDerived>::debug();
+#endif
+  return typename ProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
+}
+
+/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
+  *
+  * The returned product will behave like any other expressions: the coefficients of the product will be
+  * computed once at a time as requested. This might be useful in some extremely rare cases when only
+  * a small and no coherent fraction of the result's coefficients have to be computed.
+  *
+  * \warning This version of the matrix product can be much much slower. So use it only if you know
+  * what you are doing and that you measured a true speed improvement.
+  *
+  * \sa operator*(const MatrixBase&)
+  */
+template<typename Derived>
+template<typename OtherDerived>
+const typename LazyProductReturnType<Derived,OtherDerived>::Type
+MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived> &other) const
+{
+  enum {
+    ProductIsValid =  Derived::ColsAtCompileTime==Dynamic
+                   || OtherDerived::RowsAtCompileTime==Dynamic
+                   || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
+    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
+    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
+  };
+  // note to the lost user:
+  //    * for a dot product use: v1.dot(v2)
+  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
+    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
+    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
+  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
+
+  return typename LazyProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_PRODUCT_H
diff --git a/extern/Eigen3/Eigen/src/Core/PlainObjectBase.h b/extern/Eigen3/Eigen/src/Core/PlainObjectBase.h
index baab5653619..f9c43273232 100644
--- a/extern/Eigen3/Eigen/src/Core/PlainObjectBase.h
+++ b/extern/Eigen3/Eigen/src/Core/PlainObjectBase.h
@@ -49,7 +49,7 @@ EIGEN_ALWAYS_INLINE void check_rows_cols_for_overflow(Index rows, Index cols)
     throw_std_bad_alloc();
 }
 
-template <typename Derived, typename OtherDerived = Derived, bool IsVector = static_cast<bool>(Derived::IsVectorAtCompileTime)> struct conservative_resize_like_impl;
+template <typename Derived, typename OtherDerived = Derived, bool IsVector = bool(Derived::IsVectorAtCompileTime)> struct conservative_resize_like_impl;
 
 template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct matrix_swap_impl;
 
diff --git a/extern/Eigen3/Eigen/src/Core/arch/AltiVec/CMakeLists.txt b/extern/Eigen3/Eigen/src/Core/arch/AltiVec/CMakeLists.txt
new file mode 100644
index 00000000000..9f8d2e9c467
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/arch/AltiVec/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Core_arch_AltiVec_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Core_arch_AltiVec_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/AltiVec COMPONENT Devel
+)
diff --git a/extern/Eigen3/Eigen/src/Core/arch/CMakeLists.txt b/extern/Eigen3/Eigen/src/Core/arch/CMakeLists.txt
new file mode 100644
index 00000000000..8456dec1581
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/arch/CMakeLists.txt
@@ -0,0 +1,4 @@
+ADD_SUBDIRECTORY(SSE)
+ADD_SUBDIRECTORY(AltiVec)
+ADD_SUBDIRECTORY(NEON)
+ADD_SUBDIRECTORY(Default)
diff --git a/extern/Eigen3/Eigen/src/Core/arch/Default/CMakeLists.txt b/extern/Eigen3/Eigen/src/Core/arch/Default/CMakeLists.txt
new file mode 100644
index 00000000000..339c091d156
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/arch/Default/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Core_arch_Default_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Core_arch_Default_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/Default COMPONENT Devel
+)
diff --git a/extern/Eigen3/Eigen/src/Core/arch/NEON/CMakeLists.txt b/extern/Eigen3/Eigen/src/Core/arch/NEON/CMakeLists.txt
new file mode 100644
index 00000000000..fd4d4af50cb
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/arch/NEON/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Core_arch_NEON_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Core_arch_NEON_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/NEON COMPONENT Devel
+)
diff --git a/extern/Eigen3/Eigen/src/Core/arch/NEON/PacketMath.h b/extern/Eigen3/Eigen/src/Core/arch/NEON/PacketMath.h
index 7868539ba36..1eb082a5b06 100644
--- a/extern/Eigen3/Eigen/src/Core/arch/NEON/PacketMath.h
+++ b/extern/Eigen3/Eigen/src/Core/arch/NEON/PacketMath.h
@@ -160,7 +160,8 @@ template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, co
 }
 
 // for some weird raisons, it has to be overloaded for packet of integers
-template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return padd(pmul(a,b), c); }
+template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vmlaq_f32(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return vmlaq_s32(c,a,b); }
 
 template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) { return vminq_f32(a,b); }
 template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vminq_s32(a,b); }
diff --git a/extern/Eigen3/Eigen/src/Core/arch/SSE/CMakeLists.txt b/extern/Eigen3/Eigen/src/Core/arch/SSE/CMakeLists.txt
new file mode 100644
index 00000000000..46ea7cc623c
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/arch/SSE/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Core_arch_SSE_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Core_arch_SSE_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/arch/SSE COMPONENT Devel
+)
diff --git a/extern/Eigen3/Eigen/src/Core/products/CMakeLists.txt b/extern/Eigen3/Eigen/src/Core/products/CMakeLists.txt
new file mode 100644
index 00000000000..21fc94ae38a
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/products/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Core_Product_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Core_Product_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/products COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_MKL.h b/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_MKL.h
new file mode 100644
index 00000000000..3deed068e39
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_MKL.h
@@ -0,0 +1,146 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   Level 3 BLAS SYRK/HERK implementation.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_MKL_H
+#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_MKL_H
+
+namespace Eigen { 
+
+namespace internal {
+
+template <typename Index, typename Scalar, int AStorageOrder, bool ConjugateA, int ResStorageOrder, int  UpLo>
+struct general_matrix_matrix_rankupdate :
+       general_matrix_matrix_triangular_product<
+         Index,Scalar,AStorageOrder,ConjugateA,Scalar,AStorageOrder,ConjugateA,ResStorageOrder,UpLo,BuiltIn> {};
+
+
+// try to go to BLAS specialization
+#define EIGEN_MKL_RANKUPDATE_SPECIALIZE(Scalar) \
+template <typename Index, int LhsStorageOrder, bool ConjugateLhs, \
+                          int RhsStorageOrder, bool ConjugateRhs, int  UpLo> \
+struct general_matrix_matrix_triangular_product<Index,Scalar,LhsStorageOrder,ConjugateLhs, \
+               Scalar,RhsStorageOrder,ConjugateRhs,ColMajor,UpLo,Specialized> { \
+  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const Scalar* lhs, Index lhsStride, \
+                          const Scalar* rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) \
+  { \
+    if (lhs==rhs) { \
+      general_matrix_matrix_rankupdate<Index,Scalar,LhsStorageOrder,ConjugateLhs,ColMajor,UpLo> \
+      ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resStride,alpha); \
+    } else { \
+      general_matrix_matrix_triangular_product<Index, \
+        Scalar, LhsStorageOrder, ConjugateLhs, \
+        Scalar, RhsStorageOrder, ConjugateRhs, \
+        ColMajor, UpLo, BuiltIn> \
+      ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resStride,alpha); \
+    } \
+  } \
+};
+
+EIGEN_MKL_RANKUPDATE_SPECIALIZE(double)
+//EIGEN_MKL_RANKUPDATE_SPECIALIZE(dcomplex)
+EIGEN_MKL_RANKUPDATE_SPECIALIZE(float)
+//EIGEN_MKL_RANKUPDATE_SPECIALIZE(scomplex)
+
+// SYRK for float/double
+#define EIGEN_MKL_RANKUPDATE_R(EIGTYPE, MKLTYPE, MKLFUNC) \
+template <typename Index, int AStorageOrder, bool ConjugateA, int  UpLo> \
+struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,ColMajor,UpLo> { \
+  enum { \
+    IsLower = (UpLo&Lower) == Lower, \
+    LowUp = IsLower ? Lower : Upper, \
+    conjA = ((AStorageOrder==ColMajor) && ConjugateA) ? 1 : 0 \
+  }; \
+  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \
+                          const EIGTYPE* rhs, Index rhsStride, EIGTYPE* res, Index resStride, EIGTYPE alpha) \
+  { \
+  /* typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs;*/ \
+\
+   MKL_INT lda=lhsStride, ldc=resStride, n=size, k=depth; \
+   char uplo=(IsLower) ? 'L' : 'U', trans=(AStorageOrder==RowMajor) ? 'T':'N'; \
+   MKLTYPE alpha_, beta_; \
+\
+/* Set alpha_ & beta_ */ \
+   assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \
+   assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(beta_, EIGTYPE(1)); \
+   MKLFUNC(&uplo, &trans, &n, &k, &alpha_, lhs, &lda, &beta_, res, &ldc); \
+  } \
+};
+
+// HERK for complex data
+#define EIGEN_MKL_RANKUPDATE_C(EIGTYPE, MKLTYPE, RTYPE, MKLFUNC) \
+template <typename Index, int AStorageOrder, bool ConjugateA, int  UpLo> \
+struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,ColMajor,UpLo> { \
+  enum { \
+    IsLower = (UpLo&Lower) == Lower, \
+    LowUp = IsLower ? Lower : Upper, \
+    conjA = (((AStorageOrder==ColMajor) && ConjugateA) || ((AStorageOrder==RowMajor) && !ConjugateA)) ? 1 : 0 \
+  }; \
+  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \
+                          const EIGTYPE* rhs, Index rhsStride, EIGTYPE* res, Index resStride, EIGTYPE alpha) \
+  { \
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, AStorageOrder> MatrixType; \
+\
+   MKL_INT lda=lhsStride, ldc=resStride, n=size, k=depth; \
+   char uplo=(IsLower) ? 'L' : 'U', trans=(AStorageOrder==RowMajor) ? 'C':'N'; \
+   RTYPE alpha_, beta_; \
+   const EIGTYPE* a_ptr; \
+\
+/* Set alpha_ & beta_ */ \
+/*   assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); */\
+/*   assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(beta_, EIGTYPE(1));*/ \
+   alpha_ = alpha.real(); \
+   beta_ = 1.0; \
+/* Copy with conjugation in some cases*/ \
+   MatrixType a; \
+   if (conjA) { \
+     Map<const MatrixType, 0, OuterStride<> > mapA(lhs,n,k,OuterStride<>(lhsStride)); \
+     a = mapA.conjugate(); \
+     lda = a.outerStride(); \
+     a_ptr = a.data(); \
+   } else a_ptr=lhs; \
+   MKLFUNC(&uplo, &trans, &n, &k, &alpha_, (MKLTYPE*)a_ptr, &lda, &beta_, (MKLTYPE*)res, &ldc); \
+  } \
+};
+
+
+EIGEN_MKL_RANKUPDATE_R(double, double, dsyrk)
+EIGEN_MKL_RANKUPDATE_R(float,  float,  ssyrk)
+
+//EIGEN_MKL_RANKUPDATE_C(dcomplex, MKL_Complex16, double, zherk)
+//EIGEN_MKL_RANKUPDATE_C(scomplex, MKL_Complex8,  double, cherk)
+
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_MKL_H
diff --git a/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_MKL.h b/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_MKL.h
new file mode 100644
index 00000000000..060af328ebe
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_MKL.h
@@ -0,0 +1,118 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   General matrix-matrix product functionality based on ?GEMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_GENERAL_MATRIX_MATRIX_MKL_H
+#define EIGEN_GENERAL_MATRIX_MATRIX_MKL_H
+
+namespace Eigen { 
+
+namespace internal {
+
+/**********************************************************************
+* This file implements general matrix-matrix multiplication using BLAS
+* gemm function via partial specialization of
+* general_matrix_matrix_product::run(..) method for float, double,
+* std::complex<float> and std::complex<double> types
+**********************************************************************/
+
+// gemm specialization
+
+#define GEMM_SPECIALIZATION(EIGTYPE, EIGPREFIX, MKLTYPE, MKLPREFIX) \
+template< \
+  typename Index, \
+  int LhsStorageOrder, bool ConjugateLhs, \
+  int RhsStorageOrder, bool ConjugateRhs> \
+struct general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor> \
+{ \
+static void run(Index rows, Index cols, Index depth, \
+  const EIGTYPE* _lhs, Index lhsStride, \
+  const EIGTYPE* _rhs, Index rhsStride, \
+  EIGTYPE* res, Index resStride, \
+  EIGTYPE alpha, \
+  level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/, \
+  GemmParallelInfo<Index>* /*info = 0*/) \
+{ \
+  using std::conj; \
+\
+  char transa, transb; \
+  MKL_INT m, n, k, lda, ldb, ldc; \
+  const EIGTYPE *a, *b; \
+  MKLTYPE alpha_, beta_; \
+  MatrixX##EIGPREFIX a_tmp, b_tmp; \
+  EIGTYPE myone(1);\
+\
+/* Set transpose options */ \
+  transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \
+  transb = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \
+\
+/* Set m, n, k */ \
+  m = (MKL_INT)rows;  \
+  n = (MKL_INT)cols;  \
+  k = (MKL_INT)depth; \
+\
+/* Set alpha_ & beta_ */ \
+  assign_scalar_eig2mkl(alpha_, alpha); \
+  assign_scalar_eig2mkl(beta_, myone); \
+\
+/* Set lda, ldb, ldc */ \
+  lda = (MKL_INT)lhsStride; \
+  ldb = (MKL_INT)rhsStride; \
+  ldc = (MKL_INT)resStride; \
+\
+/* Set a, b, c */ \
+  if ((LhsStorageOrder==ColMajor) && (ConjugateLhs)) { \
+    Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,m,k,OuterStride<>(lhsStride)); \
+    a_tmp = lhs.conjugate(); \
+    a = a_tmp.data(); \
+    lda = a_tmp.outerStride(); \
+  } else a = _lhs; \
+\
+  if ((RhsStorageOrder==ColMajor) && (ConjugateRhs)) { \
+    Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,k,n,OuterStride<>(rhsStride)); \
+    b_tmp = rhs.conjugate(); \
+    b = b_tmp.data(); \
+    ldb = b_tmp.outerStride(); \
+  } else b = _rhs; \
+\
+  MKLPREFIX##gemm(&transa, &transb, &m, &n, &k, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \
+}};
+
+GEMM_SPECIALIZATION(double,   d,  double,        d)
+GEMM_SPECIALIZATION(float,    f,  float,         s)
+GEMM_SPECIALIZATION(dcomplex, cd, MKL_Complex16, z)
+GEMM_SPECIALIZATION(scomplex, cf, MKL_Complex8,  c)
+
+} // end namespase internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERAL_MATRIX_MATRIX_MKL_H
diff --git a/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixVector_MKL.h b/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixVector_MKL.h
new file mode 100644
index 00000000000..e9de6af3ed1
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixVector_MKL.h
@@ -0,0 +1,131 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   General matrix-vector product functionality based on ?GEMV.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_GENERAL_MATRIX_VECTOR_MKL_H
+#define EIGEN_GENERAL_MATRIX_VECTOR_MKL_H
+
+namespace Eigen { 
+
+namespace internal {
+
+/**********************************************************************
+* This file implements general matrix-vector multiplication using BLAS
+* gemv function via partial specialization of
+* general_matrix_vector_product::run(..) method for float, double,
+* std::complex<float> and std::complex<double> types
+**********************************************************************/
+
+// gemv specialization
+
+template<typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar, bool ConjugateRhs>
+struct general_matrix_vector_product_gemv :
+  general_matrix_vector_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,ConjugateRhs,BuiltIn> {};
+
+#define EIGEN_MKL_GEMV_SPECIALIZE(Scalar) \
+template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \
+struct general_matrix_vector_product<Index,Scalar,ColMajor,ConjugateLhs,Scalar,ConjugateRhs,Specialized> { \
+static EIGEN_DONT_INLINE void run( \
+  Index rows, Index cols, \
+  const Scalar* lhs, Index lhsStride, \
+  const Scalar* rhs, Index rhsIncr, \
+  Scalar* res, Index resIncr, Scalar alpha) \
+{ \
+  if (ConjugateLhs) { \
+    general_matrix_vector_product<Index,Scalar,ColMajor,ConjugateLhs,Scalar,ConjugateRhs,BuiltIn>::run( \
+      rows, cols, lhs, lhsStride, rhs, rhsIncr, res, resIncr, alpha); \
+  } else { \
+    general_matrix_vector_product_gemv<Index,Scalar,ColMajor,ConjugateLhs,Scalar,ConjugateRhs>::run( \
+      rows, cols, lhs, lhsStride, rhs, rhsIncr, res, resIncr, alpha); \
+  } \
+} \
+}; \
+template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \
+struct general_matrix_vector_product<Index,Scalar,RowMajor,ConjugateLhs,Scalar,ConjugateRhs,Specialized> { \
+static EIGEN_DONT_INLINE void run( \
+  Index rows, Index cols, \
+  const Scalar* lhs, Index lhsStride, \
+  const Scalar* rhs, Index rhsIncr, \
+  Scalar* res, Index resIncr, Scalar alpha) \
+{ \
+    general_matrix_vector_product_gemv<Index,Scalar,RowMajor,ConjugateLhs,Scalar,ConjugateRhs>::run( \
+      rows, cols, lhs, lhsStride, rhs, rhsIncr, res, resIncr, alpha); \
+} \
+}; \
+
+EIGEN_MKL_GEMV_SPECIALIZE(double)
+EIGEN_MKL_GEMV_SPECIALIZE(float)
+EIGEN_MKL_GEMV_SPECIALIZE(dcomplex)
+EIGEN_MKL_GEMV_SPECIALIZE(scomplex)
+
+#define EIGEN_MKL_GEMV_SPECIALIZATION(EIGTYPE,MKLTYPE,MKLPREFIX) \
+template<typename Index, int LhsStorageOrder, bool ConjugateLhs, bool ConjugateRhs> \
+struct general_matrix_vector_product_gemv<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,ConjugateRhs> \
+{ \
+typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> GEMVVector;\
+\
+static EIGEN_DONT_INLINE void run( \
+  Index rows, Index cols, \
+  const EIGTYPE* lhs, Index lhsStride, \
+  const EIGTYPE* rhs, Index rhsIncr, \
+  EIGTYPE* res, Index resIncr, EIGTYPE alpha) \
+{ \
+  MKL_INT m=rows, n=cols, lda=lhsStride, incx=rhsIncr, incy=resIncr; \
+  MKLTYPE alpha_, beta_; \
+  const EIGTYPE *x_ptr, myone(1); \
+  char trans=(LhsStorageOrder==ColMajor) ? 'N' : (ConjugateLhs) ? 'C' : 'T'; \
+  if (LhsStorageOrder==RowMajor) { \
+    m=cols; \
+    n=rows; \
+  }\
+  assign_scalar_eig2mkl(alpha_, alpha); \
+  assign_scalar_eig2mkl(beta_, myone); \
+  GEMVVector x_tmp; \
+  if (ConjugateRhs) { \
+    Map<const GEMVVector, 0, InnerStride<> > map_x(rhs,cols,1,InnerStride<>(incx)); \
+    x_tmp=map_x.conjugate(); \
+    x_ptr=x_tmp.data(); \
+    incx=1; \
+  } else x_ptr=rhs; \
+  MKLPREFIX##gemv(&trans, &m, &n, &alpha_, (const MKLTYPE*)lhs, &lda, (const MKLTYPE*)x_ptr, &incx, &beta_, (MKLTYPE*)res, &incy); \
+}\
+};
+
+EIGEN_MKL_GEMV_SPECIALIZATION(double,   double,        d)
+EIGEN_MKL_GEMV_SPECIALIZATION(float,    float,         s)
+EIGEN_MKL_GEMV_SPECIALIZATION(dcomplex, MKL_Complex16, z)
+EIGEN_MKL_GEMV_SPECIALIZATION(scomplex, MKL_Complex8,  c)
+
+} // end namespase internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERAL_MATRIX_VECTOR_MKL_H
diff --git a/extern/Eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_MKL.h b/extern/Eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_MKL.h
new file mode 100644
index 00000000000..4e5c4125c01
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_MKL.h
@@ -0,0 +1,295 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   Self adjoint matrix * matrix product functionality based on ?SYMM/?HEMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_MKL_H
+#define EIGEN_SELFADJOINT_MATRIX_MATRIX_MKL_H
+
+namespace Eigen { 
+
+namespace internal {
+
+
+/* Optimized selfadjoint matrix * matrix (?SYMM/?HEMM) product */
+
+#define EIGEN_MKL_SYMM_L(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \
+template <typename Index, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor> \
+{\
+\
+  static EIGEN_DONT_INLINE void run( \
+    Index rows, Index cols, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resStride, \
+    EIGTYPE alpha) \
+  { \
+    char side='L', uplo='L'; \
+    MKL_INT m, n, lda, ldb, ldc; \
+    const EIGTYPE *a, *b; \
+    MKLTYPE alpha_, beta_; \
+    MatrixX##EIGPREFIX b_tmp; \
+    EIGTYPE myone(1);\
+\
+/* Set transpose options */ \
+/* Set m, n, k */ \
+    m = (MKL_INT)rows;  \
+    n = (MKL_INT)cols;  \
+\
+/* Set alpha_ & beta_ */ \
+    assign_scalar_eig2mkl(alpha_, alpha); \
+    assign_scalar_eig2mkl(beta_, myone); \
+\
+/* Set lda, ldb, ldc */ \
+    lda = (MKL_INT)lhsStride; \
+    ldb = (MKL_INT)rhsStride; \
+    ldc = (MKL_INT)resStride; \
+\
+/* Set a, b, c */ \
+    if (LhsStorageOrder==RowMajor) uplo='U'; \
+    a = _lhs; \
+\
+    if (RhsStorageOrder==RowMajor) { \
+      Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
+      b_tmp = rhs.adjoint(); \
+      b = b_tmp.data(); \
+      ldb = b_tmp.outerStride(); \
+    } else b = _rhs; \
+\
+    MKLPREFIX##symm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \
+\
+  } \
+};
+
+
+#define EIGEN_MKL_HEMM_L(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \
+template <typename Index, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor> \
+{\
+  static EIGEN_DONT_INLINE void run( \
+    Index rows, Index cols, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resStride, \
+    EIGTYPE alpha) \
+  { \
+    char side='L', uplo='L'; \
+    MKL_INT m, n, lda, ldb, ldc; \
+    const EIGTYPE *a, *b; \
+    MKLTYPE alpha_, beta_; \
+    MatrixX##EIGPREFIX b_tmp; \
+    Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> a_tmp; \
+    EIGTYPE myone(1); \
+\
+/* Set transpose options */ \
+/* Set m, n, k */ \
+    m = (MKL_INT)rows; \
+    n = (MKL_INT)cols; \
+\
+/* Set alpha_ & beta_ */ \
+    assign_scalar_eig2mkl(alpha_, alpha); \
+    assign_scalar_eig2mkl(beta_, myone); \
+\
+/* Set lda, ldb, ldc */ \
+    lda = (MKL_INT)lhsStride; \
+    ldb = (MKL_INT)rhsStride; \
+    ldc = (MKL_INT)resStride; \
+\
+/* Set a, b, c */ \
+    if (((LhsStorageOrder==ColMajor) && ConjugateLhs) || ((LhsStorageOrder==RowMajor) && (!ConjugateLhs))) { \
+      Map<const Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder>, 0, OuterStride<> > lhs(_lhs,m,m,OuterStride<>(lhsStride)); \
+      a_tmp = lhs.conjugate(); \
+      a = a_tmp.data(); \
+      lda = a_tmp.outerStride(); \
+    } else a = _lhs; \
+    if (LhsStorageOrder==RowMajor) uplo='U'; \
+\
+    if (RhsStorageOrder==ColMajor && (!ConjugateRhs)) { \
+       b = _rhs; } \
+    else { \
+      if (RhsStorageOrder==ColMajor && ConjugateRhs) { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,m,n,OuterStride<>(rhsStride)); \
+        b_tmp = rhs.conjugate(); \
+      } else \
+      if (ConjugateRhs) { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
+        b_tmp = rhs.adjoint(); \
+      } else { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
+        b_tmp = rhs.transpose(); \
+      } \
+      b = b_tmp.data(); \
+      ldb = b_tmp.outerStride(); \
+    } \
+\
+    MKLPREFIX##hemm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \
+\
+  } \
+};
+
+EIGEN_MKL_SYMM_L(double, double, d, d)
+EIGEN_MKL_SYMM_L(float, float, f, s)
+EIGEN_MKL_HEMM_L(dcomplex, MKL_Complex16, cd, z)
+EIGEN_MKL_HEMM_L(scomplex, MKL_Complex8, cf, c)
+
+
+/* Optimized matrix * selfadjoint matrix (?SYMM/?HEMM) product */
+
+#define EIGEN_MKL_SYMM_R(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \
+template <typename Index, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor> \
+{\
+\
+  static EIGEN_DONT_INLINE void run( \
+    Index rows, Index cols, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resStride, \
+    EIGTYPE alpha) \
+  { \
+    char side='R', uplo='L'; \
+    MKL_INT m, n, lda, ldb, ldc; \
+    const EIGTYPE *a, *b; \
+    MKLTYPE alpha_, beta_; \
+    MatrixX##EIGPREFIX b_tmp; \
+    EIGTYPE myone(1);\
+\
+/* Set m, n, k */ \
+    m = (MKL_INT)rows;  \
+    n = (MKL_INT)cols;  \
+\
+/* Set alpha_ & beta_ */ \
+    assign_scalar_eig2mkl(alpha_, alpha); \
+    assign_scalar_eig2mkl(beta_, myone); \
+\
+/* Set lda, ldb, ldc */ \
+    lda = (MKL_INT)rhsStride; \
+    ldb = (MKL_INT)lhsStride; \
+    ldc = (MKL_INT)resStride; \
+\
+/* Set a, b, c */ \
+    if (RhsStorageOrder==RowMajor) uplo='U'; \
+    a = _rhs; \
+\
+    if (LhsStorageOrder==RowMajor) { \
+      Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(rhsStride)); \
+      b_tmp = lhs.adjoint(); \
+      b = b_tmp.data(); \
+      ldb = b_tmp.outerStride(); \
+    } else b = _lhs; \
+\
+    MKLPREFIX##symm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \
+\
+  } \
+};
+
+
+#define EIGEN_MKL_HEMM_R(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \
+template <typename Index, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor> \
+{\
+  static EIGEN_DONT_INLINE void run( \
+    Index rows, Index cols, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resStride, \
+    EIGTYPE alpha) \
+  { \
+    char side='R', uplo='L'; \
+    MKL_INT m, n, lda, ldb, ldc; \
+    const EIGTYPE *a, *b; \
+    MKLTYPE alpha_, beta_; \
+    MatrixX##EIGPREFIX b_tmp; \
+    Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> a_tmp; \
+    EIGTYPE myone(1); \
+\
+/* Set m, n, k */ \
+    m = (MKL_INT)rows; \
+    n = (MKL_INT)cols; \
+\
+/* Set alpha_ & beta_ */ \
+    assign_scalar_eig2mkl(alpha_, alpha); \
+    assign_scalar_eig2mkl(beta_, myone); \
+\
+/* Set lda, ldb, ldc */ \
+    lda = (MKL_INT)rhsStride; \
+    ldb = (MKL_INT)lhsStride; \
+    ldc = (MKL_INT)resStride; \
+\
+/* Set a, b, c */ \
+    if (((RhsStorageOrder==ColMajor) && ConjugateRhs) || ((RhsStorageOrder==RowMajor) && (!ConjugateRhs))) { \
+      Map<const Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder>, 0, OuterStride<> > rhs(_rhs,n,n,OuterStride<>(rhsStride)); \
+      a_tmp = rhs.conjugate(); \
+      a = a_tmp.data(); \
+      lda = a_tmp.outerStride(); \
+    } else a = _rhs; \
+    if (RhsStorageOrder==RowMajor) uplo='U'; \
+\
+    if (LhsStorageOrder==ColMajor && (!ConjugateLhs)) { \
+       b = _lhs; } \
+    else { \
+      if (LhsStorageOrder==ColMajor && ConjugateLhs) { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,m,n,OuterStride<>(lhsStride)); \
+        b_tmp = lhs.conjugate(); \
+      } else \
+      if (ConjugateLhs) { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \
+        b_tmp = lhs.adjoint(); \
+      } else { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \
+        b_tmp = lhs.transpose(); \
+      } \
+      b = b_tmp.data(); \
+      ldb = b_tmp.outerStride(); \
+    } \
+\
+    MKLPREFIX##hemm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \
+  } \
+};
+
+EIGEN_MKL_SYMM_R(double, double, d, d)
+EIGEN_MKL_SYMM_R(float, float, f, s)
+EIGEN_MKL_HEMM_R(dcomplex, MKL_Complex16, cd, z)
+EIGEN_MKL_HEMM_R(scomplex, MKL_Complex8, cf, c)
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_MKL_H
diff --git a/extern/Eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_MKL.h b/extern/Eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_MKL.h
new file mode 100644
index 00000000000..f88d483b653
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_MKL.h
@@ -0,0 +1,114 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   Selfadjoint matrix-vector product functionality based on ?SYMV/HEMV.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_MKL_H
+#define EIGEN_SELFADJOINT_MATRIX_VECTOR_MKL_H
+
+namespace Eigen { 
+
+namespace internal {
+
+/**********************************************************************
+* This file implements selfadjoint matrix-vector multiplication using BLAS
+**********************************************************************/
+
+// symv/hemv specialization
+
+template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs>
+struct selfadjoint_matrix_vector_product_symv :
+  selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,BuiltIn> {};
+
+#define EIGEN_MKL_SYMV_SPECIALIZE(Scalar) \
+template<typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> \
+struct selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Specialized> { \
+static EIGEN_DONT_INLINE void run( \
+  Index size, const Scalar*  lhs, Index lhsStride, \
+  const Scalar* _rhs, Index rhsIncr, Scalar* res, Scalar alpha) { \
+    enum {\
+      IsColMajor = StorageOrder==ColMajor \
+    }; \
+    if (IsColMajor == ConjugateLhs) {\
+      selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,BuiltIn>::run( \
+        size, lhs, lhsStride, _rhs, rhsIncr, res, alpha);  \
+    } else {\
+      selfadjoint_matrix_vector_product_symv<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs>::run( \
+        size, lhs, lhsStride, _rhs, rhsIncr, res, alpha);  \
+    }\
+  } \
+}; \
+
+EIGEN_MKL_SYMV_SPECIALIZE(double)
+EIGEN_MKL_SYMV_SPECIALIZE(float)
+EIGEN_MKL_SYMV_SPECIALIZE(dcomplex)
+EIGEN_MKL_SYMV_SPECIALIZE(scomplex)
+
+#define EIGEN_MKL_SYMV_SPECIALIZATION(EIGTYPE,MKLTYPE,MKLFUNC) \
+template<typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> \
+struct selfadjoint_matrix_vector_product_symv<EIGTYPE,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs> \
+{ \
+typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> SYMVVector;\
+\
+static EIGEN_DONT_INLINE void run( \
+Index size, const EIGTYPE*  lhs, Index lhsStride, \
+const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* res, EIGTYPE alpha) \
+{ \
+  enum {\
+    IsRowMajor = StorageOrder==RowMajor ? 1 : 0, \
+    IsLower = UpLo == Lower ? 1 : 0 \
+  }; \
+  MKL_INT n=size, lda=lhsStride, incx=rhsIncr, incy=1; \
+  MKLTYPE alpha_, beta_; \
+  const EIGTYPE *x_ptr, myone(1); \
+  char uplo=(IsRowMajor) ? (IsLower ? 'U' : 'L') : (IsLower ? 'L' : 'U'); \
+  assign_scalar_eig2mkl(alpha_, alpha); \
+  assign_scalar_eig2mkl(beta_, myone); \
+  SYMVVector x_tmp; \
+  if (ConjugateRhs) { \
+    Map<const SYMVVector, 0, InnerStride<> > map_x(_rhs,size,1,InnerStride<>(incx)); \
+    x_tmp=map_x.conjugate(); \
+    x_ptr=x_tmp.data(); \
+    incx=1; \
+  } else x_ptr=_rhs; \
+  MKLFUNC(&uplo, &n, &alpha_, (const MKLTYPE*)lhs, &lda, (const MKLTYPE*)x_ptr, &incx, &beta_, (MKLTYPE*)res, &incy); \
+}\
+};
+
+EIGEN_MKL_SYMV_SPECIALIZATION(double,   double,        dsymv)
+EIGEN_MKL_SYMV_SPECIALIZATION(float,    float,         ssymv)
+EIGEN_MKL_SYMV_SPECIALIZATION(dcomplex, MKL_Complex16, zhemv)
+EIGEN_MKL_SYMV_SPECIALIZATION(scomplex, MKL_Complex8,  chemv)
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_MKL_H
diff --git a/extern/Eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_MKL.h b/extern/Eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_MKL.h
new file mode 100644
index 00000000000..8173da5bb6d
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_MKL.h
@@ -0,0 +1,309 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   Triangular matrix * matrix product functionality based on ?TRMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_MKL_H
+#define EIGEN_TRIANGULAR_MATRIX_MATRIX_MKL_H
+
+namespace Eigen { 
+
+namespace internal {
+
+
+template <typename Scalar, typename Index,
+          int Mode, bool LhsIsTriangular,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResStorageOrder>
+struct product_triangular_matrix_matrix_trmm :
+       product_triangular_matrix_matrix<Scalar,Index,Mode,
+          LhsIsTriangular,LhsStorageOrder,ConjugateLhs,
+          RhsStorageOrder, ConjugateRhs, ResStorageOrder, BuiltIn> {};
+
+
+// try to go to BLAS specialization
+#define EIGEN_MKL_TRMM_SPECIALIZE(Scalar, LhsIsTriangular) \
+template <typename Index, int Mode, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_triangular_matrix_matrix<Scalar,Index, Mode, LhsIsTriangular, \
+           LhsStorageOrder,ConjugateLhs, RhsStorageOrder,ConjugateRhs,ColMajor,Specialized> { \
+  static inline void run(Index _rows, Index _cols, Index _depth, const Scalar* _lhs, Index lhsStride,\
+    const Scalar* _rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) { \
+      product_triangular_matrix_matrix_trmm<Scalar,Index,Mode, \
+        LhsIsTriangular,LhsStorageOrder,ConjugateLhs, \
+        RhsStorageOrder, ConjugateRhs, ColMajor>::run( \
+        _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha); \
+  } \
+};
+
+EIGEN_MKL_TRMM_SPECIALIZE(double, true)
+EIGEN_MKL_TRMM_SPECIALIZE(double, false)
+EIGEN_MKL_TRMM_SPECIALIZE(dcomplex, true)
+EIGEN_MKL_TRMM_SPECIALIZE(dcomplex, false)
+EIGEN_MKL_TRMM_SPECIALIZE(float, true)
+EIGEN_MKL_TRMM_SPECIALIZE(float, false)
+EIGEN_MKL_TRMM_SPECIALIZE(scomplex, true)
+EIGEN_MKL_TRMM_SPECIALIZE(scomplex, false)
+
+// implements col-major += alpha * op(triangular) * op(general)
+#define EIGEN_MKL_TRMM_L(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \
+template <typename Index, int Mode, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,true, \
+         LhsStorageOrder,ConjugateLhs,RhsStorageOrder,ConjugateRhs,ColMajor> \
+{ \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    LowUp = IsLower ? Lower : Upper, \
+    conjA = ((LhsStorageOrder==ColMajor) && ConjugateLhs) ? 1 : 0 \
+  }; \
+\
+  static EIGEN_DONT_INLINE void run( \
+    Index _rows, Index _cols, Index _depth, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resStride, \
+    EIGTYPE alpha) \
+  { \
+   Index diagSize  = (std::min)(_rows,_depth); \
+   Index rows      = IsLower ? _rows : diagSize; \
+   Index depth     = IsLower ? diagSize : _depth; \
+   Index cols      = _cols; \
+\
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs; \
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs; \
+\
+/* Non-square case - doesn't fit to MKL ?TRMM. Fall to default triangular product or call MKL ?GEMM*/ \
+   if (rows != depth) { \
+\
+     int nthr = mkl_domain_get_max_threads(MKL_BLAS); \
+\
+     if (((nthr==1) && (((std::max)(rows,depth)-diagSize)/(double)diagSize < 0.5))) { \
+     /* Most likely no benefit to call TRMM or GEMM from MKL*/ \
+       product_triangular_matrix_matrix<EIGTYPE,Index,Mode,true, \
+       LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, BuiltIn>::run( \
+           _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha); \
+     /*std::cout << "TRMM_L: A is not square! Go to Eigen TRMM implementation!\n";*/ \
+     } else { \
+     /* Make sense to call GEMM */ \
+       Map<const MatrixLhs, 0, OuterStride<> > lhsMap(_lhs,rows,depth,OuterStride<>(lhsStride)); \
+       MatrixLhs aa_tmp=lhsMap.template triangularView<Mode>(); \
+       MKL_INT aStride = aa_tmp.outerStride(); \
+       gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> blocking(_rows,_cols,_depth); \
+       general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor>::run( \
+       rows, cols, depth, aa_tmp.data(), aStride, _rhs, rhsStride, res, resStride, alpha, blocking, 0); \
+\
+     /*std::cout << "TRMM_L: A is not square! Go to MKL GEMM implementation! " << nthr<<" \n";*/ \
+     } \
+     return; \
+   } \
+   char side = 'L', transa, uplo, diag = 'N'; \
+   EIGTYPE *b; \
+   const EIGTYPE *a; \
+   MKL_INT m, n, lda, ldb; \
+   MKLTYPE alpha_; \
+\
+/* Set alpha_*/ \
+   assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \
+\
+/* Set m, n */ \
+   m = (MKL_INT)diagSize; \
+   n = (MKL_INT)cols; \
+\
+/* Set trans */ \
+   transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \
+\
+/* Set b, ldb */ \
+   Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs,depth,cols,OuterStride<>(rhsStride)); \
+   MatrixX##EIGPREFIX b_tmp; \
+\
+   if (ConjugateRhs) b_tmp = rhs.conjugate(); else b_tmp = rhs; \
+   b = b_tmp.data(); \
+   ldb = b_tmp.outerStride(); \
+\
+/* Set uplo */ \
+   uplo = IsLower ? 'L' : 'U'; \
+   if (LhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
+/* Set a, lda */ \
+   Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \
+   MatrixLhs a_tmp; \
+\
+   if ((conjA!=0) || (SetDiag==0)) { \
+     if (conjA) a_tmp = lhs.conjugate(); else a_tmp = lhs; \
+     if (IsZeroDiag) \
+       a_tmp.diagonal().setZero(); \
+     else if (IsUnitDiag) \
+       a_tmp.diagonal().setOnes();\
+     a = a_tmp.data(); \
+     lda = a_tmp.outerStride(); \
+   } else { \
+     a = _lhs; \
+     lda = lhsStride; \
+   } \
+   /*std::cout << "TRMM_L: A is square! Go to MKL TRMM implementation! \n";*/ \
+/* call ?trmm*/ \
+   MKLPREFIX##trmm(&side, &uplo, &transa, &diag, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (MKLTYPE*)b, &ldb); \
+\
+/* Add op(a_triangular)*b into res*/ \
+   Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \
+   res_tmp=res_tmp+b_tmp; \
+  } \
+};
+
+EIGEN_MKL_TRMM_L(double, double, d, d)
+EIGEN_MKL_TRMM_L(dcomplex, MKL_Complex16, cd, z)
+EIGEN_MKL_TRMM_L(float, float, f, s)
+EIGEN_MKL_TRMM_L(scomplex, MKL_Complex8, cf, c)
+
+// implements col-major += alpha * op(general) * op(triangular)
+#define EIGEN_MKL_TRMM_R(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \
+template <typename Index, int Mode, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,false, \
+         LhsStorageOrder,ConjugateLhs,RhsStorageOrder,ConjugateRhs,ColMajor> \
+{ \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    LowUp = IsLower ? Lower : Upper, \
+    conjA = ((RhsStorageOrder==ColMajor) && ConjugateRhs) ? 1 : 0 \
+  }; \
+\
+  static EIGEN_DONT_INLINE void run( \
+    Index _rows, Index _cols, Index _depth, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resStride, \
+    EIGTYPE alpha) \
+  { \
+   Index diagSize  = (std::min)(_cols,_depth); \
+   Index rows      = _rows; \
+   Index depth     = IsLower ? _depth : diagSize; \
+   Index cols      = IsLower ? diagSize : _cols; \
+\
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs; \
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs; \
+\
+/* Non-square case - doesn't fit to MKL ?TRMM. Fall to default triangular product or call MKL ?GEMM*/ \
+   if (cols != depth) { \
+\
+     int nthr = mkl_domain_get_max_threads(MKL_BLAS); \
+\
+     if ((nthr==1) && (((std::max)(cols,depth)-diagSize)/(double)diagSize < 0.5)) { \
+     /* Most likely no benefit to call TRMM or GEMM from MKL*/ \
+       product_triangular_matrix_matrix<EIGTYPE,Index,Mode,false, \
+       LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, BuiltIn>::run( \
+           _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha); \
+       /*std::cout << "TRMM_R: A is not square! Go to Eigen TRMM implementation!\n";*/ \
+     } else { \
+     /* Make sense to call GEMM */ \
+       Map<const MatrixRhs, 0, OuterStride<> > rhsMap(_rhs,depth,cols, OuterStride<>(rhsStride)); \
+       MatrixRhs aa_tmp=rhsMap.template triangularView<Mode>(); \
+       MKL_INT aStride = aa_tmp.outerStride(); \
+       gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> blocking(_rows,_cols,_depth); \
+       general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor>::run( \
+       rows, cols, depth, _lhs, lhsStride, aa_tmp.data(), aStride, res, resStride, alpha, blocking, 0); \
+\
+     /*std::cout << "TRMM_R: A is not square! Go to MKL GEMM implementation! " << nthr<<" \n";*/ \
+     } \
+     return; \
+   } \
+   char side = 'R', transa, uplo, diag = 'N'; \
+   EIGTYPE *b; \
+   const EIGTYPE *a; \
+   MKL_INT m, n, lda, ldb; \
+   MKLTYPE alpha_; \
+\
+/* Set alpha_*/ \
+   assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \
+\
+/* Set m, n */ \
+   m = (MKL_INT)rows; \
+   n = (MKL_INT)diagSize; \
+\
+/* Set trans */ \
+   transa = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \
+\
+/* Set b, ldb */ \
+   Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \
+   MatrixX##EIGPREFIX b_tmp; \
+\
+   if (ConjugateLhs) b_tmp = lhs.conjugate(); else b_tmp = lhs; \
+   b = b_tmp.data(); \
+   ldb = b_tmp.outerStride(); \
+\
+/* Set uplo */ \
+   uplo = IsLower ? 'L' : 'U'; \
+   if (RhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
+/* Set a, lda */ \
+   Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs,depth,cols, OuterStride<>(rhsStride)); \
+   MatrixRhs a_tmp; \
+\
+   if ((conjA!=0) || (SetDiag==0)) { \
+     if (conjA) a_tmp = rhs.conjugate(); else a_tmp = rhs; \
+     if (IsZeroDiag) \
+       a_tmp.diagonal().setZero(); \
+     else if (IsUnitDiag) \
+       a_tmp.diagonal().setOnes();\
+     a = a_tmp.data(); \
+     lda = a_tmp.outerStride(); \
+   } else { \
+     a = _rhs; \
+     lda = rhsStride; \
+   } \
+   /*std::cout << "TRMM_R: A is square! Go to MKL TRMM implementation! \n";*/ \
+/* call ?trmm*/ \
+   MKLPREFIX##trmm(&side, &uplo, &transa, &diag, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (MKLTYPE*)b, &ldb); \
+\
+/* Add op(a_triangular)*b into res*/ \
+   Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \
+   res_tmp=res_tmp+b_tmp; \
+  } \
+};
+
+EIGEN_MKL_TRMM_R(double, double, d, d)
+EIGEN_MKL_TRMM_R(dcomplex, MKL_Complex16, cd, z)
+EIGEN_MKL_TRMM_R(float, float, f, s)
+EIGEN_MKL_TRMM_R(scomplex, MKL_Complex8, cf, c)
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_MKL_H
diff --git a/extern/Eigen3/Eigen/src/Core/products/TriangularMatrixVector_MKL.h b/extern/Eigen3/Eigen/src/Core/products/TriangularMatrixVector_MKL.h
new file mode 100644
index 00000000000..3c2c3049aa3
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/products/TriangularMatrixVector_MKL.h
@@ -0,0 +1,247 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   Triangular matrix-vector product functionality based on ?TRMV.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_TRIANGULAR_MATRIX_VECTOR_MKL_H
+#define EIGEN_TRIANGULAR_MATRIX_VECTOR_MKL_H
+
+namespace Eigen { 
+
+namespace internal {
+
+/**********************************************************************
+* This file implements triangular matrix-vector multiplication using BLAS
+**********************************************************************/
+
+// trmv/hemv specialization
+
+template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int StorageOrder>
+struct triangular_matrix_vector_product_trmv :
+  triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,StorageOrder,BuiltIn> {};
+
+#define EIGEN_MKL_TRMV_SPECIALIZE(Scalar) \
+template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
+struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor,Specialized> { \
+ static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
+                                     const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
+      triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor>::run( \
+        _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
+  } \
+}; \
+template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
+struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor,Specialized> { \
+ static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
+                                     const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
+      triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor>::run( \
+        _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
+  } \
+};
+
+EIGEN_MKL_TRMV_SPECIALIZE(double)
+EIGEN_MKL_TRMV_SPECIALIZE(float)
+EIGEN_MKL_TRMV_SPECIALIZE(dcomplex)
+EIGEN_MKL_TRMV_SPECIALIZE(scomplex)
+
+// implements col-major: res += alpha * op(triangular) * vector
+#define EIGEN_MKL_TRMV_CM(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \
+template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
+struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor> { \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    LowUp = IsLower ? Lower : Upper \
+  }; \
+ static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
+                             const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
+ { \
+   if (ConjLhs || IsZeroDiag) { \
+     triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor,BuiltIn>::run( \
+       _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
+     return; \
+   }\
+   Index size = (std::min)(_rows,_cols); \
+   Index rows = IsLower ? _rows : size; \
+   Index cols = IsLower ? size : _cols; \
+\
+   typedef VectorX##EIGPREFIX VectorRhs; \
+   EIGTYPE *x, *y;\
+\
+/* Set x*/ \
+   Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \
+   VectorRhs x_tmp; \
+   if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
+   x = x_tmp.data(); \
+\
+/* Square part handling */\
+\
+   char trans, uplo, diag; \
+   MKL_INT m, n, lda, incx, incy; \
+   EIGTYPE const *a; \
+   MKLTYPE alpha_, beta_; \
+   assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \
+   assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(beta_, EIGTYPE(1)); \
+\
+/* Set m, n */ \
+   n = (MKL_INT)size; \
+   lda = lhsStride; \
+   incx = 1; \
+   incy = resIncr; \
+\
+/* Set uplo, trans and diag*/ \
+   trans = 'N'; \
+   uplo = IsLower ? 'L' : 'U'; \
+   diag = IsUnitDiag ? 'U' : 'N'; \
+\
+/* call ?TRMV*/ \
+   MKLPREFIX##trmv(&uplo, &trans, &diag, &n, (const MKLTYPE*)_lhs, &lda, (MKLTYPE*)x, &incx); \
+\
+/* Add op(a_tr)rhs into res*/ \
+   MKLPREFIX##axpy(&n, &alpha_,(const MKLTYPE*)x, &incx, (MKLTYPE*)_res, &incy); \
+/* Non-square case - doesn't fit to MKL ?TRMV. Fall to default triangular product*/ \
+   if (size<(std::max)(rows,cols)) { \
+     typedef Matrix<EIGTYPE, Dynamic, Dynamic> MatrixLhs; \
+     if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
+     x = x_tmp.data(); \
+     if (size<rows) { \
+       y = _res + size*resIncr; \
+       a = _lhs + size; \
+       m = rows-size; \
+       n = size; \
+     } \
+     else { \
+       x += size; \
+       y = _res; \
+       a = _lhs + size*lda; \
+       m = size; \
+       n = cols-size; \
+     } \
+     MKLPREFIX##gemv(&trans, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)x, &incx, &beta_, (MKLTYPE*)y, &incy); \
+   } \
+  } \
+};
+
+EIGEN_MKL_TRMV_CM(double, double, d, d)
+EIGEN_MKL_TRMV_CM(dcomplex, MKL_Complex16, cd, z)
+EIGEN_MKL_TRMV_CM(float, float, f, s)
+EIGEN_MKL_TRMV_CM(scomplex, MKL_Complex8, cf, c)
+
+// implements row-major: res += alpha * op(triangular) * vector
+#define EIGEN_MKL_TRMV_RM(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \
+template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
+struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor> { \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    LowUp = IsLower ? Lower : Upper \
+  }; \
+ static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
+                             const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
+ { \
+   if (IsZeroDiag) { \
+     triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor,BuiltIn>::run( \
+       _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
+     return; \
+   }\
+   Index size = (std::min)(_rows,_cols); \
+   Index rows = IsLower ? _rows : size; \
+   Index cols = IsLower ? size : _cols; \
+\
+   typedef VectorX##EIGPREFIX VectorRhs; \
+   EIGTYPE *x, *y;\
+\
+/* Set x*/ \
+   Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \
+   VectorRhs x_tmp; \
+   if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
+   x = x_tmp.data(); \
+\
+/* Square part handling */\
+\
+   char trans, uplo, diag; \
+   MKL_INT m, n, lda, incx, incy; \
+   EIGTYPE const *a; \
+   MKLTYPE alpha_, beta_; \
+   assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \
+   assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(beta_, EIGTYPE(1)); \
+\
+/* Set m, n */ \
+   n = (MKL_INT)size; \
+   lda = lhsStride; \
+   incx = 1; \
+   incy = resIncr; \
+\
+/* Set uplo, trans and diag*/ \
+   trans = ConjLhs ? 'C' : 'T'; \
+   uplo = IsLower ? 'U' : 'L'; \
+   diag = IsUnitDiag ? 'U' : 'N'; \
+\
+/* call ?TRMV*/ \
+   MKLPREFIX##trmv(&uplo, &trans, &diag, &n, (const MKLTYPE*)_lhs, &lda, (MKLTYPE*)x, &incx); \
+\
+/* Add op(a_tr)rhs into res*/ \
+   MKLPREFIX##axpy(&n, &alpha_,(const MKLTYPE*)x, &incx, (MKLTYPE*)_res, &incy); \
+/* Non-square case - doesn't fit to MKL ?TRMV. Fall to default triangular product*/ \
+   if (size<(std::max)(rows,cols)) { \
+     typedef Matrix<EIGTYPE, Dynamic, Dynamic> MatrixLhs; \
+     if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
+     x = x_tmp.data(); \
+     if (size<rows) { \
+       y = _res + size*resIncr; \
+       a = _lhs + size*lda; \
+       m = rows-size; \
+       n = size; \
+     } \
+     else { \
+       x += size; \
+       y = _res; \
+       a = _lhs + size; \
+       m = size; \
+       n = cols-size; \
+     } \
+     MKLPREFIX##gemv(&trans, &n, &m, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)x, &incx, &beta_, (MKLTYPE*)y, &incy); \
+   } \
+  } \
+};
+
+EIGEN_MKL_TRMV_RM(double, double, d, d)
+EIGEN_MKL_TRMV_RM(dcomplex, MKL_Complex16, cd, z)
+EIGEN_MKL_TRMV_RM(float, float, f, s)
+EIGEN_MKL_TRMV_RM(scomplex, MKL_Complex8, cf, c)
+
+} // end namespase internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIANGULAR_MATRIX_VECTOR_MKL_H
diff --git a/extern/Eigen3/Eigen/src/Core/products/TriangularSolverMatrix_MKL.h b/extern/Eigen3/Eigen/src/Core/products/TriangularSolverMatrix_MKL.h
new file mode 100644
index 00000000000..47699f2100f
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/products/TriangularSolverMatrix_MKL.h
@@ -0,0 +1,155 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   Triangular matrix * matrix product functionality based on ?TRMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_MKL_H
+#define EIGEN_TRIANGULAR_SOLVER_MATRIX_MKL_H
+
+namespace Eigen {
+
+namespace internal {
+
+// implements LeftSide op(triangular)^-1 * general
+#define EIGEN_MKL_TRSM_L(EIGTYPE, MKLTYPE, MKLPREFIX) \
+template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \
+struct triangular_solve_matrix<EIGTYPE,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor> \
+{ \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \
+  }; \
+  static EIGEN_DONT_INLINE void run( \
+      Index size, Index otherSize, \
+      const EIGTYPE* _tri, Index triStride, \
+      EIGTYPE* _other, Index otherStride) \
+  { \
+   MKL_INT m = size, n = otherSize, lda, ldb; \
+   char side = 'L', uplo, diag='N', transa; \
+   /* Set alpha_ */ \
+   MKLTYPE alpha; \
+   EIGTYPE myone(1); \
+   assign_scalar_eig2mkl(alpha, myone); \
+   ldb = otherStride;\
+\
+   const EIGTYPE *a; \
+/* Set trans */ \
+   transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \
+/* Set uplo */ \
+   uplo = IsLower ? 'L' : 'U'; \
+   if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
+/* Set a, lda */ \
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri; \
+   Map<const MatrixTri, 0, OuterStride<> > tri(_tri,size,size,OuterStride<>(triStride)); \
+   MatrixTri a_tmp; \
+\
+   if (conjA) { \
+     a_tmp = tri.conjugate(); \
+     a = a_tmp.data(); \
+     lda = a_tmp.outerStride(); \
+   } else { \
+     a = _tri; \
+     lda = triStride; \
+   } \
+   if (IsUnitDiag) diag='U'; \
+/* call ?trsm*/ \
+   MKLPREFIX##trsm(&side, &uplo, &transa, &diag, &m, &n, &alpha, (const MKLTYPE*)a, &lda, (MKLTYPE*)_other, &ldb); \
+ } \
+};
+
+EIGEN_MKL_TRSM_L(double, double, d)
+EIGEN_MKL_TRSM_L(dcomplex, MKL_Complex16, z)
+EIGEN_MKL_TRSM_L(float, float, s)
+EIGEN_MKL_TRSM_L(scomplex, MKL_Complex8, c)
+
+
+// implements RightSide general * op(triangular)^-1
+#define EIGEN_MKL_TRSM_R(EIGTYPE, MKLTYPE, MKLPREFIX) \
+template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \
+struct triangular_solve_matrix<EIGTYPE,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor> \
+{ \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \
+  }; \
+  static EIGEN_DONT_INLINE void run( \
+      Index size, Index otherSize, \
+      const EIGTYPE* _tri, Index triStride, \
+      EIGTYPE* _other, Index otherStride) \
+  { \
+   MKL_INT m = otherSize, n = size, lda, ldb; \
+   char side = 'R', uplo, diag='N', transa; \
+   /* Set alpha_ */ \
+   MKLTYPE alpha; \
+   EIGTYPE myone(1); \
+   assign_scalar_eig2mkl(alpha, myone); \
+   ldb = otherStride;\
+\
+   const EIGTYPE *a; \
+/* Set trans */ \
+   transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \
+/* Set uplo */ \
+   uplo = IsLower ? 'L' : 'U'; \
+   if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
+/* Set a, lda */ \
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri; \
+   Map<const MatrixTri, 0, OuterStride<> > tri(_tri,size,size,OuterStride<>(triStride)); \
+   MatrixTri a_tmp; \
+\
+   if (conjA) { \
+     a_tmp = tri.conjugate(); \
+     a = a_tmp.data(); \
+     lda = a_tmp.outerStride(); \
+   } else { \
+     a = _tri; \
+     lda = triStride; \
+   } \
+   if (IsUnitDiag) diag='U'; \
+/* call ?trsm*/ \
+   MKLPREFIX##trsm(&side, &uplo, &transa, &diag, &m, &n, &alpha, (const MKLTYPE*)a, &lda, (MKLTYPE*)_other, &ldb); \
+   /*std::cout << "TRMS_L specialization!\n";*/ \
+ } \
+};
+
+EIGEN_MKL_TRSM_R(double, double, d)
+EIGEN_MKL_TRSM_R(dcomplex, MKL_Complex16, z)
+EIGEN_MKL_TRSM_R(float, float, s)
+EIGEN_MKL_TRSM_R(scomplex, MKL_Complex8, c)
+
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_MKL_H
diff --git a/extern/Eigen3/Eigen/src/Core/util/CMakeLists.txt b/extern/Eigen3/Eigen/src/Core/util/CMakeLists.txt
new file mode 100644
index 00000000000..a1e2e521fc1
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/util/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Core_util_SRCS "*.h")
+
+INSTALL(FILES 
+  ${Eigen_Core_util_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/util COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/Core/util/MKL_support.h b/extern/Eigen3/Eigen/src/Core/util/MKL_support.h
new file mode 100644
index 00000000000..1e6e355d626
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Core/util/MKL_support.h
@@ -0,0 +1,109 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   Include file with common MKL declarations
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_MKL_SUPPORT_H
+#define EIGEN_MKL_SUPPORT_H
+
+#ifdef EIGEN_USE_MKL_ALL
+  #ifndef EIGEN_USE_BLAS
+    #define EIGEN_USE_BLAS
+  #endif
+  #ifndef EIGEN_USE_LAPACKE
+    #define EIGEN_USE_LAPACKE
+  #endif
+  #ifndef EIGEN_USE_MKL_VML
+    #define EIGEN_USE_MKL_VML
+  #endif
+#endif
+
+#ifdef EIGEN_USE_LAPACKE_STRICT
+  #define EIGEN_USE_LAPACKE
+#endif
+
+#if defined(EIGEN_USE_BLAS) || defined(EIGEN_USE_LAPACKE) || defined(EIGEN_USE_MKL_VML)
+  #define EIGEN_USE_MKL
+#endif
+
+#if defined EIGEN_USE_MKL
+
+#include <mkl.h>
+#include <mkl_lapacke.h>
+#define EIGEN_MKL_VML_THRESHOLD 128
+
+namespace Eigen {
+
+typedef std::complex<double> dcomplex;
+typedef std::complex<float>  scomplex;
+
+namespace internal {
+
+template<typename MKLType, typename EigenType>
+static inline void assign_scalar_eig2mkl(MKLType& mklScalar, const EigenType& eigenScalar) {
+  mklScalar=eigenScalar;
+}
+
+template<typename MKLType, typename EigenType>
+static inline void assign_conj_scalar_eig2mkl(MKLType& mklScalar, const EigenType& eigenScalar) {
+  mklScalar=eigenScalar;
+}
+
+template <>
+inline void assign_scalar_eig2mkl<MKL_Complex16,dcomplex>(MKL_Complex16& mklScalar, const dcomplex& eigenScalar) {
+  mklScalar.real=eigenScalar.real();
+  mklScalar.imag=eigenScalar.imag();
+}
+
+template <>
+inline void assign_scalar_eig2mkl<MKL_Complex8,scomplex>(MKL_Complex8& mklScalar, const scomplex& eigenScalar) {
+  mklScalar.real=eigenScalar.real();
+  mklScalar.imag=eigenScalar.imag();
+}
+
+template <>
+inline void assign_conj_scalar_eig2mkl<MKL_Complex16,dcomplex>(MKL_Complex16& mklScalar, const dcomplex& eigenScalar) {
+  mklScalar.real=eigenScalar.real();
+  mklScalar.imag=-eigenScalar.imag();
+}
+
+template <>
+inline void assign_conj_scalar_eig2mkl<MKL_Complex8,scomplex>(MKL_Complex8& mklScalar, const scomplex& eigenScalar) {
+  mklScalar.real=eigenScalar.real();
+  mklScalar.imag=-eigenScalar.imag();
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif
+
+#endif // EIGEN_MKL_SUPPORT_H
diff --git a/extern/Eigen3/Eigen/src/Core/util/Macros.h b/extern/Eigen3/Eigen/src/Core/util/Macros.h
index cbea97c310d..8f9cf0dbf53 100644
--- a/extern/Eigen3/Eigen/src/Core/util/Macros.h
+++ b/extern/Eigen3/Eigen/src/Core/util/Macros.h
@@ -28,7 +28,7 @@
 
 #define EIGEN_WORLD_VERSION 3
 #define EIGEN_MAJOR_VERSION 0
-#define EIGEN_MINOR_VERSION 92
+#define EIGEN_MINOR_VERSION 93
 
 #define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
                                       (EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
@@ -268,7 +268,7 @@
  * If we made alignment depend on whether or not EIGEN_VECTORIZE is defined, it would be impossible to link
  * vectorized and non-vectorized code.
  */
-#if (defined __GNUC__) || (defined __PGI) || (defined __IBMCPP__)
+#if (defined __GNUC__) || (defined __PGI) || (defined __IBMCPP__) || (defined __ARMCC_VERSION)
   #define EIGEN_ALIGN_TO_BOUNDARY(n) __attribute__((aligned(n)))
 #elif (defined _MSC_VER)
   #define EIGEN_ALIGN_TO_BOUNDARY(n) __declspec(align(n))
diff --git a/extern/Eigen3/Eigen/src/Core/util/Meta.h b/extern/Eigen3/Eigen/src/Core/util/Meta.h
index 7ece430acaa..82c93f922ae 100644
--- a/extern/Eigen3/Eigen/src/Core/util/Meta.h
+++ b/extern/Eigen3/Eigen/src/Core/util/Meta.h
@@ -103,6 +103,21 @@ template<bool Condition, typename T> struct enable_if;
 template<typename T> struct enable_if<true,T>
 { typedef T type; };
 
+
+
+/** \internal
+  * A base class do disable default copy ctor and copy assignement operator.
+  */
+class noncopyable
+{
+  noncopyable(const noncopyable&);
+  const noncopyable& operator=(const noncopyable&);
+protected:
+  noncopyable() {}
+  ~noncopyable() {}
+};
+
+
 /** \internal
   * Convenient struct to get the result type of a unary or binary functor.
   *
diff --git a/extern/Eigen3/Eigen/src/Eigen2Support/CMakeLists.txt b/extern/Eigen3/Eigen/src/Eigen2Support/CMakeLists.txt
new file mode 100644
index 00000000000..7ae41b3cbe9
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Eigen2Support/CMakeLists.txt
@@ -0,0 +1,8 @@
+FILE(GLOB Eigen_Eigen2Support_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Eigen2Support_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Eigen2Support COMPONENT Devel
+  )
+
+ADD_SUBDIRECTORY(Geometry)
\ No newline at end of file
diff --git a/extern/Eigen3/Eigen/src/Eigen2Support/Geometry/CMakeLists.txt b/extern/Eigen3/Eigen/src/Eigen2Support/Geometry/CMakeLists.txt
new file mode 100644
index 00000000000..c347a8f2622
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Eigen2Support/Geometry/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Eigen2Support_Geometry_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Eigen2Support_Geometry_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Eigen2Support/Geometry
+  )
diff --git a/extern/Eigen3/Eigen/src/Eigenvalues/CMakeLists.txt b/extern/Eigen3/Eigen/src/Eigenvalues/CMakeLists.txt
new file mode 100644
index 00000000000..193e0268599
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Eigenvalues/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_EIGENVALUES_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_EIGENVALUES_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Eigenvalues COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/Eigenvalues/ComplexSchur_MKL.h b/extern/Eigen3/Eigen/src/Eigenvalues/ComplexSchur_MKL.h
new file mode 100644
index 00000000000..e21a8d8360a
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Eigenvalues/ComplexSchur_MKL.h
@@ -0,0 +1,94 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *    Complex Schur needed to complex unsymmetrical eigenvalues/eigenvectors.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_COMPLEX_SCHUR_MKL_H
+#define EIGEN_COMPLEX_SCHUR_MKL_H
+
+#include "Eigen/src/Core/util/MKL_support.h"
+
+namespace Eigen { 
+
+/** \internal Specialization for the data types supported by MKL */
+
+#define EIGEN_MKL_SCHUR_COMPLEX(EIGTYPE, MKLTYPE, MKLPREFIX, MKLPREFIX_U, EIGCOLROW, MKLCOLROW) \
+template<> \
+ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
+ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW>& matrix, bool computeU) \
+{ \
+  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> MatrixType; \
+  typedef MatrixType::Scalar Scalar; \
+  typedef MatrixType::RealScalar RealScalar; \
+  typedef std::complex<RealScalar> ComplexScalar; \
+\
+  assert(matrix.cols() == matrix.rows()); \
+\
+  m_matUisUptodate = false; \
+  if(matrix.cols() == 1) \
+  { \
+    m_matT = matrix.cast<ComplexScalar>(); \
+    if(computeU)  m_matU = ComplexMatrixType::Identity(1,1); \
+      m_info = Success; \
+      m_isInitialized = true; \
+      m_matUisUptodate = computeU; \
+      return *this; \
+  } \
+  lapack_int n = matrix.cols(), sdim, info; \
+  lapack_int lda = matrix.outerStride(); \
+  lapack_int matrix_order = MKLCOLROW; \
+  char jobvs, sort='N'; \
+  LAPACK_##MKLPREFIX_U##_SELECT1 select = 0; \
+  jobvs = (computeU) ? 'V' : 'N'; \
+  m_matU.resize(n, n); \
+  lapack_int ldvs  = m_matU.outerStride(); \
+  m_matT = matrix; \
+  Matrix<EIGTYPE, Dynamic, Dynamic> w; \
+  w.resize(n, 1);\
+  info = LAPACKE_##MKLPREFIX##gees( matrix_order, jobvs, sort, select, n, (MKLTYPE*)m_matT.data(), lda, &sdim, (MKLTYPE*)w.data(), (MKLTYPE*)m_matU.data(), ldvs ); \
+  if(info == 0) \
+    m_info = Success; \
+  else \
+    m_info = NoConvergence; \
+\
+  m_isInitialized = true; \
+  m_matUisUptodate = computeU; \
+  return *this; \
+\
+}
+
+EIGEN_MKL_SCHUR_COMPLEX(dcomplex, MKL_Complex16, z, Z, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_SCHUR_COMPLEX(scomplex, MKL_Complex8,  c, C, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_SCHUR_COMPLEX(dcomplex, MKL_Complex16, z, Z, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_MKL_SCHUR_COMPLEX(scomplex, MKL_Complex8,  c, C, RowMajor, LAPACK_ROW_MAJOR)
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMPLEX_SCHUR_MKL_H
diff --git a/extern/Eigen3/Eigen/src/Eigenvalues/RealSchur_MKL.h b/extern/Eigen3/Eigen/src/Eigenvalues/RealSchur_MKL.h
new file mode 100644
index 00000000000..c9689520b0d
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Eigenvalues/RealSchur_MKL.h
@@ -0,0 +1,83 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *    Real Schur needed to real unsymmetrical eigenvalues/eigenvectors.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_REAL_SCHUR_MKL_H
+#define EIGEN_REAL_SCHUR_MKL_H
+
+#include "Eigen/src/Core/util/MKL_support.h"
+
+namespace Eigen { 
+
+/** \internal Specialization for the data types supported by MKL */
+
+#define EIGEN_MKL_SCHUR_REAL(EIGTYPE, MKLTYPE, MKLPREFIX, MKLPREFIX_U, EIGCOLROW, MKLCOLROW) \
+template<> \
+RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
+RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW>& matrix, bool computeU) \
+{ \
+  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> MatrixType; \
+  typedef MatrixType::Scalar Scalar; \
+  typedef MatrixType::RealScalar RealScalar; \
+\
+  assert(matrix.cols() == matrix.rows()); \
+\
+  lapack_int n = matrix.cols(), sdim, info; \
+  lapack_int lda = matrix.outerStride(); \
+  lapack_int matrix_order = MKLCOLROW; \
+  char jobvs, sort='N'; \
+  LAPACK_##MKLPREFIX_U##_SELECT2 select = 0; \
+  jobvs = (computeU) ? 'V' : 'N'; \
+  m_matU.resize(n, n); \
+  lapack_int ldvs  = m_matU.outerStride(); \
+  m_matT = matrix; \
+  Matrix<EIGTYPE, Dynamic, Dynamic> wr, wi; \
+  wr.resize(n, 1); wi.resize(n, 1); \
+  info = LAPACKE_##MKLPREFIX##gees( matrix_order, jobvs, sort, select, n, (MKLTYPE*)m_matT.data(), lda, &sdim, (MKLTYPE*)wr.data(), (MKLTYPE*)wi.data(), (MKLTYPE*)m_matU.data(), ldvs ); \
+  if(info == 0) \
+    m_info = Success; \
+  else \
+    m_info = NoConvergence; \
+\
+  m_isInitialized = true; \
+  m_matUisUptodate = computeU; \
+  return *this; \
+\
+}
+
+EIGEN_MKL_SCHUR_REAL(double,   double, d, D, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_SCHUR_REAL(float,    float,  s, S, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_SCHUR_REAL(double,   double, d, D, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_MKL_SCHUR_REAL(float,    float,  s, S, RowMajor, LAPACK_ROW_MAJOR)
+
+} // end namespace Eigen
+
+#endif // EIGEN_REAL_SCHUR_MKL_H
diff --git a/extern/Eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_MKL.h b/extern/Eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_MKL.h
new file mode 100644
index 00000000000..5ebcd08e1cf
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_MKL.h
@@ -0,0 +1,92 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *    Self-adjoint eigenvalues/eigenvectors.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_SAEIGENSOLVER_MKL_H
+#define EIGEN_SAEIGENSOLVER_MKL_H
+
+#include "Eigen/src/Core/util/MKL_support.h"
+
+namespace Eigen { 
+
+/** \internal Specialization for the data types supported by MKL */
+
+#define EIGEN_MKL_EIG_SELFADJ(EIGTYPE, MKLTYPE, MKLRTYPE, MKLNAME, EIGCOLROW, MKLCOLROW ) \
+template<> \
+SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
+SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW>& matrix, int options) \
+{ \
+  eigen_assert(matrix.cols() == matrix.rows()); \
+  eigen_assert((options&~(EigVecMask|GenEigMask))==0 \
+          && (options&EigVecMask)!=EigVecMask \
+          && "invalid option parameter"); \
+  bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; \
+  lapack_int n = matrix.cols(), lda, matrix_order, info; \
+  m_eivalues.resize(n,1); \
+  m_subdiag.resize(n-1); \
+  m_eivec = matrix; \
+\
+  if(n==1) \
+  { \
+    m_eivalues.coeffRef(0,0) = internal::real(matrix.coeff(0,0)); \
+    if(computeEigenvectors) m_eivec.setOnes(n,n); \
+    m_info = Success; \
+    m_isInitialized = true; \
+    m_eigenvectorsOk = computeEigenvectors; \
+    return *this; \
+  } \
+\
+  lda = matrix.outerStride(); \
+  matrix_order=MKLCOLROW; \
+  char jobz, uplo='L'/*, range='A'*/; \
+  jobz = computeEigenvectors ? 'V' : 'N'; \
+\
+  info = LAPACKE_##MKLNAME( matrix_order, jobz, uplo, n, (MKLTYPE*)m_eivec.data(), lda, (MKLRTYPE*)m_eivalues.data() ); \
+  m_info = (info==0) ? Success : NoConvergence; \
+  m_isInitialized = true; \
+  m_eigenvectorsOk = computeEigenvectors; \
+  return *this; \
+}
+
+
+EIGEN_MKL_EIG_SELFADJ(double,   double,        double, dsyev, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_EIG_SELFADJ(float,    float,         float,  ssyev, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_EIG_SELFADJ(dcomplex, MKL_Complex16, double, zheev, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_EIG_SELFADJ(scomplex, MKL_Complex8,  float,  cheev, ColMajor, LAPACK_COL_MAJOR)
+
+EIGEN_MKL_EIG_SELFADJ(double,   double,        double, dsyev, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_MKL_EIG_SELFADJ(float,    float,         float,  ssyev, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_MKL_EIG_SELFADJ(dcomplex, MKL_Complex16, double, zheev, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_MKL_EIG_SELFADJ(scomplex, MKL_Complex8,  float,  cheev, RowMajor, LAPACK_ROW_MAJOR)
+
+} // end namespace Eigen
+
+#endif // EIGEN_SAEIGENSOLVER_H
diff --git a/extern/Eigen3/Eigen/src/Geometry/CMakeLists.txt b/extern/Eigen3/Eigen/src/Geometry/CMakeLists.txt
new file mode 100644
index 00000000000..f8f728b84fa
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Geometry/CMakeLists.txt
@@ -0,0 +1,8 @@
+FILE(GLOB Eigen_Geometry_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Geometry_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Geometry COMPONENT Devel
+  )
+
+ADD_SUBDIRECTORY(arch)
diff --git a/extern/Eigen3/Eigen/src/Geometry/arch/CMakeLists.txt b/extern/Eigen3/Eigen/src/Geometry/arch/CMakeLists.txt
new file mode 100644
index 00000000000..1267a79c787
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Geometry/arch/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Geometry_arch_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Geometry_arch_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Geometry/arch COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/Householder/CMakeLists.txt b/extern/Eigen3/Eigen/src/Householder/CMakeLists.txt
new file mode 100644
index 00000000000..ce4937db061
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Householder/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Householder_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Householder_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Householder COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
new file mode 100644
index 00000000000..c9fe9c61849
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
@@ -0,0 +1,163 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_BASIC_PRECONDITIONERS_H
+#define EIGEN_BASIC_PRECONDITIONERS_H
+
+namespace Eigen { 
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A preconditioner based on the digonal entries
+  *
+  * This class allows to approximately solve for A.x = b problems assuming A is a diagonal matrix.
+  * In other words, this preconditioner neglects all off diagonal entries and, in Eigen's language, solves for:
+  * \code
+  * A.diagonal().asDiagonal() . x = b
+  * \endcode
+  *
+  * \tparam _Scalar the type of the scalar.
+  *
+  * This preconditioner is suitable for both selfadjoint and general problems.
+  * The diagonal entries are pre-inverted and stored into a dense vector.
+  *
+  * \note A variant that has yet to be implemented would attempt to preserve the norm of each column.
+  *
+  */
+template <typename _Scalar>
+class DiagonalPreconditioner
+{
+    typedef _Scalar Scalar;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    typedef typename Vector::Index Index;
+
+  public:
+    typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
+
+    DiagonalPreconditioner() : m_isInitialized(false) {}
+
+    template<typename MatrixType>
+    DiagonalPreconditioner(const MatrixType& mat) : m_invdiag(mat.cols())
+    {
+      compute(mat);
+    }
+
+    Index rows() const { return m_invdiag.size(); }
+    Index cols() const { return m_invdiag.size(); }
+    
+    template<typename MatrixType>
+    DiagonalPreconditioner& analyzePattern(const MatrixType& )
+    {
+      return *this;
+    }
+    
+    template<typename MatrixType>
+    DiagonalPreconditioner& factorize(const MatrixType& mat)
+    {
+      m_invdiag.resize(mat.cols());
+      for(int j=0; j<mat.outerSize(); ++j)
+      {
+        typename MatrixType::InnerIterator it(mat,j);
+        while(it && it.index()!=j) ++it;
+        if(it && it.index()==j)
+          m_invdiag(j) = Scalar(1)/it.value();
+        else
+          m_invdiag(j) = 0;
+      }
+      m_isInitialized = true;
+      return *this;
+    }
+    
+    template<typename MatrixType>
+    DiagonalPreconditioner& compute(const MatrixType& mat)
+    {
+      return factorize(mat);
+    }
+
+    template<typename Rhs, typename Dest>
+    void _solve(const Rhs& b, Dest& x) const
+    {
+      x = m_invdiag.array() * b.array() ;
+    }
+
+    template<typename Rhs> inline const internal::solve_retval<DiagonalPreconditioner, Rhs>
+    solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "DiagonalPreconditioner is not initialized.");
+      eigen_assert(m_invdiag.size()==b.rows()
+                && "DiagonalPreconditioner::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::solve_retval<DiagonalPreconditioner, Rhs>(*this, b.derived());
+    }
+
+  protected:
+    Vector m_invdiag;
+    bool m_isInitialized;
+};
+
+namespace internal {
+
+template<typename _MatrixType, typename Rhs>
+struct solve_retval<DiagonalPreconditioner<_MatrixType>, Rhs>
+  : solve_retval_base<DiagonalPreconditioner<_MatrixType>, Rhs>
+{
+  typedef DiagonalPreconditioner<_MatrixType> Dec;
+  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve(rhs(),dst);
+  }
+};
+
+}
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A naive preconditioner which approximates any matrix as the identity matrix
+  *
+  * \sa class DiagonalPreconditioner
+  */
+class IdentityPreconditioner
+{
+  public:
+
+    IdentityPreconditioner() {}
+
+    template<typename MatrixType>
+    IdentityPreconditioner(const MatrixType& ) {}
+    
+    template<typename MatrixType>
+    IdentityPreconditioner& analyzePattern(const MatrixType& ) { return *this; }
+    
+    template<typename MatrixType>
+    IdentityPreconditioner& factorize(const MatrixType& ) { return *this; }
+
+    template<typename MatrixType>
+    IdentityPreconditioner& compute(const MatrixType& ) { return *this; }
+    
+    template<typename Rhs>
+    inline const Rhs& solve(const Rhs& b) const { return b; }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_BASIC_PRECONDITIONERS_H
diff --git a/extern/Eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
new file mode 100644
index 00000000000..5f23968bc0a
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
@@ -0,0 +1,269 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_BICGSTAB_H
+#define EIGEN_BICGSTAB_H
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal Low-level bi conjugate gradient stabilized algorithm
+  * \param mat The matrix A
+  * \param rhs The right hand side vector b
+  * \param x On input and initial solution, on output the computed solution.
+  * \param precond A preconditioner being able to efficiently solve for an
+  *                approximation of Ax=b (regardless of b)
+  * \param iters On input the max number of iteration, on output the number of performed iterations.
+  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
+  * \return false in the case of numerical issue, for example a break down of BiCGSTAB. 
+  */
+template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
+              const Preconditioner& precond, int& iters,
+              typename Dest::RealScalar& tol_error)
+{
+  using std::sqrt;
+  using std::abs;
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar,Dynamic,1> VectorType;
+  RealScalar tol = tol_error;
+  int maxIters = iters;
+
+  int n = mat.cols();
+  VectorType r  = rhs - mat * x;
+  VectorType r0 = r;
+  
+  RealScalar r0_sqnorm = r0.squaredNorm();
+  Scalar rho    = 1;
+  Scalar alpha  = 1;
+  Scalar w      = 1;
+  
+  VectorType v = VectorType::Zero(n), p = VectorType::Zero(n);
+  VectorType y(n),  z(n);
+  VectorType kt(n), ks(n);
+
+  VectorType s(n), t(n);
+
+  RealScalar tol2 = tol*tol;
+  int i = 0;
+
+  while ( r.squaredNorm()/r0_sqnorm > tol2 && i<maxIters )
+  {
+    Scalar rho_old = rho;
+
+    rho = r0.dot(r);
+    if (rho == Scalar(0)) return false; /* New search directions cannot be found */
+    Scalar beta = (rho/rho_old) * (alpha / w);
+    p = r + beta * (p - w * v);
+    
+    y = precond.solve(p);
+    
+    v.noalias() = mat * y;
+
+    alpha = rho / r0.dot(v);
+    s = r - alpha * v;
+
+    z = precond.solve(s);
+    t.noalias() = mat * z;
+
+    w = t.dot(s) / t.squaredNorm();
+    x += alpha * y + w * z;
+    r = s - w * t;
+    ++i;
+  }
+  tol_error = sqrt(r.squaredNorm()/r0_sqnorm);
+  iters = i;
+  return true; 
+}
+
+}
+
+template< typename _MatrixType,
+          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
+class BiCGSTAB;
+
+namespace internal {
+
+template< typename _MatrixType, typename _Preconditioner>
+struct traits<BiCGSTAB<_MatrixType,_Preconditioner> >
+{
+  typedef _MatrixType MatrixType;
+  typedef _Preconditioner Preconditioner;
+};
+
+}
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A bi conjugate gradient stabilized solver for sparse square problems
+  *
+  * This class allows to solve for A.x = b sparse linear problems using a bi conjugate gradient
+  * stabilized algorithm. The vectors x and b can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
+  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
+  *
+  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+  * and NumTraits<Scalar>::epsilon() for the tolerance.
+  * 
+  * This class can be used as the direct solver classes. Here is a typical usage example:
+  * \code
+  * int n = 10000;
+  * VectorXd x(n), b(n);
+  * SparseMatrix<double> A(n,n);
+  * // fill A and b
+  * BiCGSTAB<SparseMatrix<double> > solver;
+  * solver(A);
+  * x = solver.solve(b);
+  * std::cout << "#iterations:     " << solver.iterations() << std::endl;
+  * std::cout << "estimated error: " << solver.error()      << std::endl;
+  * // update b, and solve again
+  * x = solver.solve(b);
+  * \endcode
+  * 
+  * By default the iterations start with x=0 as an initial guess of the solution.
+  * One can control the start using the solveWithGuess() method. Here is a step by
+  * step execution example starting with a random guess and printing the evolution
+  * of the estimated error:
+  * * \code
+  * x = VectorXd::Random(n);
+  * solver.setMaxIterations(1);
+  * int i = 0;
+  * do {
+  *   x = solver.solveWithGuess(b,x);
+  *   std::cout << i << " : " << solver.error() << std::endl;
+  *   ++i;
+  * } while (solver.info()!=Success && i<100);
+  * \endcode
+  * Note that such a step by step excution is slightly slower.
+  * 
+  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+  */
+template< typename _MatrixType, typename _Preconditioner>
+class BiCGSTAB : public IterativeSolverBase<BiCGSTAB<_MatrixType,_Preconditioner> >
+{
+  typedef IterativeSolverBase<BiCGSTAB> Base;
+  using Base::mp_matrix;
+  using Base::m_error;
+  using Base::m_iterations;
+  using Base::m_info;
+  using Base::m_isInitialized;
+public:
+  typedef _MatrixType MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::Index Index;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef _Preconditioner Preconditioner;
+
+public:
+
+  /** Default constructor. */
+  BiCGSTAB() : Base() {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+    * 
+    * This constructor is a shortcut for the default constructor followed
+    * by a call to compute().
+    * 
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  BiCGSTAB(const MatrixType& A) : Base(A) {}
+
+  ~BiCGSTAB() {}
+  
+  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A
+    * \a x0 as an initial solution.
+    *
+    * \sa compute()
+    */
+  template<typename Rhs,typename Guess>
+  inline const internal::solve_retval_with_guess<BiCGSTAB, Rhs, Guess>
+  solveWithGuess(const MatrixBase<Rhs>& b, const Guess& x0) const
+  {
+    eigen_assert(m_isInitialized && "BiCGSTAB is not initialized.");
+    eigen_assert(Base::rows()==b.rows()
+              && "BiCGSTAB::solve(): invalid number of rows of the right hand side matrix b");
+    return internal::solve_retval_with_guess
+            <BiCGSTAB, Rhs, Guess>(*this, b.derived(), x0);
+  }
+  
+  /** \internal */
+  template<typename Rhs,typename Dest>
+  void _solveWithGuess(const Rhs& b, Dest& x) const
+  {    
+    bool failed = false;
+    for(int j=0; j<b.cols(); ++j)
+    {
+      m_iterations = Base::maxIterations();
+      m_error = Base::m_tolerance;
+      
+      typename Dest::ColXpr xj(x,j);
+      if(!internal::bicgstab(*mp_matrix, b.col(j), xj, Base::m_preconditioner, m_iterations, m_error))
+        failed = true;
+    }
+    m_info = failed ? NumericalIssue
+           : m_error <= Base::m_tolerance ? Success
+           : NoConvergence;
+    m_isInitialized = true;
+  }
+
+  /** \internal */
+  template<typename Rhs,typename Dest>
+  void _solve(const Rhs& b, Dest& x) const
+  {
+    x.setZero();
+    _solveWithGuess(b,x);
+  }
+
+protected:
+
+};
+
+
+namespace internal {
+
+  template<typename _MatrixType, typename _Preconditioner, typename Rhs>
+struct solve_retval<BiCGSTAB<_MatrixType, _Preconditioner>, Rhs>
+  : solve_retval_base<BiCGSTAB<_MatrixType, _Preconditioner>, Rhs>
+{
+  typedef BiCGSTAB<_MatrixType, _Preconditioner> Dec;
+  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve(rhs(),dst);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_BICGSTAB_H
diff --git a/extern/Eigen3/Eigen/src/IterativeLinearSolvers/CMakeLists.txt b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/CMakeLists.txt
new file mode 100644
index 00000000000..59ccc0072cc
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_IterativeLinearSolvers_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_IterativeLinearSolvers_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/IterativeLinearSolvers COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
new file mode 100644
index 00000000000..edab2299e63
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
@@ -0,0 +1,266 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_CONJUGATE_GRADIENT_H
+#define EIGEN_CONJUGATE_GRADIENT_H
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal Low-level conjugate gradient algorithm
+  * \param mat The matrix A
+  * \param rhs The right hand side vector b
+  * \param x On input and initial solution, on output the computed solution.
+  * \param precond A preconditioner being able to efficiently solve for an
+  *                approximation of Ax=b (regardless of b)
+  * \param iters On input the max number of iteration, on output the number of performed iterations.
+  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
+  */
+template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+EIGEN_DONT_INLINE
+void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
+                        const Preconditioner& precond, int& iters,
+                        typename Dest::RealScalar& tol_error)
+{
+  using std::sqrt;
+  using std::abs;
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar,Dynamic,1> VectorType;
+  
+  RealScalar tol = tol_error;
+  int maxIters = iters;
+  
+  int n = mat.cols();
+
+  VectorType residual = rhs - mat * x; //initial residual
+  VectorType p(n);
+
+  p = precond.solve(residual);      //initial search direction
+
+  VectorType z(n), tmp(n);
+  RealScalar absNew = internal::real(residual.dot(p));  // the square of the absolute value of r scaled by invM
+  RealScalar rhsNorm2 = rhs.squaredNorm();
+  RealScalar residualNorm2 = 0;
+  RealScalar threshold = tol*tol*rhsNorm2;
+  int i = 0;
+  while(i < maxIters)
+  {
+    tmp.noalias() = mat * p;              // the bottleneck of the algorithm
+
+    Scalar alpha = absNew / p.dot(tmp);   // the amount we travel on dir
+    x += alpha * p;                       // update solution
+    residual -= alpha * tmp;              // update residue
+    
+    residualNorm2 = residual.squaredNorm();
+    if(residualNorm2 < threshold)
+      break;
+    
+    z = precond.solve(residual);          // approximately solve for "A z = residual"
+
+    RealScalar absOld = absNew;
+    absNew = internal::real(residual.dot(z));     // update the absolute value of r
+    RealScalar beta = absNew / absOld;            // calculate the Gram-Schmidt value used to create the new search direction
+    p = z + beta * p;                             // update search direction
+    i++;
+  }
+  tol_error = sqrt(residualNorm2 / rhsNorm2);
+  iters = i;
+}
+
+}
+
+template< typename _MatrixType, int _UpLo=Lower,
+          typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> >
+class ConjugateGradient;
+
+namespace internal {
+
+template< typename _MatrixType, int _UpLo, typename _Preconditioner>
+struct traits<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
+{
+  typedef _MatrixType MatrixType;
+  typedef _Preconditioner Preconditioner;
+};
+
+}
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A conjugate gradient solver for sparse self-adjoint problems
+  *
+  * This class allows to solve for A.x = b sparse linear problems using a conjugate gradient algorithm.
+  * The sparse matrix A must be selfadjoint. The vectors x and b can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
+  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
+  *
+  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+  * and NumTraits<Scalar>::epsilon() for the tolerance.
+  * 
+  * This class can be used as the direct solver classes. Here is a typical usage example:
+  * \code
+  * int n = 10000;
+  * VectorXd x(n), b(n);
+  * SparseMatrix<double> A(n,n);
+  * // fill A and b
+  * ConjugateGradient<SparseMatrix<double> > cg;
+  * cg.compute(A);
+  * x = cg.solve(b);
+  * std::cout << "#iterations:     " << cg.iterations() << std::endl;
+  * std::cout << "estimated error: " << cg.error()      << std::endl;
+  * // update b, and solve again
+  * x = cg.solve(b);
+  * \endcode
+  * 
+  * By default the iterations start with x=0 as an initial guess of the solution.
+  * One can control the start using the solveWithGuess() method. Here is a step by
+  * step execution example starting with a random guess and printing the evolution
+  * of the estimated error:
+  * * \code
+  * x = VectorXd::Random(n);
+  * cg.setMaxIterations(1);
+  * int i = 0;
+  * do {
+  *   x = cg.solveWithGuess(b,x);
+  *   std::cout << i << " : " << cg.error() << std::endl;
+  *   ++i;
+  * } while (cg.info()!=Success && i<100);
+  * \endcode
+  * Note that such a step by step excution is slightly slower.
+  * 
+  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+  */
+template< typename _MatrixType, int _UpLo, typename _Preconditioner>
+class ConjugateGradient : public IterativeSolverBase<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
+{
+  typedef IterativeSolverBase<ConjugateGradient> Base;
+  using Base::mp_matrix;
+  using Base::m_error;
+  using Base::m_iterations;
+  using Base::m_info;
+  using Base::m_isInitialized;
+public:
+  typedef _MatrixType MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::Index Index;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef _Preconditioner Preconditioner;
+
+  enum {
+    UpLo = _UpLo
+  };
+
+public:
+
+  /** Default constructor. */
+  ConjugateGradient() : Base() {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+    * 
+    * This constructor is a shortcut for the default constructor followed
+    * by a call to compute().
+    * 
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  ConjugateGradient(const MatrixType& A) : Base(A) {}
+
+  ~ConjugateGradient() {}
+  
+  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A
+    * \a x0 as an initial solution.
+    *
+    * \sa compute()
+    */
+  template<typename Rhs,typename Guess>
+  inline const internal::solve_retval_with_guess<ConjugateGradient, Rhs, Guess>
+  solveWithGuess(const MatrixBase<Rhs>& b, const Guess& x0) const
+  {
+    eigen_assert(m_isInitialized && "ConjugateGradient is not initialized.");
+    eigen_assert(Base::rows()==b.rows()
+              && "ConjugateGradient::solve(): invalid number of rows of the right hand side matrix b");
+    return internal::solve_retval_with_guess
+            <ConjugateGradient, Rhs, Guess>(*this, b.derived(), x0);
+  }
+
+  /** \internal */
+  template<typename Rhs,typename Dest>
+  void _solveWithGuess(const Rhs& b, Dest& x) const
+  {
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+
+    for(int j=0; j<b.cols(); ++j)
+    {
+      m_iterations = Base::maxIterations();
+      m_error = Base::m_tolerance;
+
+      typename Dest::ColXpr xj(x,j);
+      internal::conjugate_gradient(mp_matrix->template selfadjointView<UpLo>(), b.col(j), xj,
+                                   Base::m_preconditioner, m_iterations, m_error);
+    }
+
+    m_isInitialized = true;
+    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
+  }
+  
+  /** \internal */
+  template<typename Rhs,typename Dest>
+  void _solve(const Rhs& b, Dest& x) const
+  {
+    x.setOnes();
+    _solveWithGuess(b,x);
+  }
+
+protected:
+
+};
+
+
+namespace internal {
+
+template<typename _MatrixType, int _UpLo, typename _Preconditioner, typename Rhs>
+struct solve_retval<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner>, Rhs>
+  : solve_retval_base<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner>, Rhs>
+{
+  typedef ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> Dec;
+  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve(rhs(),dst);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_CONJUGATE_GRADIENT_H
diff --git a/extern/Eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
new file mode 100644
index 00000000000..32f152634f9
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
@@ -0,0 +1,476 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_INCOMPLETE_LUT_H
+#define EIGEN_INCOMPLETE_LUT_H
+
+namespace Eigen { 
+
+/**
+ * \brief Incomplete LU factorization with dual-threshold strategy
+ * During the numerical factorization, two dropping rules are used :
+ *  1) any element whose magnitude is less than some tolerance is dropped.
+ *    This tolerance is obtained by multiplying the input tolerance @p droptol 
+ *    by the average magnitude of all the original elements in the current row.
+ *  2) After the elimination of the row, only the @p fill largest elements in 
+ *    the L part and the @p fill largest elements in the U part are kept 
+ *    (in addition to the diagonal element ). Note that @p fill is computed from 
+ *    the input parameter @p fillfactor which is used the ratio to control the fill_in 
+ *    relatively to the initial number of nonzero elements.
+ * 
+ * The two extreme cases are when @p droptol=0 (to keep all the @p fill*2 largest elements)
+ * and when @p fill=n/2 with @p droptol being different to zero. 
+ * 
+ * References : Yousef Saad, ILUT: A dual threshold incomplete LU factorization, 
+ *              Numerical Linear Algebra with Applications, 1(4), pp 387-402, 1994.
+ * 
+ * NOTE : The following implementation is derived from the ILUT implementation
+ * in the SPARSKIT package, Copyright (C) 2005, the Regents of the University of Minnesota 
+ *  released under the terms of the GNU LGPL; 
+ * see http://www-users.cs.umn.edu/~saad/software/SPARSKIT/README for more details.
+ */
+template <typename _Scalar>
+class IncompleteLUT : internal::noncopyable
+{
+    typedef _Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    typedef SparseMatrix<Scalar,RowMajor> FactorType;
+    typedef SparseMatrix<Scalar,ColMajor> PermutType;
+    typedef typename FactorType::Index Index;
+
+  public:
+    typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
+    
+    IncompleteLUT()
+      : m_droptol(NumTraits<Scalar>::dummy_precision()), m_fillfactor(10),
+        m_analysisIsOk(false), m_factorizationIsOk(false), m_isInitialized(false)
+    {}
+    
+    template<typename MatrixType>
+    IncompleteLUT(const MatrixType& mat, RealScalar droptol=NumTraits<Scalar>::dummy_precision(), int fillfactor = 10)
+      : m_droptol(droptol),m_fillfactor(fillfactor),
+        m_analysisIsOk(false),m_factorizationIsOk(false),m_isInitialized(false)
+    {
+      eigen_assert(fillfactor != 0);
+      compute(mat); 
+    }
+    
+    Index rows() const { return m_lu.rows(); }
+    
+    Index cols() const { return m_lu.cols(); }
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was succesful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "IncompleteLUT is not initialized.");
+      return m_info;
+    }
+    
+    template<typename MatrixType>
+    void analyzePattern(const MatrixType& amat);
+    
+    template<typename MatrixType>
+    void factorize(const MatrixType& amat);
+    
+    /**
+      * Compute an incomplete LU factorization with dual threshold on the matrix mat
+      * No pivoting is done in this version
+      * 
+      **/
+    template<typename MatrixType>
+    IncompleteLUT<Scalar>& compute(const MatrixType& amat)
+    {
+      analyzePattern(amat); 
+      factorize(amat);
+      eigen_assert(m_factorizationIsOk == true); 
+      m_isInitialized = true;
+      return *this;
+    }
+
+    void setDroptol(RealScalar droptol); 
+    void setFillfactor(int fillfactor); 
+    
+    template<typename Rhs, typename Dest>
+    void _solve(const Rhs& b, Dest& x) const
+    {
+      x = m_Pinv * b;  
+      x = m_lu.template triangularView<UnitLower>().solve(x);
+      x = m_lu.template triangularView<Upper>().solve(x);
+      x = m_P * x; 
+    }
+
+    template<typename Rhs> inline const internal::solve_retval<IncompleteLUT, Rhs>
+     solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "IncompleteLUT is not initialized.");
+      eigen_assert(cols()==b.rows()
+                && "IncompleteLUT::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::solve_retval<IncompleteLUT, Rhs>(*this, b.derived());
+    }
+
+protected:
+
+    template <typename VectorV, typename VectorI>
+    int QuickSplit(VectorV &row, VectorI &ind, int ncut);
+
+
+    /** keeps off-diagonal entries; drops diagonal entries */
+    struct keep_diag {
+      inline bool operator() (const Index& row, const Index& col, const Scalar&) const
+      {
+        return row!=col;
+      }
+    };
+
+protected:
+
+    FactorType m_lu;
+    RealScalar m_droptol;
+    int m_fillfactor;
+    bool m_analysisIsOk;
+    bool m_factorizationIsOk;
+    bool m_isInitialized;
+    ComputationInfo m_info;
+    PermutationMatrix<Dynamic,Dynamic,Index> m_P;     // Fill-reducing permutation
+    PermutationMatrix<Dynamic,Dynamic,Index> m_Pinv;  // Inverse permutation
+};
+
+/**
+ * Set control parameter droptol
+ *  \param droptol   Drop any element whose magnitude is less than this tolerance 
+ **/ 
+template<typename Scalar>
+void IncompleteLUT<Scalar>::setDroptol(RealScalar droptol)
+{
+  this->m_droptol = droptol;   
+}
+
+/**
+ * Set control parameter fillfactor
+ * \param fillfactor  This is used to compute the  number @p fill_in of largest elements to keep on each row. 
+ **/ 
+template<typename Scalar>
+void IncompleteLUT<Scalar>::setFillfactor(int fillfactor)
+{
+  this->m_fillfactor = fillfactor;   
+}
+
+
+/**
+ * Compute a quick-sort split of a vector 
+ * On output, the vector row is permuted such that its elements satisfy
+ * abs(row(i)) >= abs(row(ncut)) if i<ncut
+ * abs(row(i)) <= abs(row(ncut)) if i>ncut 
+ * \param row The vector of values
+ * \param ind The array of index for the elements in @p row
+ * \param ncut  The number of largest elements to keep
+ **/ 
+template <typename Scalar>
+template <typename VectorV, typename VectorI>
+int IncompleteLUT<Scalar>::QuickSplit(VectorV &row, VectorI &ind, int ncut)
+{
+  using std::swap;
+  int mid;
+  int n = row.size(); /* length of the vector */
+  int first, last ; 
+  
+  ncut--; /* to fit the zero-based indices */
+  first = 0; 
+  last = n-1; 
+  if (ncut < first || ncut > last ) return 0;
+  
+  do {
+    mid = first; 
+    RealScalar abskey = std::abs(row(mid)); 
+    for (int j = first + 1; j <= last; j++) {
+      if ( std::abs(row(j)) > abskey) {
+        ++mid;
+        swap(row(mid), row(j));
+        swap(ind(mid), ind(j));
+      }
+    }
+    /* Interchange for the pivot element */
+    swap(row(mid), row(first));
+    swap(ind(mid), ind(first));
+    
+    if (mid > ncut) last = mid - 1;
+    else if (mid < ncut ) first = mid + 1; 
+  } while (mid != ncut );
+  
+  return 0; /* mid is equal to ncut */ 
+}
+
+template <typename Scalar>
+template<typename _MatrixType>
+void IncompleteLUT<Scalar>::analyzePattern(const _MatrixType& amat)
+{
+  // Compute the Fill-reducing permutation
+  SparseMatrix<Scalar,ColMajor, Index> mat1 = amat;
+  SparseMatrix<Scalar,ColMajor, Index> mat2 = amat.transpose();
+  // Symmetrize the pattern
+  // FIXME for a matrix with nearly symmetric pattern, mat2+mat1 is the appropriate choice.
+  //       on the other hand for a really non-symmetric pattern, mat2*mat1 should be prefered...
+  SparseMatrix<Scalar,ColMajor, Index> AtA = mat2 + mat1;
+  AtA.prune(keep_diag());
+  internal::minimum_degree_ordering<Scalar, Index>(AtA, m_P);  // Then compute the AMD ordering...
+
+  m_Pinv  = m_P.inverse(); // ... and the inverse permutation
+
+  m_analysisIsOk = true;
+}
+
+template <typename Scalar>
+template<typename _MatrixType>
+void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
+{
+  using std::sqrt;
+  using std::swap;
+  using std::abs;
+
+  eigen_assert((amat.rows() == amat.cols()) && "The factorization should be done on a square matrix");
+  int n = amat.cols();  // Size of the matrix
+  m_lu.resize(n,n);
+  // Declare Working vectors and variables
+  Vector u(n) ;     // real values of the row -- maximum size is n --
+  VectorXi ju(n);   // column position of the values in u -- maximum size  is n
+  VectorXi jr(n);   // Indicate the position of the nonzero elements in the vector u -- A zero location is indicated by -1
+
+  // Apply the fill-reducing permutation
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  SparseMatrix<Scalar,RowMajor, Index> mat;
+  mat = amat.twistedBy(m_Pinv);
+
+  // Initialization
+  jr.fill(-1);
+  ju.fill(0);
+  u.fill(0);
+
+  // number of largest elements to keep in each row:
+  int fill_in =   static_cast<int> (amat.nonZeros()*m_fillfactor)/n+1;
+  if (fill_in > n) fill_in = n;
+
+  // number of largest nonzero elements to keep in the L and the U part of the current row:
+  int nnzL = fill_in/2;
+  int nnzU = nnzL;
+  m_lu.reserve(n * (nnzL + nnzU + 1));
+
+  // global loop over the rows of the sparse matrix
+  for (int ii = 0; ii < n; ii++)
+  {
+    // 1 - copy the lower and the upper part of the row i of mat in the working vector u
+
+    int sizeu = 1; // number of nonzero elements in the upper part of the current row
+    int sizel = 0; // number of nonzero elements in the lower part of the current row
+    ju(ii)    = ii;
+    u(ii)     = 0;
+    jr(ii)    = ii;
+    RealScalar rownorm = 0;
+
+    typename FactorType::InnerIterator j_it(mat, ii); // Iterate through the current row ii
+    for (; j_it; ++j_it)
+    {
+      int k = j_it.index();
+      if (k < ii)
+      {
+        // copy the lower part
+        ju(sizel) = k;
+        u(sizel) = j_it.value();
+        jr(k) = sizel;
+        ++sizel;
+      }
+      else if (k == ii)
+      {
+        u(ii) = j_it.value();
+      }
+      else
+      {
+        // copy the upper part
+        int jpos = ii + sizeu;
+        ju(jpos) = k;
+        u(jpos) = j_it.value();
+        jr(k) = jpos;
+        ++sizeu;
+      }
+      rownorm += internal::abs2(j_it.value());
+    }
+
+    // 2 - detect possible zero row
+    if(rownorm==0)
+    {
+      m_info = NumericalIssue;
+      return;
+    }
+    // Take the 2-norm of the current row as a relative tolerance
+    rownorm = sqrt(rownorm);
+
+    // 3 - eliminate the previous nonzero rows
+    int jj = 0;
+    int len = 0;
+    while (jj < sizel)
+    {
+      // In order to eliminate in the correct order,
+      // we must select first the smallest column index among  ju(jj:sizel)
+      int k;
+      int minrow = ju.segment(jj,sizel-jj).minCoeff(&k); // k is relative to the segment
+      k += jj;
+      if (minrow != ju(jj))
+      {
+        // swap the two locations
+        int j = ju(jj);
+        swap(ju(jj), ju(k));
+        jr(minrow) = jj;   jr(j) = k;
+        swap(u(jj), u(k));
+      }
+      // Reset this location
+      jr(minrow) = -1;
+
+      // Start elimination
+      typename FactorType::InnerIterator ki_it(m_lu, minrow);
+      while (ki_it && ki_it.index() < minrow) ++ki_it;
+      eigen_internal_assert(ki_it && ki_it.col()==minrow);
+      Scalar fact = u(jj) / ki_it.value();
+
+      // drop too small elements
+      if(abs(fact) <= m_droptol)
+      {
+        jj++;
+        continue;
+      }
+
+      // linear combination of the current row ii and the row minrow
+      ++ki_it;
+      for (; ki_it; ++ki_it)
+      {
+        Scalar prod = fact * ki_it.value();
+        int j       = ki_it.index();
+        int jpos    = jr(j);
+        if (jpos == -1) // fill-in element
+        {
+          int newpos;
+          if (j >= ii) // dealing with the upper part
+          {
+            newpos = ii + sizeu;
+            sizeu++;
+            eigen_internal_assert(sizeu<=n);
+          }
+          else // dealing with the lower part
+          {
+            newpos = sizel;
+            sizel++;
+            eigen_internal_assert(sizel<=ii);
+          }
+          ju(newpos) = j;
+          u(newpos) = -prod;
+          jr(j) = newpos;
+        }
+        else
+          u(jpos) -= prod;
+      }
+      // store the pivot element
+      u(len) = fact;
+      ju(len) = minrow;
+      ++len;
+
+      jj++;
+    } // end of the elimination on the row ii
+
+    // reset the upper part of the pointer jr to zero
+    for(int k = 0; k <sizeu; k++) jr(ju(ii+k)) = -1;
+
+    // 4 - partially sort and insert the elements in the m_lu matrix
+
+    // sort the L-part of the row
+    sizel = len;
+    len = (std::min)(sizel, nnzL);
+    typename Vector::SegmentReturnType ul(u.segment(0, sizel));
+    typename VectorXi::SegmentReturnType jul(ju.segment(0, sizel));
+    QuickSplit(ul, jul, len);
+
+    // store the largest m_fill elements of the L part
+    m_lu.startVec(ii);
+    for(int k = 0; k < len; k++)
+      m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
+
+    // store the diagonal element
+    // apply a shifting rule to avoid zero pivots (we are doing an incomplete factorization)
+    if (u(ii) == Scalar(0))
+      u(ii) = sqrt(m_droptol) * rownorm;
+    m_lu.insertBackByOuterInnerUnordered(ii, ii) = u(ii);
+
+    // sort the U-part of the row
+    // apply the dropping rule first
+    len = 0;
+    for(int k = 1; k < sizeu; k++)
+    {
+      if(abs(u(ii+k)) > m_droptol * rownorm )
+      {
+        ++len;
+        u(ii + len)  = u(ii + k);
+        ju(ii + len) = ju(ii + k);
+      }
+    }
+    sizeu = len + 1; // +1 to take into account the diagonal element
+    len = (std::min)(sizeu, nnzU);
+    typename Vector::SegmentReturnType uu(u.segment(ii+1, sizeu-1));
+    typename VectorXi::SegmentReturnType juu(ju.segment(ii+1, sizeu-1));
+    QuickSplit(uu, juu, len);
+
+    // store the largest elements of the U part
+    for(int k = ii + 1; k < ii + len; k++)
+      m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
+  }
+
+  m_lu.finalize();
+  m_lu.makeCompressed();
+
+  m_factorizationIsOk = true;
+  m_info = Success;
+}
+
+namespace internal {
+
+template<typename _MatrixType, typename Rhs>
+struct solve_retval<IncompleteLUT<_MatrixType>, Rhs>
+  : solve_retval_base<IncompleteLUT<_MatrixType>, Rhs>
+{
+  typedef IncompleteLUT<_MatrixType> Dec;
+  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve(rhs(),dst);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_INCOMPLETE_LUT_H
+
diff --git a/extern/Eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
new file mode 100644
index 00000000000..b27ad82ec49
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
@@ -0,0 +1,269 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_ITERATIVE_SOLVER_BASE_H
+#define EIGEN_ITERATIVE_SOLVER_BASE_H
+
+namespace Eigen { 
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief Base class for linear iterative solvers
+  *
+  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+  */
+template< typename Derived>
+class IterativeSolverBase : internal::noncopyable
+{
+public:
+  typedef typename internal::traits<Derived>::MatrixType MatrixType;
+  typedef typename internal::traits<Derived>::Preconditioner Preconditioner;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::Index Index;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+public:
+
+  Derived& derived() { return *static_cast<Derived*>(this); }
+  const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+  /** Default constructor. */
+  IterativeSolverBase()
+    : mp_matrix(0)
+  {
+    init();
+  }
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+    * 
+    * This constructor is a shortcut for the default constructor followed
+    * by a call to compute().
+    * 
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  IterativeSolverBase(const MatrixType& A)
+  {
+    init();
+    compute(A);
+  }
+
+  ~IterativeSolverBase() {}
+  
+  /** Initializes the iterative solver for the sparcity pattern of the matrix \a A for further solving \c Ax=b problems.
+    *
+    * Currently, this function mostly call analyzePattern on the preconditioner. In the future
+    * we might, for instance, implement column reodering for faster matrix vector products.
+    */
+  Derived& analyzePattern(const MatrixType& A)
+  {
+    m_preconditioner.analyzePattern(A);
+    m_isInitialized = true;
+    m_analysisIsOk = true;
+    m_info = Success;
+    return derived();
+  }
+  
+  /** Initializes the iterative solver with the numerical values of the matrix \a A for further solving \c Ax=b problems.
+    *
+    * Currently, this function mostly call factorize on the preconditioner.
+    *
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  Derived& factorize(const MatrixType& A)
+  {
+    eigen_assert(m_analysisIsOk && "You must first call analyzePattern()"); 
+    mp_matrix = &A;
+    m_preconditioner.factorize(A);
+    m_factorizationIsOk = true;
+    m_info = Success;
+    return derived();
+  }
+
+  /** Initializes the iterative solver with the matrix \a A for further solving \c Ax=b problems.
+    *
+    * Currently, this function mostly initialized/compute the preconditioner. In the future
+    * we might, for instance, implement column reodering for faster matrix vector products.
+    *
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  Derived& compute(const MatrixType& A)
+  {
+    mp_matrix = &A;
+    m_preconditioner.compute(A);
+    m_isInitialized = true;
+    m_analysisIsOk = true;
+    m_factorizationIsOk = true;
+    m_info = Success;
+    return derived();
+  }
+
+  /** \internal */
+  Index rows() const { return mp_matrix ? mp_matrix->rows() : 0; }
+  /** \internal */
+  Index cols() const { return mp_matrix ? mp_matrix->cols() : 0; }
+
+  /** \returns the tolerance threshold used by the stopping criteria */
+  RealScalar tolerance() const { return m_tolerance; }
+  
+  /** Sets the tolerance threshold used by the stopping criteria */
+  Derived& setTolerance(RealScalar tolerance)
+  {
+    m_tolerance = tolerance;
+    return derived();
+  }
+
+  /** \returns a read-write reference to the preconditioner for custom configuration. */
+  Preconditioner& preconditioner() { return m_preconditioner; }
+  
+  /** \returns a read-only reference to the preconditioner. */
+  const Preconditioner& preconditioner() const { return m_preconditioner; }
+
+  /** \returns the max number of iterations */
+  int maxIterations() const
+  {
+    return (mp_matrix && m_maxIterations<0) ? mp_matrix->cols() : m_maxIterations;
+  }
+  
+  /** Sets the max number of iterations */
+  Derived& setMaxIterations(int maxIters)
+  {
+    m_maxIterations = maxIters;
+    return derived();
+  }
+
+  /** \returns the number of iterations performed during the last solve */
+  int iterations() const
+  {
+    eigen_assert(m_isInitialized && "ConjugateGradient is not initialized.");
+    return m_iterations;
+  }
+
+  /** \returns the tolerance error reached during the last solve */
+  RealScalar error() const
+  {
+    eigen_assert(m_isInitialized && "ConjugateGradient is not initialized.");
+    return m_error;
+  }
+
+  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+    *
+    * \sa compute()
+    */
+  template<typename Rhs> inline const internal::solve_retval<Derived, Rhs>
+  solve(const MatrixBase<Rhs>& b) const
+  {
+    eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
+    eigen_assert(rows()==b.rows()
+              && "IterativeSolverBase::solve(): invalid number of rows of the right hand side matrix b");
+    return internal::solve_retval<Derived, Rhs>(derived(), b.derived());
+  }
+  
+  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+    *
+    * \sa compute()
+    */
+  template<typename Rhs>
+  inline const internal::sparse_solve_retval<IterativeSolverBase, Rhs>
+  solve(const SparseMatrixBase<Rhs>& b) const
+  {
+    eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
+    eigen_assert(rows()==b.rows()
+              && "IterativeSolverBase::solve(): invalid number of rows of the right hand side matrix b");
+    return internal::sparse_solve_retval<IterativeSolverBase, Rhs>(*this, b.derived());
+  }
+
+  /** \returns Success if the iterations converged, and NoConvergence otherwise. */
+  ComputationInfo info() const
+  {
+    eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
+    return m_info;
+  }
+  
+  /** \internal */
+  template<typename Rhs, typename DestScalar, int DestOptions, typename DestIndex>
+  void _solve_sparse(const Rhs& b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
+  {
+    eigen_assert(rows()==b.rows());
+    
+    int rhsCols = b.cols();
+    int size = b.rows();
+    Eigen::Matrix<DestScalar,Dynamic,1> tb(size);
+    Eigen::Matrix<DestScalar,Dynamic,1> tx(size);
+    for(int k=0; k<rhsCols; ++k)
+    {
+      tb = b.col(k);
+      tx = derived().solve(tb);
+      dest.col(k) = tx.sparseView(0);
+    }
+  }
+
+protected:
+  void init()
+  {
+    m_isInitialized = false;
+    m_analysisIsOk = false;
+    m_factorizationIsOk = false;
+    m_maxIterations = -1;
+    m_tolerance = NumTraits<Scalar>::epsilon();
+  }
+  const MatrixType* mp_matrix;
+  Preconditioner m_preconditioner;
+
+  int m_maxIterations;
+  RealScalar m_tolerance;
+  
+  mutable RealScalar m_error;
+  mutable int m_iterations;
+  mutable ComputationInfo m_info;
+  mutable bool m_isInitialized, m_analysisIsOk, m_factorizationIsOk;
+};
+
+namespace internal {
+ 
+template<typename Derived, typename Rhs>
+struct sparse_solve_retval<IterativeSolverBase<Derived>, Rhs>
+  : sparse_solve_retval_base<IterativeSolverBase<Derived>, Rhs>
+{
+  typedef IterativeSolverBase<Derived> Dec;
+  EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec().derived()._solve_sparse(rhs(),dst);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_ITERATIVE_SOLVER_BASE_H
diff --git a/extern/Eigen3/Eigen/src/Jacobi/CMakeLists.txt b/extern/Eigen3/Eigen/src/Jacobi/CMakeLists.txt
new file mode 100644
index 00000000000..490dac62643
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Jacobi/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_Jacobi_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_Jacobi_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Jacobi COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/LU/CMakeLists.txt b/extern/Eigen3/Eigen/src/LU/CMakeLists.txt
new file mode 100644
index 00000000000..e0d8d78c172
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/LU/CMakeLists.txt
@@ -0,0 +1,8 @@
+FILE(GLOB Eigen_LU_SRCS "*.h")
+
+INSTALL(FILES 
+  ${Eigen_LU_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LU COMPONENT Devel
+  )
+
+ADD_SUBDIRECTORY(arch)
diff --git a/extern/Eigen3/Eigen/src/LU/PartialPivLU_MKL.h b/extern/Eigen3/Eigen/src/LU/PartialPivLU_MKL.h
new file mode 100644
index 00000000000..9035953c82f
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/LU/PartialPivLU_MKL.h
@@ -0,0 +1,85 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *     LU decomposition with partial pivoting based on LAPACKE_?getrf function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_PARTIALLU_LAPACK_H
+#define EIGEN_PARTIALLU_LAPACK_H
+
+#include "Eigen/src/Core/util/MKL_support.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal Specialization for the data types supported by MKL */
+
+#define EIGEN_MKL_LU_PARTPIV(EIGTYPE, MKLTYPE, MKLPREFIX) \
+template<int StorageOrder> \
+struct partial_lu_impl<EIGTYPE, StorageOrder, lapack_int> \
+{ \
+  /* \internal performs the LU decomposition in-place of the matrix represented */ \
+  static lapack_int blocked_lu(lapack_int rows, lapack_int cols, EIGTYPE* lu_data, lapack_int luStride, lapack_int* row_transpositions, lapack_int& nb_transpositions, lapack_int maxBlockSize=256) \
+  { \
+    EIGEN_UNUSED_VARIABLE(maxBlockSize);\
+    lapack_int matrix_order, first_zero_pivot; \
+    lapack_int m, n, lda, *ipiv, info; \
+    EIGTYPE* a; \
+/* Set up parameters for ?getrf */ \
+    matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
+    lda = luStride; \
+    a = lu_data; \
+    ipiv = row_transpositions; \
+    m = rows; \
+    n = cols; \
+    nb_transpositions = 0; \
+\
+    info = LAPACKE_##MKLPREFIX##getrf( matrix_order, m, n, (MKLTYPE*)a, lda, ipiv ); \
+\
+    for(int i=0;i<m;i++) { ipiv[i]--; if (ipiv[i]!=i) nb_transpositions++; } \
+\
+    eigen_assert(info >= 0); \
+/* something should be done with nb_transpositions */ \
+\
+    first_zero_pivot = info; \
+    return first_zero_pivot; \
+  } \
+};
+
+EIGEN_MKL_LU_PARTPIV(double, double, d)
+EIGEN_MKL_LU_PARTPIV(float, float, s)
+EIGEN_MKL_LU_PARTPIV(dcomplex, MKL_Complex16, z)
+EIGEN_MKL_LU_PARTPIV(scomplex, MKL_Complex8, c)
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PARTIALLU_LAPACK_H
diff --git a/extern/Eigen3/Eigen/src/LU/arch/CMakeLists.txt b/extern/Eigen3/Eigen/src/LU/arch/CMakeLists.txt
new file mode 100644
index 00000000000..f6b7ed9ecc3
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/LU/arch/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_LU_arch_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_LU_arch_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LU/arch COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/OrderingMethods/Amd.h b/extern/Eigen3/Eigen/src/OrderingMethods/Amd.h
new file mode 100644
index 00000000000..df4e01d6cb0
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/OrderingMethods/Amd.h
@@ -0,0 +1,451 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+/*
+
+NOTE: this routine has been adapted from the CSparse library:
+
+Copyright (c) 2006, Timothy A. Davis.
+http://www.cise.ufl.edu/research/sparse/CSparse
+
+CSparse is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+CSparse is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this Module; if not, write to the Free Software
+Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
+
+*/
+
+#ifndef EIGEN_SPARSE_AMD_H
+#define EIGEN_SPARSE_AMD_H
+
+namespace Eigen { 
+
+namespace internal {
+  
+template<typename T> inline T amd_flip(const T& i) { return -i-2; }
+template<typename T> inline T amd_unflip(const T& i) { return i<0 ? amd_flip(i) : i; }
+template<typename T0, typename T1> inline bool amd_marked(const T0* w, const T1& j) { return w[j]<0; }
+template<typename T0, typename T1> inline void amd_mark(const T0* w, const T1& j) { return w[j] = amd_flip(w[j]); }
+
+/* clear w */
+template<typename Index>
+static int cs_wclear (Index mark, Index lemax, Index *w, Index n)
+{
+  Index k;
+  if(mark < 2 || (mark + lemax < 0))
+  {
+    for(k = 0; k < n; k++)
+      if(w[k] != 0)
+        w[k] = 1;
+    mark = 2;
+  }
+  return (mark);     /* at this point, w[0..n-1] < mark holds */
+}
+
+/* depth-first search and postorder of a tree rooted at node j */
+template<typename Index>
+Index cs_tdfs(Index j, Index k, Index *head, const Index *next, Index *post, Index *stack)
+{
+  int i, p, top = 0;
+  if(!head || !next || !post || !stack) return (-1);    /* check inputs */
+  stack[0] = j;                 /* place j on the stack */
+  while (top >= 0)                /* while (stack is not empty) */
+  {
+    p = stack[top];           /* p = top of stack */
+    i = head[p];              /* i = youngest child of p */
+    if(i == -1)
+    {
+      top--;                 /* p has no unordered children left */
+      post[k++] = p;        /* node p is the kth postordered node */
+    }
+    else
+    {
+      head[p] = next[i];   /* remove i from children of p */
+      stack[++top] = i;     /* start dfs on child node i */
+    }
+  }
+  return k;
+}
+
+
+/** \internal
+  * Approximate minimum degree ordering algorithm.
+  * \returns the permutation P reducing the fill-in of the input matrix \a C
+  * The input matrix \a C must be a selfadjoint compressed column major SparseMatrix object. Both the upper and lower parts have to be stored, but the diagonal entries are optional.
+  * On exit the values of C are destroyed */
+template<typename Scalar, typename Index>
+void minimum_degree_ordering(SparseMatrix<Scalar,ColMajor,Index>& C, PermutationMatrix<Dynamic,Dynamic,Index>& perm)
+{
+  typedef SparseMatrix<Scalar,ColMajor,Index> CCS;
+  
+  int d, dk, dext, lemax = 0, e, elenk, eln, i, j, k, k1,
+      k2, k3, jlast, ln, dense, nzmax, mindeg = 0, nvi, nvj, nvk, mark, wnvi,
+      ok, nel = 0, p, p1, p2, p3, p4, pj, pk, pk1, pk2, pn, q, t;
+  unsigned int h;
+  
+  Index n = C.cols();
+  dense = std::max<Index> (16, 10 * sqrt ((double) n));   /* find dense threshold */
+  dense = std::min<Index> (n-2, dense);
+  
+  Index cnz = C.nonZeros();
+  perm.resize(n+1);
+  t = cnz + cnz/5 + 2*n;                 /* add elbow room to C */
+  C.resizeNonZeros(t);
+  
+  Index* W       = new Index[8*(n+1)]; /* get workspace */
+  Index* len     = W;
+  Index* nv      = W +   (n+1);
+  Index* next    = W + 2*(n+1);
+  Index* head    = W + 3*(n+1);
+  Index* elen    = W + 4*(n+1);
+  Index* degree  = W + 5*(n+1);
+  Index* w       = W + 6*(n+1);
+  Index* hhead   = W + 7*(n+1);
+  Index* last    = perm.indices().data();                              /* use P as workspace for last */
+  
+  /* --- Initialize quotient graph ---------------------------------------- */
+  Index* Cp = C.outerIndexPtr();
+  Index* Ci = C.innerIndexPtr();
+  for(k = 0; k < n; k++)
+    len[k] = Cp[k+1] - Cp[k];
+  len[n] = 0;
+  nzmax = t;
+  
+  for(i = 0; i <= n; i++)
+  {
+    head[i]   = -1;                     // degree list i is empty
+    last[i]   = -1;
+    next[i]   = -1;
+    hhead[i]  = -1;                     // hash list i is empty 
+    nv[i]     = 1;                      // node i is just one node
+    w[i]      = 1;                      // node i is alive
+    elen[i]   = 0;                      // Ek of node i is empty
+    degree[i] = len[i];                 // degree of node i
+  }
+  mark = internal::cs_wclear<Index>(0, 0, w, n);         /* clear w */
+  elen[n] = -2;                         /* n is a dead element */
+  Cp[n] = -1;                           /* n is a root of assembly tree */
+  w[n] = 0;                             /* n is a dead element */
+  
+  /* --- Initialize degree lists ------------------------------------------ */
+  for(i = 0; i < n; i++)
+  {
+    d = degree[i];
+    if(d == 0)                         /* node i is empty */
+    {
+      elen[i] = -2;                 /* element i is dead */
+      nel++;
+      Cp[i] = -1;                   /* i is a root of assembly tree */
+      w[i] = 0;
+    }
+    else if(d > dense)                 /* node i is dense */
+    {
+      nv[i] = 0;                    /* absorb i into element n */
+      elen[i] = -1;                 /* node i is dead */
+      nel++;
+      Cp[i] = amd_flip (n);
+      nv[n]++;
+    }
+    else
+    {
+      if(head[d] != -1) last[head[d]] = i;
+      next[i] = head[d];           /* put node i in degree list d */
+      head[d] = i;
+    }
+  }
+  
+  while (nel < n)                         /* while (selecting pivots) do */
+  {
+    /* --- Select node of minimum approximate degree -------------------- */
+    for(k = -1; mindeg < n && (k = head[mindeg]) == -1; mindeg++) {}
+    if(next[k] != -1) last[next[k]] = -1;
+    head[mindeg] = next[k];          /* remove k from degree list */
+    elenk = elen[k];                  /* elenk = |Ek| */
+    nvk = nv[k];                      /* # of nodes k represents */
+    nel += nvk;                        /* nv[k] nodes of A eliminated */
+    
+    /* --- Garbage collection ------------------------------------------- */
+    if(elenk > 0 && cnz + mindeg >= nzmax)
+    {
+      for(j = 0; j < n; j++)
+      {
+        if((p = Cp[j]) >= 0)      /* j is a live node or element */
+        {
+          Cp[j] = Ci[p];          /* save first entry of object */
+          Ci[p] = amd_flip (j);    /* first entry is now amd_flip(j) */
+        }
+      }
+      for(q = 0, p = 0; p < cnz; ) /* scan all of memory */
+      {
+        if((j = amd_flip (Ci[p++])) >= 0)  /* found object j */
+        {
+          Ci[q] = Cp[j];       /* restore first entry of object */
+          Cp[j] = q++;          /* new pointer to object j */
+          for(k3 = 0; k3 < len[j]-1; k3++) Ci[q++] = Ci[p++];
+        }
+      }
+      cnz = q;                       /* Ci[cnz...nzmax-1] now free */
+    }
+    
+    /* --- Construct new element ---------------------------------------- */
+    dk = 0;
+    nv[k] = -nvk;                     /* flag k as in Lk */
+    p = Cp[k];
+    pk1 = (elenk == 0) ? p : cnz;      /* do in place if elen[k] == 0 */
+    pk2 = pk1;
+    for(k1 = 1; k1 <= elenk + 1; k1++)
+    {
+      if(k1 > elenk)
+      {
+        e = k;                     /* search the nodes in k */
+        pj = p;                    /* list of nodes starts at Ci[pj]*/
+        ln = len[k] - elenk;      /* length of list of nodes in k */
+      }
+      else
+      {
+        e = Ci[p++];              /* search the nodes in e */
+        pj = Cp[e];
+        ln = len[e];              /* length of list of nodes in e */
+      }
+      for(k2 = 1; k2 <= ln; k2++)
+      {
+        i = Ci[pj++];
+        if((nvi = nv[i]) <= 0) continue; /* node i dead, or seen */
+        dk += nvi;                 /* degree[Lk] += size of node i */
+        nv[i] = -nvi;             /* negate nv[i] to denote i in Lk*/
+        Ci[pk2++] = i;            /* place i in Lk */
+        if(next[i] != -1) last[next[i]] = last[i];
+        if(last[i] != -1)         /* remove i from degree list */
+        {
+          next[last[i]] = next[i];
+        }
+        else
+        {
+          head[degree[i]] = next[i];
+        }
+      }
+      if(e != k)
+      {
+        Cp[e] = amd_flip (k);      /* absorb e into k */
+        w[e] = 0;                 /* e is now a dead element */
+      }
+    }
+    if(elenk != 0) cnz = pk2;         /* Ci[cnz...nzmax] is free */
+    degree[k] = dk;                   /* external degree of k - |Lk\i| */
+    Cp[k] = pk1;                      /* element k is in Ci[pk1..pk2-1] */
+    len[k] = pk2 - pk1;
+    elen[k] = -2;                     /* k is now an element */
+    
+    /* --- Find set differences ----------------------------------------- */
+    mark = internal::cs_wclear<Index>(mark, lemax, w, n);  /* clear w if necessary */
+    for(pk = pk1; pk < pk2; pk++)    /* scan 1: find |Le\Lk| */
+    {
+      i = Ci[pk];
+      if((eln = elen[i]) <= 0) continue;/* skip if elen[i] empty */
+      nvi = -nv[i];                      /* nv[i] was negated */
+      wnvi = mark - nvi;
+      for(p = Cp[i]; p <= Cp[i] + eln - 1; p++)  /* scan Ei */
+      {
+        e = Ci[p];
+        if(w[e] >= mark)
+        {
+          w[e] -= nvi;          /* decrement |Le\Lk| */
+        }
+        else if(w[e] != 0)        /* ensure e is a live element */
+        {
+          w[e] = degree[e] + wnvi; /* 1st time e seen in scan 1 */
+        }
+      }
+    }
+    
+    /* --- Degree update ------------------------------------------------ */
+    for(pk = pk1; pk < pk2; pk++)    /* scan2: degree update */
+    {
+      i = Ci[pk];                   /* consider node i in Lk */
+      p1 = Cp[i];
+      p2 = p1 + elen[i] - 1;
+      pn = p1;
+      for(h = 0, d = 0, p = p1; p <= p2; p++)    /* scan Ei */
+      {
+        e = Ci[p];
+        if(w[e] != 0)             /* e is an unabsorbed element */
+        {
+          dext = w[e] - mark;   /* dext = |Le\Lk| */
+          if(dext > 0)
+          {
+            d += dext;         /* sum up the set differences */
+            Ci[pn++] = e;     /* keep e in Ei */
+            h += e;            /* compute the hash of node i */
+          }
+          else
+          {
+            Cp[e] = amd_flip (k);  /* aggressive absorb. e->k */
+            w[e] = 0;             /* e is a dead element */
+          }
+        }
+      }
+      elen[i] = pn - p1 + 1;        /* elen[i] = |Ei| */
+      p3 = pn;
+      p4 = p1 + len[i];
+      for(p = p2 + 1; p < p4; p++) /* prune edges in Ai */
+      {
+        j = Ci[p];
+        if((nvj = nv[j]) <= 0) continue; /* node j dead or in Lk */
+        d += nvj;                  /* degree(i) += |j| */
+        Ci[pn++] = j;             /* place j in node list of i */
+        h += j;                    /* compute hash for node i */
+      }
+      if(d == 0)                     /* check for mass elimination */
+      {
+        Cp[i] = amd_flip (k);      /* absorb i into k */
+        nvi = -nv[i];
+        dk -= nvi;                 /* |Lk| -= |i| */
+        nvk += nvi;                /* |k| += nv[i] */
+        nel += nvi;
+        nv[i] = 0;
+        elen[i] = -1;             /* node i is dead */
+      }
+      else
+      {
+        degree[i] = std::min<Index> (degree[i], d);   /* update degree(i) */
+        Ci[pn] = Ci[p3];         /* move first node to end */
+        Ci[p3] = Ci[p1];         /* move 1st el. to end of Ei */
+        Ci[p1] = k;               /* add k as 1st element in of Ei */
+        len[i] = pn - p1 + 1;     /* new len of adj. list of node i */
+        h %= n;                    /* finalize hash of i */
+        next[i] = hhead[h];      /* place i in hash bucket */
+        hhead[h] = i;
+        last[i] = h;              /* save hash of i in last[i] */
+      }
+    }                                   /* scan2 is done */
+    degree[k] = dk;                   /* finalize |Lk| */
+    lemax = std::max<Index>(lemax, dk);
+    mark = internal::cs_wclear<Index>(mark+lemax, lemax, w, n);    /* clear w */
+    
+    /* --- Supernode detection ------------------------------------------ */
+    for(pk = pk1; pk < pk2; pk++)
+    {
+      i = Ci[pk];
+      if(nv[i] >= 0) continue;         /* skip if i is dead */
+      h = last[i];                      /* scan hash bucket of node i */
+      i = hhead[h];
+      hhead[h] = -1;                    /* hash bucket will be empty */
+      for(; i != -1 && next[i] != -1; i = next[i], mark++)
+      {
+        ln = len[i];
+        eln = elen[i];
+        for(p = Cp[i]+1; p <= Cp[i] + ln-1; p++) w[Ci[p]] = mark;
+        jlast = i;
+        for(j = next[i]; j != -1; ) /* compare i with all j */
+        {
+          ok = (len[j] == ln) && (elen[j] == eln);
+          for(p = Cp[j] + 1; ok && p <= Cp[j] + ln - 1; p++)
+          {
+            if(w[Ci[p]] != mark) ok = 0;    /* compare i and j*/
+          }
+          if(ok)                     /* i and j are identical */
+          {
+            Cp[j] = amd_flip (i);  /* absorb j into i */
+            nv[i] += nv[j];
+            nv[j] = 0;
+            elen[j] = -1;         /* node j is dead */
+            j = next[j];          /* delete j from hash bucket */
+            next[jlast] = j;
+          }
+          else
+          {
+            jlast = j;             /* j and i are different */
+            j = next[j];
+          }
+        }
+      }
+    }
+    
+    /* --- Finalize new element------------------------------------------ */
+    for(p = pk1, pk = pk1; pk < pk2; pk++)   /* finalize Lk */
+    {
+      i = Ci[pk];
+      if((nvi = -nv[i]) <= 0) continue;/* skip if i is dead */
+      nv[i] = nvi;                      /* restore nv[i] */
+      d = degree[i] + dk - nvi;         /* compute external degree(i) */
+      d = std::min<Index> (d, n - nel - nvi);
+      if(head[d] != -1) last[head[d]] = i;
+      next[i] = head[d];               /* put i back in degree list */
+      last[i] = -1;
+      head[d] = i;
+      mindeg = std::min<Index> (mindeg, d);       /* find new minimum degree */
+      degree[i] = d;
+      Ci[p++] = i;                      /* place i in Lk */
+    }
+    nv[k] = nvk;                      /* # nodes absorbed into k */
+    if((len[k] = p-pk1) == 0)         /* length of adj list of element k*/
+    {
+      Cp[k] = -1;                   /* k is a root of the tree */
+      w[k] = 0;                     /* k is now a dead element */
+    }
+    if(elenk != 0) cnz = p;           /* free unused space in Lk */
+  }
+  
+  /* --- Postordering ----------------------------------------------------- */
+  for(i = 0; i < n; i++) Cp[i] = amd_flip (Cp[i]);/* fix assembly tree */
+  for(j = 0; j <= n; j++) head[j] = -1;
+  for(j = n; j >= 0; j--)              /* place unordered nodes in lists */
+  {
+    if(nv[j] > 0) continue;          /* skip if j is an element */
+    next[j] = head[Cp[j]];          /* place j in list of its parent */
+    head[Cp[j]] = j;
+  }
+  for(e = n; e >= 0; e--)              /* place elements in lists */
+  {
+    if(nv[e] <= 0) continue;         /* skip unless e is an element */
+    if(Cp[e] != -1)
+    {
+      next[e] = head[Cp[e]];      /* place e in list of its parent */
+      head[Cp[e]] = e;
+    }
+  }
+  for(k = 0, i = 0; i <= n; i++)       /* postorder the assembly tree */
+  {
+    if(Cp[i] == -1) k = internal::cs_tdfs<Index>(i, k, head, next, perm.indices().data(), w);
+  }
+  
+  perm.indices().conservativeResize(n);
+
+  delete[] W;
+}
+
+} // namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_AMD_H
diff --git a/extern/Eigen3/Eigen/src/OrderingMethods/CMakeLists.txt b/extern/Eigen3/Eigen/src/OrderingMethods/CMakeLists.txt
new file mode 100644
index 00000000000..9f4bb275808
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/OrderingMethods/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_OrderingMethods_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_OrderingMethods_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/OrderingMethods COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/PaStiXSupport/CMakeLists.txt b/extern/Eigen3/Eigen/src/PaStiXSupport/CMakeLists.txt
new file mode 100644
index 00000000000..28c657e9bcf
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/PaStiXSupport/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_PastixSupport_SRCS "*.h")
+
+INSTALL(FILES 
+  ${Eigen_PastixSupport_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/PaStiXSupport COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h b/extern/Eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h
new file mode 100644
index 00000000000..f42826208e3
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h
@@ -0,0 +1,803 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_PASTIXSUPPORT_H
+#define EIGEN_PASTIXSUPPORT_H
+
+namespace Eigen { 
+
+/** \ingroup PaStiXSupport_Module
+  * \brief Interface to the PaStix solver
+  * 
+  * This class is used to solve the linear systems A.X = B via the PaStix library. 
+  * The matrix can be either real or complex, symmetric or not.
+  *
+  * \sa TutorialSparseDirectSolvers
+  */
+
+template<typename _MatrixType, bool IsStrSym = false> class PastixLU;
+template<typename _MatrixType, int Options> class PastixLLT;
+template<typename _MatrixType, int Options> class PastixLDLT;
+
+namespace internal
+{
+    
+  template<class Pastix> struct pastix_traits;
+
+  template<typename _MatrixType>
+  struct pastix_traits< PastixLU<_MatrixType> >
+  {
+    typedef _MatrixType MatrixType;
+    typedef typename _MatrixType::Scalar Scalar;
+    typedef typename _MatrixType::RealScalar RealScalar;
+    typedef typename _MatrixType::Index Index;
+  };
+
+  template<typename _MatrixType, int Options>
+  struct pastix_traits< PastixLLT<_MatrixType,Options> >
+  {
+    typedef _MatrixType MatrixType;
+    typedef typename _MatrixType::Scalar Scalar;
+    typedef typename _MatrixType::RealScalar RealScalar;
+    typedef typename _MatrixType::Index Index;
+  };
+
+  template<typename _MatrixType, int Options>
+  struct pastix_traits< PastixLDLT<_MatrixType,Options> >
+  {
+    typedef _MatrixType MatrixType;
+    typedef typename _MatrixType::Scalar Scalar;
+    typedef typename _MatrixType::RealScalar RealScalar;
+    typedef typename _MatrixType::Index Index;
+  };
+  
+  void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, float *vals, int *perm, int * invp, float *x, int nbrhs, int *iparm, double *dparm)
+  {
+    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
+    if (nbrhs == 0) x = NULL;
+    s_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); 
+  }
+  
+  void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, double *vals, int *perm, int * invp, double *x, int nbrhs, int *iparm, double *dparm)
+  {
+    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
+    if (nbrhs == 0) x = NULL;
+    d_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); 
+  }
+  
+  void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<float> *vals, int *perm, int * invp, std::complex<float> *x, int nbrhs, int *iparm, double *dparm)
+  {
+    c_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<COMPLEX*>(vals), perm, invp, reinterpret_cast<COMPLEX*>(x), nbrhs, iparm, dparm); 
+  }
+  
+  void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<double> *vals, int *perm, int * invp, std::complex<double> *x, int nbrhs, int *iparm, double *dparm)
+  {
+    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
+    if (nbrhs == 0) x = NULL;
+    z_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<DCOMPLEX*>(vals), perm, invp, reinterpret_cast<DCOMPLEX*>(x), nbrhs, iparm, dparm); 
+  }
+
+  // Convert the matrix  to Fortran-style Numbering
+  template <typename MatrixType>
+  void EigenToFortranNumbering (MatrixType& mat)
+  {
+    if ( !(mat.outerIndexPtr()[0]) ) 
+    { 
+      int i;
+      for(i = 0; i <= mat.rows(); ++i)
+        ++mat.outerIndexPtr()[i];
+      for(i = 0; i < mat.nonZeros(); ++i)
+        ++mat.innerIndexPtr()[i];
+    }
+  }
+  
+  // Convert to C-style Numbering
+  template <typename MatrixType>
+  void EigenToCNumbering (MatrixType& mat)
+  {
+    // Check the Numbering
+    if ( mat.outerIndexPtr()[0] == 1 ) 
+    { // Convert to C-style numbering
+      int i;
+      for(i = 0; i <= mat.rows(); ++i)
+        --mat.outerIndexPtr()[i];
+      for(i = 0; i < mat.nonZeros(); ++i)
+        --mat.innerIndexPtr()[i];
+    }
+  }
+  
+  // Symmetrize the graph of the input matrix 
+  // In : The Input matrix to symmetrize the pattern
+  // Out : The output matrix
+  // StrMatTrans : The structural pattern of the transpose of In; It is 
+  // used to optimize the future symmetrization with the same matrix pattern
+  // WARNING It is assumed here that successive calls to this routine are done 
+  // with matrices having the same pattern.
+  template <typename MatrixType>
+  void EigenSymmetrizeMatrixGraph (const MatrixType& In, MatrixType& Out, MatrixType& StrMatTrans, bool& hasTranspose)
+  {
+    eigen_assert(In.cols()==In.rows() && " Can only symmetrize the graph of a square matrix");
+    if (!hasTranspose)
+    { //First call to this routine, need to compute the structural pattern of In^T
+      StrMatTrans = In.transpose();
+      // Set the elements of the matrix to zero 
+      for (int i = 0; i < StrMatTrans.rows(); i++) 
+      {
+        for (typename MatrixType::InnerIterator it(StrMatTrans, i); it; ++it)
+          it.valueRef() = 0.0;
+      }
+      hasTranspose = true;
+    }
+    Out = (StrMatTrans + In).eval(); 
+  }
+
+}
+
+// This is the base class to interface with PaStiX functions. 
+// Users should not used this class directly. 
+template <class Derived>
+class PastixBase
+{
+  public:
+    typedef typename internal::pastix_traits<Derived>::MatrixType _MatrixType;
+    typedef _MatrixType MatrixType;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::Index Index;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    
+  public:
+    
+    PastixBase():m_initisOk(false),m_analysisIsOk(false),m_factorizationIsOk(false),m_isInitialized(false) 
+    {
+      m_pastixdata = 0;
+      m_hasTranspose = false;
+      PastixInit();
+    }
+    
+    ~PastixBase() 
+    {
+       PastixDestroy();
+    }
+    
+    // Initialize the Pastix data structure, check the matrix
+    void PastixInit(); 
+    
+    // Compute the ordering and the symbolic factorization
+    Derived& analyzePattern (MatrixType& mat);
+    
+    // Compute the numerical factorization
+    Derived& factorize (MatrixType& mat);
+
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const internal::solve_retval<PastixBase, Rhs>
+    solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "Pastix solver is not initialized.");
+      eigen_assert(rows()==b.rows()
+                && "PastixBase::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::solve_retval<PastixBase, Rhs>(*this, b.derived());
+    }
+    
+    template<typename Rhs,typename Dest>
+    bool _solve (const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const;
+    
+    /** \internal */
+    template<typename Rhs, typename DestScalar, int DestOptions, typename DestIndex>
+    void _solve_sparse(const Rhs& b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
+    {
+      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
+      eigen_assert(rows()==b.rows());
+      
+      // we process the sparse rhs per block of NbColsAtOnce columns temporarily stored into a dense matrix.
+      static const int NbColsAtOnce = 1;
+      int rhsCols = b.cols();
+      int size = b.rows();
+      Eigen::Matrix<DestScalar,Dynamic,Dynamic> tmp(size,rhsCols);
+      for(int k=0; k<rhsCols; k+=NbColsAtOnce)
+      {
+        int actualCols = std::min<int>(rhsCols-k, NbColsAtOnce);
+        tmp.leftCols(actualCols) = b.middleCols(k,actualCols);
+        tmp.leftCols(actualCols) = derived().solve(tmp.leftCols(actualCols));
+        dest.middleCols(k,actualCols) = tmp.leftCols(actualCols).sparseView();
+      }
+    }
+    
+    Derived& derived()
+    {
+      return *static_cast<Derived*>(this);
+    }
+    const Derived& derived() const
+    {
+      return *static_cast<const Derived*>(this);
+    }
+
+    /** Returns a reference to the integer vector IPARM of PaStiX parameters
+      * to modify the default parameters. 
+      * The statistics related to the different phases of factorization and solve are saved here as well
+      * \sa analyzePattern() factorize()
+      */
+    Array<Index,IPARM_SIZE,1>& iparm()
+    {
+      return m_iparm; 
+    }
+    
+    /** Return a reference to a particular index parameter of the IPARM vector 
+     * \sa iparm()
+     */
+    
+    int& iparm(int idxparam)
+    {
+      return m_iparm(idxparam);
+    }
+    
+     /** Returns a reference to the double vector DPARM of PaStiX parameters 
+      * The statistics related to the different phases of factorization and solve are saved here as well
+      * \sa analyzePattern() factorize()
+      */
+    Array<RealScalar,IPARM_SIZE,1>& dparm()
+    {
+      return m_dparm; 
+    }
+    
+    
+    /** Return a reference to a particular index parameter of the DPARM vector 
+     * \sa dparm()
+     */
+    
+    double& dparm(int idxparam)
+    {
+      return m_dparm(idxparam);
+    }
+    
+    inline Index cols() const { return m_size; }
+    inline Index rows() const { return m_size; }
+    
+     /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was succesful,
+      *          \c NumericalIssue if the PaStiX reports a problem
+      *          \c InvalidInput if the input matrix is invalid
+      *
+      * \sa iparm()          
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+    
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const internal::sparse_solve_retval<PastixBase, Rhs>
+    solve(const SparseMatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "Pastix LU, LLT or LDLT is not initialized.");
+      eigen_assert(rows()==b.rows()
+                && "PastixBase::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::sparse_solve_retval<PastixBase, Rhs>(*this, b.derived());
+    }
+    
+  protected:
+    // Free all the data allocated by Pastix
+    void PastixDestroy()
+    {
+      eigen_assert(m_initisOk && "The Pastix structure should be allocated first"); 
+      m_iparm(IPARM_START_TASK) = API_TASK_CLEAN;
+      m_iparm(IPARM_END_TASK) = API_TASK_CLEAN;
+      internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, m_mat_null.outerIndexPtr(), m_mat_null.innerIndexPtr(),
+               m_mat_null.valuePtr(), m_perm.data(), m_invp.data(), m_vec_null.data(), 1, m_iparm.data(), m_dparm.data());
+    }
+    
+    Derived& compute (MatrixType& mat);
+    
+    int m_initisOk; 
+    int m_analysisIsOk;
+    int m_factorizationIsOk;
+    bool m_isInitialized;
+    mutable ComputationInfo m_info; 
+    mutable pastix_data_t *m_pastixdata; // Data structure for pastix
+    mutable SparseMatrix<Scalar, ColMajor> m_mat_null; // An input  null matrix
+    mutable Matrix<Scalar, Dynamic,1> m_vec_null; // An input null vector
+    mutable SparseMatrix<Scalar, ColMajor> m_StrMatTrans; // The transpose pattern of the input matrix
+    mutable bool m_hasTranspose; // The transpose of the current matrix has already been computed
+    mutable int m_comm; // The MPI communicator identifier
+    mutable Matrix<Index,IPARM_SIZE,1> m_iparm; // integer vector for the input parameters
+    mutable Matrix<double,DPARM_SIZE,1> m_dparm; // Scalar vector for the input parameters
+    mutable Matrix<Index,Dynamic,1> m_perm;  // Permutation vector
+    mutable Matrix<Index,Dynamic,1> m_invp;  // Inverse permutation vector
+    mutable int m_ordering; // ordering method to use
+    mutable int m_amalgamation; // level of amalgamation
+    mutable int m_size; // Size of the matrix 
+    
+  private:
+    PastixBase(PastixBase& ) {}
+    
+}; 
+
+ /** Initialize the PaStiX data structure. 
+   *A first call to this function fills iparm and dparm with the default PaStiX parameters
+   * \sa iparm() dparm()
+   */
+template <class Derived>
+void PastixBase<Derived>::PastixInit()
+{
+  m_size = 0; 
+  m_iparm.resize(IPARM_SIZE);
+  m_dparm.resize(DPARM_SIZE);
+  
+  m_iparm(IPARM_MODIFY_PARAMETER) = API_NO;
+  if(m_pastixdata)
+  { // This trick is used to reset the Pastix internal data between successive
+    // calls with (structural) different matrices
+    PastixDestroy();
+    m_pastixdata = 0;
+    m_iparm(IPARM_MODIFY_PARAMETER) = API_YES;
+    m_hasTranspose = false; 
+  }
+  
+  m_iparm(IPARM_START_TASK) = API_TASK_INIT;
+  m_iparm(IPARM_END_TASK) = API_TASK_INIT;
+  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
+  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, m_mat_null.outerIndexPtr(), m_mat_null.innerIndexPtr(),
+               m_mat_null.valuePtr(), m_perm.data(), m_invp.data(), m_vec_null.data(), 1, m_iparm.data(), m_dparm.data());
+  
+  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
+  
+  // Check the returned error
+  if(m_iparm(IPARM_ERROR_NUMBER)) {
+    m_info = InvalidInput;
+    m_initisOk = false;
+  }
+  else { 
+    m_info = Success;
+    m_initisOk = true;
+  }
+}
+
+template <class Derived>
+Derived& PastixBase<Derived>::compute(MatrixType& mat)
+{
+  eigen_assert(mat.rows() == mat.cols() && "The input matrix should be squared");
+  typedef typename MatrixType::Scalar Scalar;
+  
+  // Save the size of the current matrix 
+  m_size = mat.rows();
+  // Convert the matrix in fortran-style numbering 
+  internal::EigenToFortranNumbering(mat);
+  analyzePattern(mat);
+  factorize(mat);
+  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
+  if (m_factorizationIsOk) m_isInitialized = true;
+  
+  //Convert back the matrix -- Is it really necessary here 
+  internal::EigenToCNumbering(mat);
+  
+  return derived();
+}
+
+
+template <class Derived>
+Derived& PastixBase<Derived>::analyzePattern(MatrixType& mat)
+{
+  eigen_assert(m_initisOk && "PastixInit should be called first to set the default  parameters");
+  m_size = mat.rows();
+  m_perm.resize(m_size);
+  m_invp.resize(m_size);
+  
+  // Convert the matrix in fortran-style numbering 
+  internal::EigenToFortranNumbering(mat);
+  
+  m_iparm(IPARM_START_TASK) = API_TASK_ORDERING;
+  m_iparm(IPARM_END_TASK) = API_TASK_ANALYSE;
+  
+  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
+               mat.valuePtr(), m_perm.data(), m_invp.data(), m_vec_null.data(), 0, m_iparm.data(), m_dparm.data());
+  
+  // Check the returned error
+  if(m_iparm(IPARM_ERROR_NUMBER)) {
+    m_info = NumericalIssue;
+    m_analysisIsOk = false;
+  }
+  else { 
+    m_info = Success;
+    m_analysisIsOk = true;
+  }
+  return derived();
+}
+
+template <class Derived>
+Derived& PastixBase<Derived>::factorize(MatrixType& mat)
+{
+  eigen_assert(m_analysisIsOk && "The analysis phase should be called before the factorization phase");
+  m_iparm(IPARM_START_TASK) = API_TASK_NUMFACT;
+  m_iparm(IPARM_END_TASK) = API_TASK_NUMFACT;
+  m_size = mat.rows();
+  
+  // Convert the matrix in fortran-style numbering 
+  internal::EigenToFortranNumbering(mat);
+  
+  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
+               mat.valuePtr(), m_perm.data(), m_invp.data(), m_vec_null.data(), 0, m_iparm.data(), m_dparm.data());
+  
+  // Check the returned error
+  if(m_iparm(IPARM_ERROR_NUMBER)) {
+    m_info = NumericalIssue;
+    m_factorizationIsOk = false;
+    m_isInitialized = false;
+  }
+  else { 
+    m_info = Success;
+    m_factorizationIsOk = true;
+    m_isInitialized = true;
+  }
+  return derived();
+}
+
+/* Solve the system */
+template<typename Base>
+template<typename Rhs,typename Dest>
+bool PastixBase<Base>::_solve (const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const
+{
+  eigen_assert(m_isInitialized && "The matrix should be factorized first");
+  EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
+                     THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  int rhs = 1;
+  
+  x = b; /* on return, x is overwritten by the computed solution */
+  
+  for (int i = 0; i < b.cols(); i++){
+    m_iparm(IPARM_START_TASK) = API_TASK_SOLVE;
+    m_iparm(IPARM_END_TASK) = API_TASK_REFINE;
+    m_iparm(IPARM_RHS_MAKING) = API_RHS_B;
+    internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, x.rows(), m_mat_null.outerIndexPtr(), m_mat_null.innerIndexPtr(),
+               m_mat_null.valuePtr(), m_perm.data(), m_invp.data(), &x(0, i), rhs, m_iparm.data(), m_dparm.data());
+  }
+  // Check the returned error
+  if(m_iparm(IPARM_ERROR_NUMBER)) {
+    m_info = NumericalIssue; 
+    return false;
+  }
+  else {
+    return true;
+  }
+}
+
+/** \ingroup PaStiXSupport_Module
+  * \class PastixLU
+  * \brief Sparse direct LU solver based on PaStiX library
+  * 
+  * This class is used to solve the linear systems A.X = B with a supernodal LU 
+  * factorization in the PaStiX library. The matrix A should be squared and nonsingular
+  * PaStiX requires that the matrix A has a symmetric structural pattern. 
+  * This interface can symmetrize the input matrix otherwise. 
+  * The vectors or matrices X and B can be either dense or sparse.
+  * 
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam IsStrSym Indicates if the input matrix has a symmetric pattern, default is false
+  * NOTE : Note that if the analysis and factorization phase are called separately, 
+  * the input matrix will be symmetrized at each call, hence it is advised to 
+  * symmetrize the matrix in a end-user program and set \p IsStrSym to true
+  * 
+  * \sa \ref TutorialSparseDirectSolvers
+  * 
+  */
+template<typename _MatrixType, bool IsStrSym>
+class PastixLU : public PastixBase< PastixLU<_MatrixType> >
+{
+  public:
+    typedef _MatrixType MatrixType;
+    typedef PastixBase<PastixLU<MatrixType> > Base;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef SparseMatrix<Scalar, ColMajor> PaStiXType; 
+    
+  public:
+    PastixLU():Base() {}
+    
+    PastixLU(const MatrixType& matrix):Base()
+    {
+      compute(matrix);
+    }
+    /** Compute the LU supernodal factorization of \p matrix. 
+      * iparm and dparm can be used to tune the PaStiX parameters. 
+      * see the PaStiX user's manual
+      * \sa analyzePattern() factorize()
+      */
+    void compute (const MatrixType& matrix)
+    {
+      // Pastix supports only column-major matrices with a symmetric pattern
+      Base::PastixInit(); 
+      PaStiXType temp(matrix.rows(), matrix.cols());
+      // Symmetrize the graph of the matrix
+      if (IsStrSym)   
+        temp = matrix;
+      else 
+      { 
+        internal::EigenSymmetrizeMatrixGraph<PaStiXType>(matrix, temp, m_StrMatTrans, m_hasTranspose);
+      }
+      m_iparm[IPARM_SYM] = API_SYM_NO;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LU;
+      Base::compute(temp);
+    }
+    /** Compute the LU symbolic factorization of \p matrix using its sparsity pattern. 
+      * Several ordering methods can be used at this step. See the PaStiX user's manual. 
+      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    {
+      
+      Base::PastixInit(); 
+      /* Pastix supports only column-major matrices with symmetrized patterns */
+      SparseMatrix<Scalar, ColMajor> temp(matrix.rows(), matrix.cols());
+      // Symmetrize the graph of the matrix
+      if (IsStrSym)   
+        temp = matrix;
+      else 
+      { 
+        internal::EigenSymmetrizeMatrixGraph<PaStiXType>(matrix, temp, m_StrMatTrans,m_hasTranspose);
+      }
+      
+      m_iparm(IPARM_SYM) = API_SYM_NO;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LU;
+      Base::analyzePattern(temp);
+    }
+
+    /** Compute the LU supernodal factorization of \p matrix
+      * WARNING The matrix \p matrix should have the same structural pattern 
+      * as the same used in the analysis phase.
+      * \sa analyzePattern()
+      */ 
+    void factorize(const MatrixType& matrix)
+    {
+      /* Pastix supports only column-major matrices with symmetrized patterns */
+      SparseMatrix<Scalar, ColMajor> temp(matrix.rows(), matrix.cols());
+      // Symmetrize the graph of the matrix
+      if (IsStrSym)   
+        temp = matrix;
+      else 
+      { 
+        internal::EigenSymmetrizeMatrixGraph<PaStiXType>(matrix, temp, m_StrMatTrans,m_hasTranspose);
+      }
+      m_iparm(IPARM_SYM) = API_SYM_NO;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LU;
+      Base::factorize(temp);
+    }
+  protected:
+    using Base::m_iparm;
+    using Base::m_dparm;
+    using Base::m_StrMatTrans;
+    using Base::m_hasTranspose;
+    
+  private:
+    PastixLU(PastixLU& ) {}
+};
+
+/** \ingroup PaStiXSupport_Module
+  * \class PastixLLT
+  * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
+  * 
+  * This class is used to solve the linear systems A.X = B via a LL^T supernodal Cholesky factorization
+  * available in the PaStiX library. The matrix A should be symmetric and positive definite
+  * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
+  * The vectors or matrices X and B can be either dense or sparse
+  * 
+  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
+  * 
+  * \sa \ref TutorialSparseDirectSolvers
+  */
+template<typename _MatrixType, int _UpLo>
+class PastixLLT : public PastixBase< PastixLLT<_MatrixType, _UpLo> >
+{
+  public:
+    typedef _MatrixType MatrixType;
+    typedef PastixBase<PastixLLT<MatrixType, _UpLo> > Base;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::Index Index;
+    
+  public:
+    enum { UpLo = _UpLo };
+    PastixLLT():Base() {}
+    
+    PastixLLT(const MatrixType& matrix):Base()
+    {
+      compute(matrix);
+    }
+
+    /** Compute the L factor of the LL^T supernodal factorization of \p matrix 
+      * \sa analyzePattern() factorize()
+      */
+    void compute (const MatrixType& matrix)
+    {
+      // Pastix supports only lower, column-major matrices
+      Base::PastixInit(); // This is necessary to let PaStiX initialize its data structure between successive calls to compute
+      SparseMatrix<Scalar, ColMajor> temp(matrix.rows(), matrix.cols());
+      PermutationMatrix<Dynamic,Dynamic,Index> pnull;
+      temp.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>().twistedBy(pnull);
+      m_iparm(IPARM_SYM) = API_SYM_YES;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LLT;
+      Base::compute(temp);
+    }
+
+     /** Compute the LL^T symbolic factorization of \p matrix using its sparsity pattern
+      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    {
+      Base::PastixInit(); 
+      // Pastix supports only lower, column-major matrices
+      SparseMatrix<Scalar, ColMajor> temp(matrix.rows(), matrix.cols());
+      PermutationMatrix<Dynamic,Dynamic,Index> pnull;
+      temp.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>().twistedBy(pnull);
+      m_iparm(IPARM_SYM) = API_SYM_YES;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LLT;
+      Base::analyzePattern(temp);
+    }
+      /** Compute the LL^T supernodal numerical factorization of \p matrix 
+        * \sa analyzePattern()
+        */
+    void factorize(const MatrixType& matrix)
+    {
+      // Pastix supports only lower, column-major matrices 
+      SparseMatrix<Scalar, ColMajor> temp(matrix.rows(), matrix.cols());
+      PermutationMatrix<Dynamic,Dynamic,Index> pnull;
+      temp.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>().twistedBy(pnull);
+      m_iparm(IPARM_SYM) = API_SYM_YES;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LLT;
+      Base::factorize(temp);
+    }
+  protected:
+    using Base::m_iparm;
+    
+  private:
+    PastixLLT(PastixLLT& ) {}
+};
+
+/** \ingroup PaStiXSupport_Module
+  * \class PastixLDLT
+  * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
+  * 
+  * This class is used to solve the linear systems A.X = B via a LDL^T supernodal Cholesky factorization
+  * available in the PaStiX library. The matrix A should be symmetric and positive definite
+  * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
+  * The vectors or matrices X and B can be either dense or sparse
+  * 
+  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
+  * 
+  * \sa \ref TutorialSparseDirectSolvers
+  */
+template<typename _MatrixType, int _UpLo>
+class PastixLDLT : public PastixBase< PastixLDLT<_MatrixType, _UpLo> >
+{
+public:
+    typedef _MatrixType MatrixType;
+    typedef PastixBase<PastixLDLT<MatrixType, _UpLo> > Base; 
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::Index Index;
+    
+  public:
+    enum { UpLo = _UpLo };
+    PastixLDLT():Base() {}
+    
+    PastixLDLT(const MatrixType& matrix):Base()
+    {
+      compute(matrix);
+    }
+
+    /** Compute the L and D factors of the LDL^T factorization of \p matrix 
+      * \sa analyzePattern() factorize()
+      */
+    void compute (const MatrixType& matrix)
+    {
+      Base::PastixInit();
+      // Pastix supports only lower, column-major matrices
+      SparseMatrix<Scalar, ColMajor> temp(matrix.rows(), matrix.cols());
+      PermutationMatrix<Dynamic,Dynamic,Index> pnull;
+      temp.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>().twistedBy(pnull);
+      m_iparm(IPARM_SYM) = API_SYM_YES;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LDLT;
+      Base::compute(temp);
+    }
+
+    /** Compute the LDL^T symbolic factorization of \p matrix using its sparsity pattern
+      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    { 
+      Base::PastixInit();
+      // Pastix supports only lower, column-major matrices
+      SparseMatrix<Scalar, ColMajor> temp(matrix.rows(), matrix.cols());
+      PermutationMatrix<Dynamic,Dynamic,Index> pnull;
+      temp.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>().twistedBy(pnull);
+    
+      m_iparm(IPARM_SYM) = API_SYM_YES;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LDLT;
+      Base::analyzePattern(temp);
+    }
+    /** Compute the LDL^T supernodal numerical factorization of \p matrix 
+      * 
+      */
+    void factorize(const MatrixType& matrix)
+    {
+      // Pastix supports only lower, column-major matrices
+      SparseMatrix<Scalar, ColMajor> temp(matrix.rows(), matrix.cols());
+      PermutationMatrix<Dynamic,Dynamic,Index> pnull;
+      temp.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>().twistedBy(pnull);
+
+      m_iparm(IPARM_SYM) = API_SYM_YES;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LDLT;
+      Base::factorize(temp);
+    }
+
+  protected:
+    using Base::m_iparm;
+    
+  private:
+    PastixLDLT(PastixLDLT& ) {}
+};
+
+namespace internal {
+
+template<typename _MatrixType, typename Rhs>
+struct solve_retval<PastixBase<_MatrixType>, Rhs>
+  : solve_retval_base<PastixBase<_MatrixType>, Rhs>
+{
+  typedef PastixBase<_MatrixType> Dec;
+  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve(rhs(),dst);
+  }
+};
+
+template<typename _MatrixType, typename Rhs>
+struct sparse_solve_retval<PastixBase<_MatrixType>, Rhs>
+  : sparse_solve_retval_base<PastixBase<_MatrixType>, Rhs>
+{
+  typedef PastixBase<_MatrixType> Dec;
+  EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve_sparse(rhs(),dst);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif
diff --git a/extern/Eigen3/Eigen/src/PardisoSupport/CMakeLists.txt b/extern/Eigen3/Eigen/src/PardisoSupport/CMakeLists.txt
new file mode 100644
index 00000000000..a097ab40192
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/PardisoSupport/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_PardisoSupport_SRCS "*.h")
+
+INSTALL(FILES 
+  ${Eigen_PardisoSupport_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/PardisoSupport COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/PardisoSupport/PardisoSupport.h b/extern/Eigen3/Eigen/src/PardisoSupport/PardisoSupport.h
new file mode 100644
index 00000000000..e6defc8c39e
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/PardisoSupport/PardisoSupport.h
@@ -0,0 +1,614 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL PARDISO
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_PARDISOSUPPORT_H
+#define EIGEN_PARDISOSUPPORT_H
+
+namespace Eigen { 
+
+template<typename _MatrixType> class PardisoLU;
+template<typename _MatrixType, int Options=Upper> class PardisoLLT;
+template<typename _MatrixType, int Options=Upper> class PardisoLDLT;
+
+namespace internal
+{
+  template<typename Index>
+  struct pardiso_run_selector
+  {
+    static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a,
+                      Index *ia, Index *ja, Index *perm, Index nrhs, Index *iparm, Index msglvl, void *b, void *x)
+    {
+      Index error = 0;
+      ::pardiso(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
+      return error;
+    }
+  };
+  template<>
+  struct pardiso_run_selector<long long int>
+  {
+    typedef long long int Index;
+    static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a,
+                      Index *ia, Index *ja, Index *perm, Index nrhs, Index *iparm, Index msglvl, void *b, void *x)
+    {
+      Index error = 0;
+      ::pardiso_64(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
+      return error;
+    }
+  };
+
+  template<class Pardiso> struct pardiso_traits;
+
+  template<typename _MatrixType>
+  struct pardiso_traits< PardisoLU<_MatrixType> >
+  {
+    typedef _MatrixType MatrixType;
+    typedef typename _MatrixType::Scalar Scalar;
+    typedef typename _MatrixType::RealScalar RealScalar;
+    typedef typename _MatrixType::Index Index;
+  };
+
+  template<typename _MatrixType, int Options>
+  struct pardiso_traits< PardisoLLT<_MatrixType, Options> >
+  {
+    typedef _MatrixType MatrixType;
+    typedef typename _MatrixType::Scalar Scalar;
+    typedef typename _MatrixType::RealScalar RealScalar;
+    typedef typename _MatrixType::Index Index;
+  };
+
+  template<typename _MatrixType, int Options>
+  struct pardiso_traits< PardisoLDLT<_MatrixType, Options> >
+  {
+    typedef _MatrixType MatrixType;
+    typedef typename _MatrixType::Scalar Scalar;
+    typedef typename _MatrixType::RealScalar RealScalar;
+    typedef typename _MatrixType::Index Index;    
+  };
+
+}
+
+template<class Derived>
+class PardisoImpl
+{
+    typedef internal::pardiso_traits<Derived> Traits;
+  public:
+    typedef typename Traits::MatrixType MatrixType;
+    typedef typename Traits::Scalar Scalar;
+    typedef typename Traits::RealScalar RealScalar;
+    typedef typename Traits::Index Index;
+    typedef SparseMatrix<Scalar,RowMajor,Index> SparseMatrixType;
+    typedef Matrix<Scalar,Dynamic,1> VectorType;
+    typedef Matrix<Index, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+    typedef Matrix<Index, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
+    enum {
+      ScalarIsComplex = NumTraits<Scalar>::IsComplex
+    };
+
+    PardisoImpl()
+    {
+      eigen_assert((sizeof(Index) >= sizeof(_INTEGER_t) && sizeof(Index) <= 8) && "Non-supported index type");
+      m_iparm.setZero();
+      m_msglvl = 0; // No output
+      m_initialized = false;
+    }
+
+    ~PardisoImpl()
+    {
+      pardisoRelease();
+    }
+
+    inline Index cols() const { return m_size; }
+    inline Index rows() const { return m_size; }
+  
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was succesful,
+      *          \c NumericalIssue if the matrix appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_initialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+
+    /** \warning for advanced usage only.
+      * \returns a reference to the parameter array controlling PARDISO.
+      * See the PARDISO manual to know how to use it. */
+    Array<Index,64,1>& pardisoParameterArray()
+    {
+      return m_iparm;
+    }
+    
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      * 
+      * \sa factorize()
+      */
+    Derived& analyzePattern(const MatrixType& matrix);
+    
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    Derived& factorize(const MatrixType& matrix);
+
+    Derived& compute(const MatrixType& matrix);
+    
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const internal::solve_retval<PardisoImpl, Rhs>
+    solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_initialized && "Pardiso solver is not initialized.");
+      eigen_assert(rows()==b.rows()
+                && "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::solve_retval<PardisoImpl, Rhs>(*this, b.derived());
+    }
+
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const internal::sparse_solve_retval<PardisoImpl, Rhs>
+    solve(const SparseMatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_initialized && "Pardiso solver is not initialized.");
+      eigen_assert(rows()==b.rows()
+                && "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::sparse_solve_retval<PardisoImpl, Rhs>(*this, b.derived());
+    }
+
+    Derived& derived()
+    {
+      return *static_cast<Derived*>(this);
+    }
+    const Derived& derived() const
+    {
+      return *static_cast<const Derived*>(this);
+    }
+
+    template<typename BDerived, typename XDerived>
+    bool _solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const;
+
+    /** \internal */
+    template<typename Rhs, typename DestScalar, int DestOptions, typename DestIndex>
+    void _solve_sparse(const Rhs& b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
+    {
+      eigen_assert(m_size==b.rows());
+
+      // we process the sparse rhs per block of NbColsAtOnce columns temporarily stored into a dense matrix.
+      static const int NbColsAtOnce = 4;
+      int rhsCols = b.cols();
+      int size = b.rows();
+      // Pardiso cannot solve in-place,
+      // so we need two temporaries
+      Eigen::Matrix<DestScalar,Dynamic,Dynamic,ColMajor> tmp_rhs(size,rhsCols);
+      Eigen::Matrix<DestScalar,Dynamic,Dynamic,ColMajor> tmp_res(size,rhsCols);
+      for(int k=0; k<rhsCols; k+=NbColsAtOnce)
+      {
+        int actualCols = std::min<int>(rhsCols-k, NbColsAtOnce);
+        tmp_rhs.leftCols(actualCols) = b.middleCols(k,actualCols);
+        tmp_res.leftCols(actualCols) = derived().solve(tmp_rhs.leftCols(actualCols));
+        dest.middleCols(k,actualCols) = tmp_res.leftCols(actualCols).sparseView();
+      }
+    }
+
+  protected:
+    void pardisoRelease()
+    {
+      if(m_initialized) // Factorization ran at least once
+      {
+        internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, -1, m_size, 0, 0, 0, m_perm.data(), 0,
+                                                   m_iparm.data(), m_msglvl, 0, 0);
+      }
+    }
+
+    void pardisoInit(int type)
+    {
+      m_type = type;
+      bool symmetric = abs(m_type) < 10;
+      m_iparm[0] = 1;   // No solver default
+      m_iparm[1] = 3;   // use Metis for the ordering
+      m_iparm[2] = 1;   // Numbers of processors, value of OMP_NUM_THREADS
+      m_iparm[3] = 0;   // No iterative-direct algorithm
+      m_iparm[4] = 0;   // No user fill-in reducing permutation
+      m_iparm[5] = 0;   // Write solution into x
+      m_iparm[6] = 0;   // Not in use
+      m_iparm[7] = 2;   // Max numbers of iterative refinement steps
+      m_iparm[8] = 0;   // Not in use
+      m_iparm[9] = 13;  // Perturb the pivot elements with 1E-13
+      m_iparm[10] = symmetric ? 0 : 1; // Use nonsymmetric permutation and scaling MPS
+      m_iparm[11] = 0;  // Not in use
+      m_iparm[12] = symmetric ? 0 : 1;  // Maximum weighted matching algorithm is switched-off (default for symmetric).
+                                        // Try m_iparm[12] = 1 in case of inappropriate accuracy
+      m_iparm[13] = 0;  // Output: Number of perturbed pivots
+      m_iparm[14] = 0;  // Not in use
+      m_iparm[15] = 0;  // Not in use
+      m_iparm[16] = 0;  // Not in use
+      m_iparm[17] = -1; // Output: Number of nonzeros in the factor LU
+      m_iparm[18] = -1; // Output: Mflops for LU factorization
+      m_iparm[19] = 0;  // Output: Numbers of CG Iterations
+      
+      m_iparm[20] = 0;  // 1x1 pivoting
+      m_iparm[26] = 0;  // No matrix checker
+      m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0;
+      m_iparm[34] = 1;  // C indexing
+      m_iparm[59] = 1;  // Automatic switch between In-Core and Out-of-Core modes
+    }
+
+  protected:
+    // cached data to reduce reallocation, etc.
+    
+    void manageErrorCode(Index error)
+    {
+      switch(error)
+      {
+        case 0:
+          m_info = Success;
+          break;
+        case -4:
+        case -7:
+          m_info = NumericalIssue;
+          break;
+        default:
+          m_info = InvalidInput;
+      }
+    }
+
+    mutable SparseMatrixType m_matrix;
+    ComputationInfo m_info;
+    bool m_initialized, m_analysisIsOk, m_factorizationIsOk;
+    Index m_type, m_msglvl;
+    mutable void *m_pt[64];
+    mutable Array<Index,64,1> m_iparm;
+    mutable IntColVectorType m_perm;
+    Index m_size;
+    
+  private:
+    PardisoImpl(PardisoImpl &) {}
+};
+
+template<class Derived>
+Derived& PardisoImpl<Derived>::compute(const MatrixType& a)
+{
+  m_size = a.rows();
+  eigen_assert(a.rows() == a.cols());
+
+  pardisoRelease();
+  memset(m_pt, 0, sizeof(m_pt));
+  m_perm.setZero(m_size);
+  derived().getMatrix(a);
+  
+  Index error;
+  error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 12, m_size,
+                                                     m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
+                                                     m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
+
+  manageErrorCode(error);
+  m_analysisIsOk = true;
+  m_factorizationIsOk = true;
+  m_initialized = true;
+  return derived();
+}
+
+template<class Derived>
+Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a)
+{
+  m_size = a.rows();
+  eigen_assert(m_size == a.cols());
+
+  pardisoRelease();
+  memset(m_pt, 0, sizeof(m_pt));
+  m_perm.setZero(m_size);
+  derived().getMatrix(a);
+  
+  Index error;
+  error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 11, m_size,
+                                                     m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
+                                                     m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
+  
+  manageErrorCode(error);
+  m_analysisIsOk = true;
+  m_factorizationIsOk = false;
+  m_initialized = true;
+  return derived();
+}
+
+template<class Derived>
+Derived& PardisoImpl<Derived>::factorize(const MatrixType& a)
+{
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  eigen_assert(m_size == a.rows() && m_size == a.cols());
+  
+  derived().getMatrix(a);
+
+  Index error;  
+  error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 22, m_size,
+                                                     m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
+                                                     m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
+  
+  manageErrorCode(error);
+  m_factorizationIsOk = true;
+  return derived();
+}
+
+template<class Base>
+template<typename BDerived,typename XDerived>
+bool PardisoImpl<Base>::_solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const
+{
+  if(m_iparm[0] == 0) // Factorization was not computed
+    return false;
+
+  //Index n = m_matrix.rows();
+  Index nrhs = Index(b.cols());
+  eigen_assert(m_size==b.rows());
+  eigen_assert(((MatrixBase<BDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major right hand sides are not supported");
+  eigen_assert(((MatrixBase<XDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major matrices of unknowns are not supported");
+  eigen_assert(((nrhs == 1) || b.outerStride() == b.rows()));
+
+
+//  switch (transposed) {
+//    case SvNoTrans    : m_iparm[11] = 0 ; break;
+//    case SvTranspose  : m_iparm[11] = 2 ; break;
+//    case SvAdjoint    : m_iparm[11] = 1 ; break;
+//    default:
+//      //std::cerr << "Eigen: transposition  option \"" << transposed << "\" not supported by the PARDISO backend\n";
+//      m_iparm[11] = 0;
+//  }
+
+  Scalar* rhs_ptr = const_cast<Scalar*>(b.derived().data());
+  Matrix<Scalar,Dynamic,Dynamic,ColMajor> tmp;
+  
+  // Pardiso cannot solve in-place
+  if(rhs_ptr == x.derived().data())
+  {
+    tmp = b;
+    rhs_ptr = tmp.data();
+  }
+  
+  Index error;
+  error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 33, m_size,
+                                                     m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
+                                                     m_perm.data(), nrhs, m_iparm.data(), m_msglvl,
+                                                     rhs_ptr, x.derived().data());
+
+  return error==0;
+}
+
+
+/** \ingroup PardisoSupport_Module
+  * \class PardisoLU
+  * \brief A sparse direct LU factorization and solver based on the PARDISO library
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
+  * using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible.
+  * The vectors or matrices X and B can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \sa \ref TutorialSparseDirectSolvers
+  */
+template<typename MatrixType>
+class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
+{
+  protected:
+    typedef PardisoImpl< PardisoLU<MatrixType> > Base;
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::RealScalar RealScalar;
+    using Base::pardisoInit;
+    using Base::m_matrix;
+    friend class PardisoImpl< PardisoLU<MatrixType> >;
+
+  public:
+
+    using Base::compute;
+    using Base::solve;
+
+    PardisoLU()
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? 13 : 11);
+    }
+
+    PardisoLU(const MatrixType& matrix)
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? 13 : 11);
+      compute(matrix);
+    }
+  protected:
+    void getMatrix(const MatrixType& matrix)
+    {
+      m_matrix = matrix;
+    }
+    
+  private:
+    PardisoLU(PardisoLU& ) {}
+};
+
+/** \ingroup PardisoSupport_Module
+  * \class PardisoLLT
+  * \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization
+  * using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
+  * The vectors or matrices X and B can be either dense or sparse.
+  *
+  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular part has to be used.
+  *         Upper|Lower can be used to tell both triangular parts can be used as input.
+  *
+  * \sa \ref TutorialSparseDirectSolvers
+  */
+template<typename MatrixType, int _UpLo>
+class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,_UpLo> >
+{
+  protected:
+    typedef PardisoImpl< PardisoLLT<MatrixType,_UpLo> > Base;
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::Index Index;
+    typedef typename Base::RealScalar RealScalar;
+    using Base::pardisoInit;
+    using Base::m_matrix;
+    friend class PardisoImpl< PardisoLLT<MatrixType,_UpLo> >;
+
+  public:
+
+    enum { UpLo = _UpLo };
+    using Base::compute;
+    using Base::solve;
+
+    PardisoLLT()
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? 4 : 2);
+    }
+
+    PardisoLLT(const MatrixType& matrix)
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? 4 : 2);
+      compute(matrix);
+    }
+    
+  protected:
+    
+    void getMatrix(const MatrixType& matrix)
+    {
+      // PARDISO supports only upper, row-major matrices
+      PermutationMatrix<Dynamic,Dynamic,Index> p_null;
+      m_matrix.resize(matrix.rows(), matrix.cols());
+      m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
+    }
+    
+  private:
+    PardisoLLT(PardisoLLT& ) {}
+};
+
+/** \ingroup PardisoSupport_Module
+  * \class PardisoLDLT
+  * \brief A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization
+  * using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite.
+  * For complex matrices, A can also be symmetric only, see the \a Options template parameter.
+  * The vectors or matrices X and B can be either dense or sparse.
+  *
+  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used.
+  *         Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix.
+  *         Upper|Lower can be used to tell both triangular parts can be used as input.
+  *
+  * \sa \ref TutorialSparseDirectSolvers
+  */
+template<typename MatrixType, int Options>
+class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType,Options> >
+{
+  protected:
+    typedef PardisoImpl< PardisoLDLT<MatrixType,Options> > Base;
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::Index Index;
+    typedef typename Base::RealScalar RealScalar;
+    using Base::pardisoInit;
+    using Base::m_matrix;
+    friend class PardisoImpl< PardisoLDLT<MatrixType,Options> >;
+
+  public:
+
+    using Base::compute;
+    using Base::solve;
+    enum { UpLo = Options&(Upper|Lower) };
+
+    PardisoLDLT()
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
+    }
+
+    PardisoLDLT(const MatrixType& matrix)
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
+      compute(matrix);
+    }
+    
+    void getMatrix(const MatrixType& matrix)
+    {
+      // PARDISO supports only upper, row-major matrices
+      PermutationMatrix<Dynamic,Dynamic,Index> p_null;
+      m_matrix.resize(matrix.rows(), matrix.cols());
+      m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
+    }
+    
+  private:
+    PardisoLDLT(PardisoLDLT& ) {}
+};
+
+namespace internal {
+  
+template<typename _Derived, typename Rhs>
+struct solve_retval<PardisoImpl<_Derived>, Rhs>
+  : solve_retval_base<PardisoImpl<_Derived>, Rhs>
+{
+  typedef PardisoImpl<_Derived> Dec;
+  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve(rhs(),dst);
+  }
+};
+
+template<typename Derived, typename Rhs>
+struct sparse_solve_retval<PardisoImpl<Derived>, Rhs>
+  : sparse_solve_retval_base<PardisoImpl<Derived>, Rhs>
+{
+  typedef PardisoImpl<Derived> Dec;
+  EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec().derived()._solve_sparse(rhs(),dst);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PARDISOSUPPORT_H
diff --git a/extern/Eigen3/Eigen/src/QR/CMakeLists.txt b/extern/Eigen3/Eigen/src/QR/CMakeLists.txt
new file mode 100644
index 00000000000..96f43d7f5b3
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/QR/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_QR_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_QR_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/QR COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/QR/ColPivHouseholderQR_MKL.h b/extern/Eigen3/Eigen/src/QR/ColPivHouseholderQR_MKL.h
new file mode 100644
index 00000000000..0ad66d3f8d3
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/QR/ColPivHouseholderQR_MKL.h
@@ -0,0 +1,98 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *    Householder QR decomposition of a matrix with column pivoting based on
+ *    LAPACKE_?geqp3 function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_MKL_H
+#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_MKL_H
+
+#include "Eigen/src/Core/util/MKL_support.h"
+
+namespace Eigen { 
+
+/** \internal Specialization for the data types supported by MKL */
+
+#define EIGEN_MKL_QR_COLPIV(EIGTYPE, MKLTYPE, MKLPREFIX, EIGCOLROW, MKLCOLROW) \
+template<> \
+ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >& \
+ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >::compute( \
+              const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix) \
+\
+{ \
+  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \
+  typedef MatrixType::Scalar Scalar; \
+  typedef MatrixType::RealScalar RealScalar; \
+  Index rows = matrix.rows();\
+  Index cols = matrix.cols();\
+  Index size = matrix.diagonalSize();\
+\
+  m_qr = matrix;\
+  m_hCoeffs.resize(size);\
+\
+  m_colsTranspositions.resize(cols);\
+  /*Index number_of_transpositions = 0;*/ \
+\
+  m_nonzero_pivots = 0; \
+  m_maxpivot = RealScalar(0);\
+  m_colsPermutation.resize(cols); \
+  m_colsPermutation.indices().setZero(); \
+\
+  lapack_int lda = m_qr.outerStride(), i; \
+  lapack_int matrix_order = MKLCOLROW; \
+  LAPACKE_##MKLPREFIX##geqp3( matrix_order, rows, cols, (MKLTYPE*)m_qr.data(), lda, (lapack_int*)m_colsPermutation.indices().data(), (MKLTYPE*)m_hCoeffs.data()); \
+  m_isInitialized = true; \
+  m_maxpivot=m_qr.diagonal().cwiseAbs().maxCoeff(); \
+  m_hCoeffs.adjointInPlace(); \
+  RealScalar premultiplied_threshold = internal::abs(m_maxpivot) * threshold(); \
+  lapack_int *perm = m_colsPermutation.indices().data(); \
+  for(i=0;i<size;i++) { \
+    m_nonzero_pivots += (internal::abs(m_qr.coeff(i,i)) > premultiplied_threshold);\
+  } \
+  for(i=0;i<cols;i++) perm[i]--;\
+\
+  /*m_det_pq = (number_of_transpositions%2) ? -1 : 1;  // TODO: It's not needed now; fix upon availability in Eigen */ \
+\
+  return *this; \
+}
+
+EIGEN_MKL_QR_COLPIV(double,   double,        d, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_QR_COLPIV(float,    float,         s, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_QR_COLPIV(dcomplex, MKL_Complex16, z, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_QR_COLPIV(scomplex, MKL_Complex8,  c, ColMajor, LAPACK_COL_MAJOR)
+
+EIGEN_MKL_QR_COLPIV(double,   double,        d, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_MKL_QR_COLPIV(float,    float,         s, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_MKL_QR_COLPIV(dcomplex, MKL_Complex16, z, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_MKL_QR_COLPIV(scomplex, MKL_Complex8,  c, RowMajor, LAPACK_ROW_MAJOR)
+
+} // end namespace Eigen
+
+#endif // EIGEN_COLPIVOTINGHOUSEHOLDERQR_MKL_H
diff --git a/extern/Eigen3/Eigen/src/QR/HouseholderQR_MKL.h b/extern/Eigen3/Eigen/src/QR/HouseholderQR_MKL.h
new file mode 100644
index 00000000000..5313de604d2
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/QR/HouseholderQR_MKL.h
@@ -0,0 +1,69 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *    Householder QR decomposition of a matrix w/o pivoting based on
+ *    LAPACKE_?geqrf function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_QR_MKL_H
+#define EIGEN_QR_MKL_H
+
+#include "Eigen/src/Core/util/MKL_support.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal Specialization for the data types supported by MKL */
+
+#define EIGEN_MKL_QR_NOPIV(EIGTYPE, MKLTYPE, MKLPREFIX) \
+template<typename MatrixQR, typename HCoeffs> \
+void householder_qr_inplace_blocked(MatrixQR& mat, HCoeffs& hCoeffs, \
+                                       typename MatrixQR::Index maxBlockSize=32, \
+                                       EIGTYPE* tempData = 0) \
+{ \
+  lapack_int m = mat.rows(); \
+  lapack_int n = mat.cols(); \
+  lapack_int lda = mat.outerStride(); \
+  lapack_int matrix_order = (MatrixQR::IsRowMajor) ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \
+  LAPACKE_##MKLPREFIX##geqrf( matrix_order, m, n, (MKLTYPE*)mat.data(), lda, (MKLTYPE*)hCoeffs.data()); \
+  hCoeffs.adjointInPlace(); \
+\
+}
+
+EIGEN_MKL_QR_NOPIV(double, double, d)
+EIGEN_MKL_QR_NOPIV(float, float, s)
+EIGEN_MKL_QR_NOPIV(dcomplex, MKL_Complex16, z)
+EIGEN_MKL_QR_NOPIV(scomplex, MKL_Complex8, c)
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_QR_MKL_H
diff --git a/extern/Eigen3/Eigen/src/SVD/CMakeLists.txt b/extern/Eigen3/Eigen/src/SVD/CMakeLists.txt
new file mode 100644
index 00000000000..55efc44b129
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SVD/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_SVD_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_SVD_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SVD COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/SVD/JacobiSVD_MKL.h b/extern/Eigen3/Eigen/src/SVD/JacobiSVD_MKL.h
new file mode 100644
index 00000000000..1eeafca49dd
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SVD/JacobiSVD_MKL.h
@@ -0,0 +1,92 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *    Singular Value Decomposition - SVD.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_JACOBISVD_MKL_H
+#define EIGEN_JACOBISVD_MKL_H
+
+#include "Eigen/src/Core/util/MKL_support.h"
+
+namespace Eigen { 
+
+/** \internal Specialization for the data types supported by MKL */
+
+#define EIGEN_MKL_SVD(EIGTYPE, MKLTYPE, MKLRTYPE, MKLPREFIX, EIGCOLROW, MKLCOLROW) \
+template<> \
+JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>& \
+JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, unsigned int computationOptions) \
+{ \
+  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \
+  typedef MatrixType::Scalar Scalar; \
+  typedef MatrixType::RealScalar RealScalar; \
+  allocate(matrix.rows(), matrix.cols(), computationOptions); \
+\
+  /*const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();*/ \
+  m_nonzeroSingularValues = m_diagSize; \
+\
+  lapack_int lda = matrix.outerStride(), ldu, ldvt; \
+  lapack_int matrix_order = MKLCOLROW; \
+  char jobu, jobvt; \
+  MKLTYPE *u, *vt, dummy; \
+  jobu  = (m_computeFullU) ? 'A' : (m_computeThinU) ? 'S' : 'N'; \
+  jobvt = (m_computeFullV) ? 'A' : (m_computeThinV) ? 'S' : 'N'; \
+  if (computeU()) { \
+    ldu  = m_matrixU.outerStride(); \
+    u    = (MKLTYPE*)m_matrixU.data(); \
+  } else { ldu=1; u=&dummy; }\
+  MatrixType localV; \
+  ldvt = (m_computeFullV) ? m_cols : (m_computeThinV) ? m_diagSize : 1; \
+  if (computeV()) { \
+    localV.resize(ldvt, m_cols); \
+    vt   = (MKLTYPE*)localV.data(); \
+  } else { ldvt=1; vt=&dummy; }\
+  Matrix<MKLRTYPE, Dynamic, Dynamic> superb; superb.resize(m_diagSize, 1); \
+  MatrixType m_temp; m_temp = matrix; \
+  LAPACKE_##MKLPREFIX##gesvd( matrix_order, jobu, jobvt, m_rows, m_cols, (MKLTYPE*)m_temp.data(), lda, (MKLRTYPE*)m_singularValues.data(), u, ldu, vt, ldvt, superb.data()); \
+  if (computeV()) m_matrixV = localV.adjoint(); \
+ /* for(int i=0;i<m_diagSize;i++) if (m_singularValues.coeffRef(i) < precision) { m_nonzeroSingularValues--; m_singularValues.coeffRef(i)=RealScalar(0);}*/ \
+  m_isInitialized = true; \
+  return *this; \
+}
+
+EIGEN_MKL_SVD(double,   double,        double, d, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_SVD(float,    float,         float , s, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_SVD(dcomplex, MKL_Complex16, double, z, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_MKL_SVD(scomplex, MKL_Complex8,  float , c, ColMajor, LAPACK_COL_MAJOR)
+
+EIGEN_MKL_SVD(double,   double,        double, d, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_MKL_SVD(float,    float,         float , s, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_MKL_SVD(dcomplex, MKL_Complex16, double, z, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_MKL_SVD(scomplex, MKL_Complex8,  float , c, RowMajor, LAPACK_ROW_MAJOR)
+
+} // end namespace Eigen
+
+#endif // EIGEN_JACOBISVD_MKL_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/AmbiVector.h b/extern/Eigen3/Eigen/src/Sparse/AmbiVector.h
new file mode 100644
index 00000000000..2ea8ba3096b
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/AmbiVector.h
@@ -0,0 +1,379 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_AMBIVECTOR_H
+#define EIGEN_AMBIVECTOR_H
+
+/** \internal
+  * Hybrid sparse/dense vector class designed for intensive read-write operations.
+  *
+  * See BasicSparseLLT and SparseProduct for usage examples.
+  */
+template<typename _Scalar, typename _Index>
+class AmbiVector
+{
+  public:
+    typedef _Scalar Scalar;
+    typedef _Index Index;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    AmbiVector(Index size)
+      : m_buffer(0), m_zero(0), m_size(0), m_allocatedSize(0), m_allocatedElements(0), m_mode(-1)
+    {
+      resize(size);
+    }
+
+    void init(double estimatedDensity);
+    void init(int mode);
+
+    Index nonZeros() const;
+
+    /** Specifies a sub-vector to work on */
+    void setBounds(Index start, Index end) { m_start = start; m_end = end; }
+
+    void setZero();
+
+    void restart();
+    Scalar& coeffRef(Index i);
+    Scalar& coeff(Index i);
+
+    class Iterator;
+
+    ~AmbiVector() { delete[] m_buffer; }
+
+    void resize(Index size)
+    {
+      if (m_allocatedSize < size)
+        reallocate(size);
+      m_size = size;
+    }
+
+    Index size() const { return m_size; }
+
+  protected:
+
+    void reallocate(Index size)
+    {
+      // if the size of the matrix is not too large, let's allocate a bit more than needed such
+      // that we can handle dense vector even in sparse mode.
+      delete[] m_buffer;
+      if (size<1000)
+      {
+        Index allocSize = (size * sizeof(ListEl))/sizeof(Scalar);
+        m_allocatedElements = (allocSize*sizeof(Scalar))/sizeof(ListEl);
+        m_buffer = new Scalar[allocSize];
+      }
+      else
+      {
+        m_allocatedElements = (size*sizeof(Scalar))/sizeof(ListEl);
+        m_buffer = new Scalar[size];
+      }
+      m_size = size;
+      m_start = 0;
+      m_end = m_size;
+    }
+
+    void reallocateSparse()
+    {
+      Index copyElements = m_allocatedElements;
+      m_allocatedElements = (std::min)(Index(m_allocatedElements*1.5),m_size);
+      Index allocSize = m_allocatedElements * sizeof(ListEl);
+      allocSize = allocSize/sizeof(Scalar) + (allocSize%sizeof(Scalar)>0?1:0);
+      Scalar* newBuffer = new Scalar[allocSize];
+      memcpy(newBuffer,  m_buffer,  copyElements * sizeof(ListEl));
+      delete[] m_buffer;
+      m_buffer = newBuffer;
+    }
+
+  protected:
+    // element type of the linked list
+    struct ListEl
+    {
+      Index next;
+      Index index;
+      Scalar value;
+    };
+
+    // used to store data in both mode
+    Scalar* m_buffer;
+    Scalar m_zero;
+    Index m_size;
+    Index m_start;
+    Index m_end;
+    Index m_allocatedSize;
+    Index m_allocatedElements;
+    Index m_mode;
+
+    // linked list mode
+    Index m_llStart;
+    Index m_llCurrent;
+    Index m_llSize;
+};
+
+/** \returns the number of non zeros in the current sub vector */
+template<typename _Scalar,typename _Index>
+_Index AmbiVector<_Scalar,_Index>::nonZeros() const
+{
+  if (m_mode==IsSparse)
+    return m_llSize;
+  else
+    return m_end - m_start;
+}
+
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::init(double estimatedDensity)
+{
+  if (estimatedDensity>0.1)
+    init(IsDense);
+  else
+    init(IsSparse);
+}
+
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::init(int mode)
+{
+  m_mode = mode;
+  if (m_mode==IsSparse)
+  {
+    m_llSize = 0;
+    m_llStart = -1;
+  }
+}
+
+/** Must be called whenever we might perform a write access
+  * with an index smaller than the previous one.
+  *
+  * Don't worry, this function is extremely cheap.
+  */
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::restart()
+{
+  m_llCurrent = m_llStart;
+}
+
+/** Set all coefficients of current subvector to zero */
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::setZero()
+{
+  if (m_mode==IsDense)
+  {
+    for (Index i=m_start; i<m_end; ++i)
+      m_buffer[i] = Scalar(0);
+  }
+  else
+  {
+    eigen_assert(m_mode==IsSparse);
+    m_llSize = 0;
+    m_llStart = -1;
+  }
+}
+
+template<typename _Scalar,typename _Index>
+_Scalar& AmbiVector<_Scalar,_Index>::coeffRef(_Index i)
+{
+  if (m_mode==IsDense)
+    return m_buffer[i];
+  else
+  {
+    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+    // TODO factorize the following code to reduce code generation
+    eigen_assert(m_mode==IsSparse);
+    if (m_llSize==0)
+    {
+      // this is the first element
+      m_llStart = 0;
+      m_llCurrent = 0;
+      ++m_llSize;
+      llElements[0].value = Scalar(0);
+      llElements[0].index = i;
+      llElements[0].next = -1;
+      return llElements[0].value;
+    }
+    else if (i<llElements[m_llStart].index)
+    {
+      // this is going to be the new first element of the list
+      ListEl& el = llElements[m_llSize];
+      el.value = Scalar(0);
+      el.index = i;
+      el.next = m_llStart;
+      m_llStart = m_llSize;
+      ++m_llSize;
+      m_llCurrent = m_llStart;
+      return el.value;
+    }
+    else
+    {
+      Index nextel = llElements[m_llCurrent].next;
+      eigen_assert(i>=llElements[m_llCurrent].index && "you must call restart() before inserting an element with lower or equal index");
+      while (nextel >= 0 && llElements[nextel].index<=i)
+      {
+        m_llCurrent = nextel;
+        nextel = llElements[nextel].next;
+      }
+
+      if (llElements[m_llCurrent].index==i)
+      {
+        // the coefficient already exists and we found it !
+        return llElements[m_llCurrent].value;
+      }
+      else
+      {
+        if (m_llSize>=m_allocatedElements)
+        {
+          reallocateSparse();
+          llElements = reinterpret_cast<ListEl*>(m_buffer);
+        }
+        eigen_internal_assert(m_llSize<m_allocatedElements && "internal error: overflow in sparse mode");
+        // let's insert a new coefficient
+        ListEl& el = llElements[m_llSize];
+        el.value = Scalar(0);
+        el.index = i;
+        el.next = llElements[m_llCurrent].next;
+        llElements[m_llCurrent].next = m_llSize;
+        ++m_llSize;
+        return el.value;
+      }
+    }
+  }
+}
+
+template<typename _Scalar,typename _Index>
+_Scalar& AmbiVector<_Scalar,_Index>::coeff(_Index i)
+{
+  if (m_mode==IsDense)
+    return m_buffer[i];
+  else
+  {
+    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+    eigen_assert(m_mode==IsSparse);
+    if ((m_llSize==0) || (i<llElements[m_llStart].index))
+    {
+      return m_zero;
+    }
+    else
+    {
+      Index elid = m_llStart;
+      while (elid >= 0 && llElements[elid].index<i)
+        elid = llElements[elid].next;
+
+      if (llElements[elid].index==i)
+        return llElements[m_llCurrent].value;
+      else
+        return m_zero;
+    }
+  }
+}
+
+/** Iterator over the nonzero coefficients */
+template<typename _Scalar,typename _Index>
+class AmbiVector<_Scalar,_Index>::Iterator
+{
+  public:
+    typedef _Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    /** Default constructor
+      * \param vec the vector on which we iterate
+      * \param epsilon the minimal value used to prune zero coefficients.
+      * In practice, all coefficients having a magnitude smaller than \a epsilon
+      * are skipped.
+      */
+    Iterator(const AmbiVector& vec, RealScalar epsilon = RealScalar(0.1)*NumTraits<RealScalar>::dummy_precision())
+      : m_vector(vec)
+    {
+      m_epsilon = epsilon;
+      m_isDense = m_vector.m_mode==IsDense;
+      if (m_isDense)
+      {
+        m_currentEl = 0;   // this is to avoid a compilation warning
+        m_cachedValue = 0; // this is to avoid a compilation warning
+        m_cachedIndex = m_vector.m_start-1;
+        ++(*this);
+      }
+      else
+      {
+        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+        m_currentEl = m_vector.m_llStart;
+        while (m_currentEl>=0 && internal::abs(llElements[m_currentEl].value)<m_epsilon)
+          m_currentEl = llElements[m_currentEl].next;
+        if (m_currentEl<0)
+        {
+          m_cachedValue = 0; // this is to avoid a compilation warning
+          m_cachedIndex = -1;
+        }
+        else
+        {
+          m_cachedIndex = llElements[m_currentEl].index;
+          m_cachedValue = llElements[m_currentEl].value;
+        }
+      }
+    }
+
+    Index index() const { return m_cachedIndex; }
+    Scalar value() const { return m_cachedValue; }
+
+    operator bool() const { return m_cachedIndex>=0; }
+
+    Iterator& operator++()
+    {
+      if (m_isDense)
+      {
+        do {
+          ++m_cachedIndex;
+        } while (m_cachedIndex<m_vector.m_end && internal::abs(m_vector.m_buffer[m_cachedIndex])<m_epsilon);
+        if (m_cachedIndex<m_vector.m_end)
+          m_cachedValue = m_vector.m_buffer[m_cachedIndex];
+        else
+          m_cachedIndex=-1;
+      }
+      else
+      {
+        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+        do {
+          m_currentEl = llElements[m_currentEl].next;
+        } while (m_currentEl>=0 && internal::abs(llElements[m_currentEl].value)<m_epsilon);
+        if (m_currentEl<0)
+        {
+          m_cachedIndex = -1;
+        }
+        else
+        {
+          m_cachedIndex = llElements[m_currentEl].index;
+          m_cachedValue = llElements[m_currentEl].value;
+        }
+      }
+      return *this;
+    }
+
+  protected:
+    const AmbiVector& m_vector; // the target vector
+    Index m_currentEl;            // the current element in sparse/linked-list mode
+    RealScalar m_epsilon;       // epsilon used to prune zero coefficients
+    Index m_cachedIndex;          // current coordinate
+    Scalar m_cachedValue;       // current value
+    bool m_isDense;             // mode of the vector
+};
+
+
+#endif // EIGEN_AMBIVECTOR_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/CompressedStorage.h b/extern/Eigen3/Eigen/src/Sparse/CompressedStorage.h
new file mode 100644
index 00000000000..b3bde272ec2
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/CompressedStorage.h
@@ -0,0 +1,239 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_COMPRESSED_STORAGE_H
+#define EIGEN_COMPRESSED_STORAGE_H
+
+/** Stores a sparse set of values as a list of values and a list of indices.
+  *
+  */
+template<typename _Scalar,typename _Index>
+class CompressedStorage
+{
+  public:
+
+    typedef _Scalar Scalar;
+    typedef _Index Index;
+
+  protected:
+
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  public:
+
+    CompressedStorage()
+      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
+    {}
+
+    CompressedStorage(size_t size)
+      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
+    {
+      resize(size);
+    }
+
+    CompressedStorage(const CompressedStorage& other)
+      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
+    {
+      *this = other;
+    }
+
+    CompressedStorage& operator=(const CompressedStorage& other)
+    {
+      resize(other.size());
+      memcpy(m_values, other.m_values, m_size * sizeof(Scalar));
+      memcpy(m_indices, other.m_indices, m_size * sizeof(Index));
+      return *this;
+    }
+
+    void swap(CompressedStorage& other)
+    {
+      std::swap(m_values, other.m_values);
+      std::swap(m_indices, other.m_indices);
+      std::swap(m_size, other.m_size);
+      std::swap(m_allocatedSize, other.m_allocatedSize);
+    }
+
+    ~CompressedStorage()
+    {
+      delete[] m_values;
+      delete[] m_indices;
+    }
+
+    void reserve(size_t size)
+    {
+      size_t newAllocatedSize = m_size + size;
+      if (newAllocatedSize > m_allocatedSize)
+        reallocate(newAllocatedSize);
+    }
+
+    void squeeze()
+    {
+      if (m_allocatedSize>m_size)
+        reallocate(m_size);
+    }
+
+    void resize(size_t size, float reserveSizeFactor = 0)
+    {
+      if (m_allocatedSize<size)
+        reallocate(size + size_t(reserveSizeFactor*size));
+      m_size = size;
+    }
+
+    void append(const Scalar& v, Index i)
+    {
+      Index id = static_cast<Index>(m_size);
+      resize(m_size+1, 1);
+      m_values[id] = v;
+      m_indices[id] = i;
+    }
+
+    inline size_t size() const { return m_size; }
+    inline size_t allocatedSize() const { return m_allocatedSize; }
+    inline void clear() { m_size = 0; }
+
+    inline Scalar& value(size_t i) { return m_values[i]; }
+    inline const Scalar& value(size_t i) const { return m_values[i]; }
+
+    inline Index& index(size_t i) { return m_indices[i]; }
+    inline const Index& index(size_t i) const { return m_indices[i]; }
+
+    static CompressedStorage Map(Index* indices, Scalar* values, size_t size)
+    {
+      CompressedStorage res;
+      res.m_indices = indices;
+      res.m_values = values;
+      res.m_allocatedSize = res.m_size = size;
+      return res;
+    }
+
+    /** \returns the largest \c k such that for all \c j in [0,k) index[\c j]\<\a key */
+    inline Index searchLowerIndex(Index key) const
+    {
+      return searchLowerIndex(0, m_size, key);
+    }
+
+    /** \returns the largest \c k in [start,end) such that for all \c j in [start,k) index[\c j]\<\a key */
+    inline Index searchLowerIndex(size_t start, size_t end, Index key) const
+    {
+      while(end>start)
+      {
+        size_t mid = (end+start)>>1;
+        if (m_indices[mid]<key)
+          start = mid+1;
+        else
+          end = mid;
+      }
+      return static_cast<Index>(start);
+    }
+
+    /** \returns the stored value at index \a key
+      * If the value does not exist, then the value \a defaultValue is returned without any insertion. */
+    inline Scalar at(Index key, Scalar defaultValue = Scalar(0)) const
+    {
+      if (m_size==0)
+        return defaultValue;
+      else if (key==m_indices[m_size-1])
+        return m_values[m_size-1];
+      // ^^  optimization: let's first check if it is the last coefficient
+      // (very common in high level algorithms)
+      const size_t id = searchLowerIndex(0,m_size-1,key);
+      return ((id<m_size) && (m_indices[id]==key)) ? m_values[id] : defaultValue;
+    }
+
+    /** Like at(), but the search is performed in the range [start,end) */
+    inline Scalar atInRange(size_t start, size_t end, Index key, Scalar defaultValue = Scalar(0)) const
+    {
+      if (start>=end)
+        return Scalar(0);
+      else if (end>start && key==m_indices[end-1])
+        return m_values[end-1];
+      // ^^  optimization: let's first check if it is the last coefficient
+      // (very common in high level algorithms)
+      const size_t id = searchLowerIndex(start,end-1,key);
+      return ((id<end) && (m_indices[id]==key)) ? m_values[id] : defaultValue;
+    }
+
+    /** \returns a reference to the value at index \a key
+      * If the value does not exist, then the value \a defaultValue is inserted
+      * such that the keys are sorted. */
+    inline Scalar& atWithInsertion(Index key, Scalar defaultValue = Scalar(0))
+    {
+      size_t id = searchLowerIndex(0,m_size,key);
+      if (id>=m_size || m_indices[id]!=key)
+      {
+        resize(m_size+1,1);
+        for (size_t j=m_size-1; j>id; --j)
+        {
+          m_indices[j] = m_indices[j-1];
+          m_values[j] = m_values[j-1];
+        }
+        m_indices[id] = key;
+        m_values[id] = defaultValue;
+      }
+      return m_values[id];
+    }
+
+    void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision())
+    {
+      size_t k = 0;
+      size_t n = size();
+      for (size_t i=0; i<n; ++i)
+      {
+        if (!internal::isMuchSmallerThan(value(i), reference, epsilon))
+        {
+          value(k) = value(i);
+          index(k) = index(i);
+          ++k;
+        }
+      }
+      resize(k,0);
+    }
+
+  protected:
+
+    inline void reallocate(size_t size)
+    {
+      Scalar* newValues  = new Scalar[size];
+      Index* newIndices = new Index[size];
+      size_t copySize = (std::min)(size, m_size);
+      // copy
+      memcpy(newValues,  m_values,  copySize * sizeof(Scalar));
+      memcpy(newIndices, m_indices, copySize * sizeof(Index));
+      // delete old stuff
+      delete[] m_values;
+      delete[] m_indices;
+      m_values = newValues;
+      m_indices = newIndices;
+      m_allocatedSize = size;
+    }
+
+  protected:
+    Scalar* m_values;
+    Index* m_indices;
+    size_t m_size;
+    size_t m_allocatedSize;
+
+};
+
+#endif // EIGEN_COMPRESSED_STORAGE_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/CoreIterators.h b/extern/Eigen3/Eigen/src/Sparse/CoreIterators.h
new file mode 100644
index 00000000000..b4beaeee69e
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/CoreIterators.h
@@ -0,0 +1,71 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_COREITERATORS_H
+#define EIGEN_COREITERATORS_H
+
+/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
+ */
+
+/** \class InnerIterator
+  * \brief An InnerIterator allows to loop over the element of a sparse (or dense) matrix or expression
+  *
+  * todo
+  */
+
+// generic version for dense matrix and expressions
+template<typename Derived> class DenseBase<Derived>::InnerIterator
+{
+  protected:
+    typedef typename Derived::Scalar Scalar;
+    typedef typename Derived::Index Index;
+
+    enum { IsRowMajor = (Derived::Flags&RowMajorBit)==RowMajorBit };
+  public:
+    EIGEN_STRONG_INLINE InnerIterator(const Derived& expr, Index outer)
+      : m_expression(expr), m_inner(0), m_outer(outer), m_end(expr.innerSize())
+    {}
+
+    EIGEN_STRONG_INLINE Scalar value() const
+    {
+      return (IsRowMajor) ? m_expression.coeff(m_outer, m_inner)
+                          : m_expression.coeff(m_inner, m_outer);
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() { m_inner++; return *this; }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_inner; }
+    inline Index row() const { return IsRowMajor ? m_outer : index(); }
+    inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
+
+  protected:
+    const Derived& m_expression;
+    Index m_inner;
+    const Index m_outer;
+    const Index m_end;
+};
+
+#endif // EIGEN_COREITERATORS_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/DynamicSparseMatrix.h b/extern/Eigen3/Eigen/src/Sparse/DynamicSparseMatrix.h
new file mode 100644
index 00000000000..93e75f4c601
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/DynamicSparseMatrix.h
@@ -0,0 +1,346 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_DYNAMIC_SPARSEMATRIX_H
+#define EIGEN_DYNAMIC_SPARSEMATRIX_H
+
+/** \class DynamicSparseMatrix
+  *
+  * \brief A sparse matrix class designed for matrix assembly purpose
+  *
+  * \param _Scalar the scalar type, i.e. the type of the coefficients
+  *
+  * Unlike SparseMatrix, this class provides a much higher degree of flexibility. In particular, it allows
+  * random read/write accesses in log(rho*outer_size) where \c rho is the probability that a coefficient is
+  * nonzero and outer_size is the number of columns if the matrix is column-major and the number of rows
+  * otherwise.
+  *
+  * Internally, the data are stored as a std::vector of compressed vector. The performances of random writes might
+  * decrease as the number of nonzeros per inner-vector increase. In practice, we observed very good performance
+  * till about 100 nonzeros/vector, and the performance remains relatively good till 500 nonzeros/vectors.
+  *
+  * \see SparseMatrix
+  */
+
+namespace internal {
+template<typename _Scalar, int _Options, typename _Index>
+struct traits<DynamicSparseMatrix<_Scalar, _Options, _Index> >
+{
+  typedef _Scalar Scalar;
+  typedef _Index Index;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = Dynamic,
+    Flags = _Options | NestByRefBit | LvalueBit,
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    SupportedAccessPatterns = OuterRandomAccessPattern
+  };
+};
+}
+
+template<typename _Scalar, int _Options, typename _Index>
+class DynamicSparseMatrix
+  : public SparseMatrixBase<DynamicSparseMatrix<_Scalar, _Options, _Index> >
+{
+  public:
+    EIGEN_SPARSE_PUBLIC_INTERFACE(DynamicSparseMatrix)
+    // FIXME: why are these operator already alvailable ???
+    // EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(DynamicSparseMatrix, +=)
+    // EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(DynamicSparseMatrix, -=)
+    typedef MappedSparseMatrix<Scalar,Flags> Map;
+    using Base::IsRowMajor;
+    using Base::operator=;
+    enum {
+      Options = _Options
+    };
+
+  protected:
+
+    typedef DynamicSparseMatrix<Scalar,(Flags&~RowMajorBit)|(IsRowMajor?RowMajorBit:0)> TransposedSparseMatrix;
+
+    Index m_innerSize;
+    std::vector<CompressedStorage<Scalar,Index> > m_data;
+
+  public:
+
+    inline Index rows() const { return IsRowMajor ? outerSize() : m_innerSize; }
+    inline Index cols() const { return IsRowMajor ? m_innerSize : outerSize(); }
+    inline Index innerSize() const { return m_innerSize; }
+    inline Index outerSize() const { return static_cast<Index>(m_data.size()); }
+    inline Index innerNonZeros(Index j) const { return m_data[j].size(); }
+
+    std::vector<CompressedStorage<Scalar,Index> >& _data() { return m_data; }
+    const std::vector<CompressedStorage<Scalar,Index> >& _data() const { return m_data; }
+
+    /** \returns the coefficient value at given position \a row, \a col
+      * This operation involes a log(rho*outer_size) binary search.
+      */
+    inline Scalar coeff(Index row, Index col) const
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+      return m_data[outer].at(inner);
+    }
+
+    /** \returns a reference to the coefficient value at given position \a row, \a col
+      * This operation involes a log(rho*outer_size) binary search. If the coefficient does not
+      * exist yet, then a sorted insertion into a sequential buffer is performed.
+      */
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+      return m_data[outer].atWithInsertion(inner);
+    }
+
+    class InnerIterator;
+
+    void setZero()
+    {
+      for (Index j=0; j<outerSize(); ++j)
+        m_data[j].clear();
+    }
+
+    /** \returns the number of non zero coefficients */
+    Index nonZeros() const
+    {
+      Index res = 0;
+      for (Index j=0; j<outerSize(); ++j)
+        res += static_cast<Index>(m_data[j].size());
+      return res;
+    }
+
+
+
+    void reserve(Index reserveSize = 1000)
+    {
+      if (outerSize()>0)
+      {
+        Index reserveSizePerVector = (std::max)(reserveSize/outerSize(),Index(4));
+        for (Index j=0; j<outerSize(); ++j)
+        {
+          m_data[j].reserve(reserveSizePerVector);
+        }
+      }
+    }
+
+    /** Does nothing: provided for compatibility with SparseMatrix */
+    inline void startVec(Index /*outer*/) {}
+
+    /** \returns a reference to the non zero coefficient at position \a row, \a col assuming that:
+      * - the nonzero does not already exist
+      * - the new coefficient is the last one of the given inner vector.
+      *
+      * \sa insert, insertBackByOuterInner */
+    inline Scalar& insertBack(Index row, Index col)
+    {
+      return insertBackByOuterInner(IsRowMajor?row:col, IsRowMajor?col:row);
+    }
+
+    /** \sa insertBack */
+    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
+    {
+      eigen_assert(outer<Index(m_data.size()) && inner<m_innerSize && "out of range");
+      eigen_assert(((m_data[outer].size()==0) || (m_data[outer].index(m_data[outer].size()-1)<inner))
+                && "wrong sorted insertion");
+      m_data[outer].append(0, inner);
+      return m_data[outer].value(m_data[outer].size()-1);
+    }
+
+    inline Scalar& insert(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index startId = 0;
+      Index id = static_cast<Index>(m_data[outer].size()) - 1;
+      m_data[outer].resize(id+2,1);
+
+      while ( (id >= startId) && (m_data[outer].index(id) > inner) )
+      {
+        m_data[outer].index(id+1) = m_data[outer].index(id);
+        m_data[outer].value(id+1) = m_data[outer].value(id);
+        --id;
+      }
+      m_data[outer].index(id+1) = inner;
+      m_data[outer].value(id+1) = 0;
+      return m_data[outer].value(id+1);
+    }
+
+    /** Does nothing: provided for compatibility with SparseMatrix */
+    inline void finalize() {}
+
+    /** Suppress all nonzeros which are smaller than \a reference under the tolerence \a epsilon */
+    void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision())
+    {
+      for (Index j=0; j<outerSize(); ++j)
+        m_data[j].prune(reference,epsilon);
+    }
+
+    /** Resize the matrix without preserving the data (the matrix is set to zero)
+      */
+    void resize(Index rows, Index cols)
+    {
+      const Index outerSize = IsRowMajor ? rows : cols;
+      m_innerSize = IsRowMajor ? cols : rows;
+      setZero();
+      if (Index(m_data.size()) != outerSize)
+      {
+        m_data.resize(outerSize);
+      }
+    }
+
+    void resizeAndKeepData(Index rows, Index cols)
+    {
+      const Index outerSize = IsRowMajor ? rows : cols;
+      const Index innerSize = IsRowMajor ? cols : rows;
+      if (m_innerSize>innerSize)
+      {
+        // remove all coefficients with innerCoord>=innerSize
+        // TODO
+        //std::cerr << "not implemented yet\n";
+        exit(2);
+      }
+      if (m_data.size() != outerSize)
+      {
+        m_data.resize(outerSize);
+      }
+    }
+
+    inline DynamicSparseMatrix()
+      : m_innerSize(0), m_data(0)
+    {
+      eigen_assert(innerSize()==0 && outerSize()==0);
+    }
+
+    inline DynamicSparseMatrix(Index rows, Index cols)
+      : m_innerSize(0)
+    {
+      resize(rows, cols);
+    }
+
+    template<typename OtherDerived>
+    explicit inline DynamicSparseMatrix(const SparseMatrixBase<OtherDerived>& other)
+      : m_innerSize(0)
+    {
+    Base::operator=(other.derived());
+    }
+
+    inline DynamicSparseMatrix(const DynamicSparseMatrix& other)
+      : Base(), m_innerSize(0)
+    {
+      *this = other.derived();
+    }
+
+    inline void swap(DynamicSparseMatrix& other)
+    {
+      //EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n");
+      std::swap(m_innerSize, other.m_innerSize);
+      //std::swap(m_outerSize, other.m_outerSize);
+      m_data.swap(other.m_data);
+    }
+
+    inline DynamicSparseMatrix& operator=(const DynamicSparseMatrix& other)
+    {
+      if (other.isRValue())
+      {
+        swap(other.const_cast_derived());
+      }
+      else
+      {
+        resize(other.rows(), other.cols());
+        m_data = other.m_data;
+      }
+      return *this;
+    }
+
+    /** Destructor */
+    inline ~DynamicSparseMatrix() {}
+
+  public:
+
+    /** \deprecated
+      * Set the matrix to zero and reserve the memory for \a reserveSize nonzero coefficients. */
+    EIGEN_DEPRECATED void startFill(Index reserveSize = 1000)
+    {
+      setZero();
+      reserve(reserveSize);
+    }
+
+    /** \deprecated use insert()
+      * inserts a nonzero coefficient at given coordinates \a row, \a col and returns its reference assuming that:
+      *  1 - the coefficient does not exist yet
+      *  2 - this the coefficient with greater inner coordinate for the given outer coordinate.
+      * In other words, assuming \c *this is column-major, then there must not exists any nonzero coefficient of coordinates
+      * \c i \c x \a col such that \c i >= \a row. Otherwise the matrix is invalid.
+      *
+      * \see fillrand(), coeffRef()
+      */
+    EIGEN_DEPRECATED Scalar& fill(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+      return insertBack(outer,inner);
+    }
+
+    /** \deprecated use insert()
+      * Like fill() but with random inner coordinates.
+      * Compared to the generic coeffRef(), the unique limitation is that we assume
+      * the coefficient does not exist yet.
+      */
+    EIGEN_DEPRECATED Scalar& fillrand(Index row, Index col)
+    {
+      return insert(row,col);
+    }
+
+    /** \deprecated use finalize()
+      * Does nothing. Provided for compatibility with SparseMatrix. */
+    EIGEN_DEPRECATED void endFill() {}
+    
+#   ifdef EIGEN_DYNAMICSPARSEMATRIX_PLUGIN
+#     include EIGEN_DYNAMICSPARSEMATRIX_PLUGIN
+#   endif
+};
+
+template<typename Scalar, int _Options, typename _Index>
+class DynamicSparseMatrix<Scalar,_Options,_Index>::InnerIterator : public SparseVector<Scalar,_Options>::InnerIterator
+{
+    typedef typename SparseVector<Scalar,_Options>::InnerIterator Base;
+  public:
+    InnerIterator(const DynamicSparseMatrix& mat, Index outer)
+      : Base(mat.m_data[outer]), m_outer(outer)
+    {}
+
+    inline Index row() const { return IsRowMajor ? m_outer : Base::index(); }
+    inline Index col() const { return IsRowMajor ? Base::index() : m_outer; }
+
+  protected:
+    const Index m_outer;
+};
+
+#endif // EIGEN_DYNAMIC_SPARSEMATRIX_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/MappedSparseMatrix.h b/extern/Eigen3/Eigen/src/Sparse/MappedSparseMatrix.h
new file mode 100644
index 00000000000..31a431fb224
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/MappedSparseMatrix.h
@@ -0,0 +1,165 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_MAPPED_SPARSEMATRIX_H
+#define EIGEN_MAPPED_SPARSEMATRIX_H
+
+/** \class MappedSparseMatrix
+  *
+  * \brief Sparse matrix
+  *
+  * \param _Scalar the scalar type, i.e. the type of the coefficients
+  *
+  * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
+  *
+  */
+namespace internal {
+template<typename _Scalar, int _Flags, typename _Index>
+struct traits<MappedSparseMatrix<_Scalar, _Flags, _Index> > : traits<SparseMatrix<_Scalar, _Flags, _Index> >
+{};
+}
+
+template<typename _Scalar, int _Flags, typename _Index>
+class MappedSparseMatrix
+  : public SparseMatrixBase<MappedSparseMatrix<_Scalar, _Flags, _Index> >
+{
+  public:
+    EIGEN_SPARSE_PUBLIC_INTERFACE(MappedSparseMatrix)
+
+  protected:
+    enum { IsRowMajor = Base::IsRowMajor };
+
+    Index   m_outerSize;
+    Index   m_innerSize;
+    Index   m_nnz;
+    Index*  m_outerIndex;
+    Index*  m_innerIndices;
+    Scalar* m_values;
+
+  public:
+
+    inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
+    inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
+    inline Index innerSize() const { return m_innerSize; }
+    inline Index outerSize() const { return m_outerSize; }
+    inline Index innerNonZeros(Index j) const { return m_outerIndex[j+1]-m_outerIndex[j]; }
+
+    //----------------------------------------
+    // direct access interface
+    inline const Scalar* _valuePtr() const { return m_values; }
+    inline Scalar* _valuePtr() { return m_values; }
+
+    inline const Index* _innerIndexPtr() const { return m_innerIndices; }
+    inline Index* _innerIndexPtr() { return m_innerIndices; }
+
+    inline const Index* _outerIndexPtr() const { return m_outerIndex; }
+    inline Index* _outerIndexPtr() { return m_outerIndex; }
+    //----------------------------------------
+
+    inline Scalar coeff(Index row, Index col) const
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index start = m_outerIndex[outer];
+      Index end = m_outerIndex[outer+1];
+      if (start==end)
+        return Scalar(0);
+      else if (end>0 && inner==m_innerIndices[end-1])
+        return m_values[end-1];
+      // ^^  optimization: let's first check if it is the last coefficient
+      // (very common in high level algorithms)
+
+      const Index* r = std::lower_bound(&m_innerIndices[start],&m_innerIndices[end-1],inner);
+      const Index id = r-&m_innerIndices[0];
+      return ((*r==inner) && (id<end)) ? m_values[id] : Scalar(0);
+    }
+
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index start = m_outerIndex[outer];
+      Index end = m_outerIndex[outer+1];
+      eigen_assert(end>=start && "you probably called coeffRef on a non finalized matrix");
+      eigen_assert(end>start && "coeffRef cannot be called on a zero coefficient");
+      Index* r = std::lower_bound(&m_innerIndices[start],&m_innerIndices[end],inner);
+      const Index id = r-&m_innerIndices[0];
+      eigen_assert((*r==inner) && (id<end) && "coeffRef cannot be called on a zero coefficient");
+      return m_values[id];
+    }
+
+    class InnerIterator;
+
+    /** \returns the number of non zero coefficients */
+    inline Index nonZeros() const  { return m_nnz; }
+
+    inline MappedSparseMatrix(Index rows, Index cols, Index nnz, Index* outerIndexPtr, Index* innerIndexPtr, Scalar* valuePtr)
+      : m_outerSize(IsRowMajor?rows:cols), m_innerSize(IsRowMajor?cols:rows), m_nnz(nnz), m_outerIndex(outerIndexPtr),
+        m_innerIndices(innerIndexPtr), m_values(valuePtr)
+    {}
+
+    /** Empty destructor */
+    inline ~MappedSparseMatrix() {}
+};
+
+template<typename Scalar, int _Flags, typename _Index>
+class MappedSparseMatrix<Scalar,_Flags,_Index>::InnerIterator
+{
+  public:
+    InnerIterator(const MappedSparseMatrix& mat, Index outer)
+      : m_matrix(mat),
+        m_outer(outer),
+        m_id(mat._outerIndexPtr()[outer]),
+        m_start(m_id),
+        m_end(mat._outerIndexPtr()[outer+1])
+    {}
+
+    template<unsigned int Added, unsigned int Removed>
+    InnerIterator(const Flagged<MappedSparseMatrix,Added,Removed>& mat, Index outer)
+      : m_matrix(mat._expression()), m_id(m_matrix._outerIndexPtr()[outer]),
+        m_start(m_id), m_end(m_matrix._outerIndexPtr()[outer+1])
+    {}
+
+    inline InnerIterator& operator++() { m_id++; return *this; }
+
+    inline Scalar value() const { return m_matrix._valuePtr()[m_id]; }
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix._valuePtr()[m_id]); }
+
+    inline Index index() const { return m_matrix._innerIndexPtr()[m_id]; }
+    inline Index row() const { return IsRowMajor ? m_outer : index(); }
+    inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+    inline operator bool() const { return (m_id < m_end) && (m_id>=m_start); }
+
+  protected:
+    const MappedSparseMatrix& m_matrix;
+    const Index m_outer;
+    Index m_id;
+    const Index m_start;
+    const Index m_end;
+};
+
+#endif // EIGEN_MAPPED_SPARSEMATRIX_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseAssign.h b/extern/Eigen3/Eigen/src/Sparse/SparseAssign.h
new file mode 100644
index 00000000000..e69de29bb2d
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseAssign.h
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseBlock.h b/extern/Eigen3/Eigen/src/Sparse/SparseBlock.h
new file mode 100644
index 00000000000..8079c999994
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseBlock.h
@@ -0,0 +1,465 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_BLOCK_H
+#define EIGEN_SPARSE_BLOCK_H
+
+namespace internal {
+template<typename MatrixType, int Size>
+struct traits<SparseInnerVectorSet<MatrixType, Size> >
+{
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  typedef typename traits<MatrixType>::Index Index;
+  typedef typename traits<MatrixType>::StorageKind StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    IsRowMajor = (int(MatrixType::Flags)&RowMajorBit)==RowMajorBit,
+    Flags = MatrixType::Flags,
+    RowsAtCompileTime = IsRowMajor ? Size : MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = IsRowMajor ? MatrixType::ColsAtCompileTime : Size,
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    CoeffReadCost = MatrixType::CoeffReadCost
+  };
+};
+} // end namespace internal
+
+template<typename MatrixType, int Size>
+class SparseInnerVectorSet : internal::no_assignment_operator,
+  public SparseMatrixBase<SparseInnerVectorSet<MatrixType, Size> >
+{
+  public:
+
+    enum { IsRowMajor = internal::traits<SparseInnerVectorSet>::IsRowMajor };
+
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseInnerVectorSet)
+    class InnerIterator: public MatrixType::InnerIterator
+    {
+      public:
+        inline InnerIterator(const SparseInnerVectorSet& xpr, Index outer)
+          : MatrixType::InnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
+        {}
+        inline Index row() const { return IsRowMajor ? m_outer : this->index(); }
+        inline Index col() const { return IsRowMajor ? this->index() : m_outer; }
+      protected:
+        Index m_outer;
+    };
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outerStart, Index outerSize)
+      : m_matrix(matrix), m_outerStart(outerStart), m_outerSize(outerSize)
+    {
+      eigen_assert( (outerStart>=0) && ((outerStart+outerSize)<=matrix.outerSize()) );
+    }
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outer)
+      : m_matrix(matrix), m_outerStart(outer), m_outerSize(Size)
+    {
+      eigen_assert(Size!=Dynamic);
+      eigen_assert( (outer>=0) && (outer<matrix.outerSize()) );
+    }
+
+//     template<typename OtherDerived>
+//     inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+//     {
+//       return *this;
+//     }
+
+//     template<typename Sparse>
+//     inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+//     {
+//       return *this;
+//     }
+
+    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+  protected:
+
+    const typename MatrixType::Nested m_matrix;
+    Index m_outerStart;
+    const internal::variable_if_dynamic<Index, Size> m_outerSize;
+};
+
+/***************************************************************************
+* specialisation for DynamicSparseMatrix
+***************************************************************************/
+
+template<typename _Scalar, int _Options, int Size>
+class SparseInnerVectorSet<DynamicSparseMatrix<_Scalar, _Options>, Size>
+  : public SparseMatrixBase<SparseInnerVectorSet<DynamicSparseMatrix<_Scalar, _Options>, Size> >
+{
+    typedef DynamicSparseMatrix<_Scalar, _Options> MatrixType;
+  public:
+
+    enum { IsRowMajor = internal::traits<SparseInnerVectorSet>::IsRowMajor };
+
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseInnerVectorSet)
+    class InnerIterator: public MatrixType::InnerIterator
+    {
+      public:
+        inline InnerIterator(const SparseInnerVectorSet& xpr, Index outer)
+          : MatrixType::InnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
+        {}
+        inline Index row() const { return IsRowMajor ? m_outer : this->index(); }
+        inline Index col() const { return IsRowMajor ? this->index() : m_outer; }
+      protected:
+        Index m_outer;
+    };
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outerStart, Index outerSize)
+      : m_matrix(matrix), m_outerStart(outerStart), m_outerSize(outerSize)
+    {
+      eigen_assert( (outerStart>=0) && ((outerStart+outerSize)<=matrix.outerSize()) );
+    }
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outer)
+      : m_matrix(matrix), m_outerStart(outer), m_outerSize(Size)
+    {
+      eigen_assert(Size!=Dynamic);
+      eigen_assert( (outer>=0) && (outer<matrix.outerSize()) );
+    }
+
+    template<typename OtherDerived>
+    inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+      if (IsRowMajor != ((OtherDerived::Flags&RowMajorBit)==RowMajorBit))
+      {
+        // need to transpose => perform a block evaluation followed by a big swap
+        DynamicSparseMatrix<Scalar,IsRowMajor?RowMajorBit:0> aux(other);
+        *this = aux.markAsRValue();
+      }
+      else
+      {
+        // evaluate/copy vector per vector
+        for (Index j=0; j<m_outerSize.value(); ++j)
+        {
+          SparseVector<Scalar,IsRowMajor ? RowMajorBit : 0> aux(other.innerVector(j));
+          m_matrix.const_cast_derived()._data()[m_outerStart+j].swap(aux._data());
+        }
+      }
+      return *this;
+    }
+
+    inline SparseInnerVectorSet& operator=(const SparseInnerVectorSet& other)
+    {
+      return operator=<SparseInnerVectorSet>(other);
+    }
+
+    Index nonZeros() const
+    {
+      Index count = 0;
+      for (Index j=0; j<m_outerSize.value(); ++j)
+        count += m_matrix._data()[m_outerStart+j].size();
+      return count;
+    }
+
+    const Scalar& lastCoeff() const
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(SparseInnerVectorSet);
+      eigen_assert(m_matrix.data()[m_outerStart].size()>0);
+      return m_matrix.data()[m_outerStart].vale(m_matrix.data()[m_outerStart].size()-1);
+    }
+
+//     template<typename Sparse>
+//     inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+//     {
+//       return *this;
+//     }
+
+    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+  protected:
+
+    const typename MatrixType::Nested m_matrix;
+    Index m_outerStart;
+    const internal::variable_if_dynamic<Index, Size> m_outerSize;
+
+};
+
+
+/***************************************************************************
+* specialisation for SparseMatrix
+***************************************************************************/
+
+template<typename _Scalar, int _Options, typename _Index, int Size>
+class SparseInnerVectorSet<SparseMatrix<_Scalar, _Options, _Index>, Size>
+  : public SparseMatrixBase<SparseInnerVectorSet<SparseMatrix<_Scalar, _Options>, Size> >
+{
+    typedef SparseMatrix<_Scalar, _Options> MatrixType;
+  public:
+
+    enum { IsRowMajor = internal::traits<SparseInnerVectorSet>::IsRowMajor };
+
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseInnerVectorSet)
+    class InnerIterator: public MatrixType::InnerIterator
+    {
+      public:
+        inline InnerIterator(const SparseInnerVectorSet& xpr, Index outer)
+          : MatrixType::InnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
+        {}
+        inline Index row() const { return IsRowMajor ? m_outer : this->index(); }
+        inline Index col() const { return IsRowMajor ? this->index() : m_outer; }
+      protected:
+        Index m_outer;
+    };
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outerStart, Index outerSize)
+      : m_matrix(matrix), m_outerStart(outerStart), m_outerSize(outerSize)
+    {
+      eigen_assert( (outerStart>=0) && ((outerStart+outerSize)<=matrix.outerSize()) );
+    }
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outer)
+      : m_matrix(matrix), m_outerStart(outer), m_outerSize(Size)
+    {
+      eigen_assert(Size==1);
+      eigen_assert( (outer>=0) && (outer<matrix.outerSize()) );
+    }
+
+    template<typename OtherDerived>
+    inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+      typedef typename internal::remove_all<typename MatrixType::Nested>::type _NestedMatrixType;
+      _NestedMatrixType& matrix = const_cast<_NestedMatrixType&>(m_matrix);;
+      // This assignement is slow if this vector set not empty
+      // and/or it is not at the end of the nonzeros of the underlying matrix.
+
+      // 1 - eval to a temporary to avoid transposition and/or aliasing issues
+      SparseMatrix<Scalar, IsRowMajor ? RowMajor : ColMajor, Index> tmp(other);
+
+      // 2 - let's check whether there is enough allocated memory
+      Index nnz = tmp.nonZeros();
+      Index nnz_previous = nonZeros();
+      Index free_size = matrix.data().allocatedSize() - nnz_previous;
+      std::size_t nnz_head = m_outerStart==0 ? 0 : matrix._outerIndexPtr()[m_outerStart];
+      std::size_t tail = m_matrix._outerIndexPtr()[m_outerStart+m_outerSize.value()];
+      std::size_t nnz_tail = matrix.nonZeros() - tail;
+
+      if(nnz>free_size)
+      {
+        // realloc manually to reduce copies
+        typename MatrixType::Storage newdata(m_matrix.nonZeros() - nnz_previous + nnz);
+
+        std::memcpy(&newdata.value(0), &m_matrix.data().value(0), nnz_head*sizeof(Scalar));
+        std::memcpy(&newdata.index(0), &m_matrix.data().index(0), nnz_head*sizeof(Index));
+
+        std::memcpy(&newdata.value(nnz_head), &tmp.data().value(0), nnz*sizeof(Scalar));
+        std::memcpy(&newdata.index(nnz_head), &tmp.data().index(0), nnz*sizeof(Index));
+
+        std::memcpy(&newdata.value(nnz_head+nnz), &matrix.data().value(tail), nnz_tail*sizeof(Scalar));
+        std::memcpy(&newdata.index(nnz_head+nnz), &matrix.data().index(tail), nnz_tail*sizeof(Index));
+
+        matrix.data().swap(newdata);
+      }
+      else
+      {
+        // no need to realloc, simply copy the tail at its respective position and insert tmp
+        matrix.data().resize(nnz_head + nnz + nnz_tail);
+
+        if(nnz<nnz_previous)
+        {
+          std::memcpy(&matrix.data().value(nnz_head+nnz), &matrix.data().value(tail), nnz_tail*sizeof(Scalar));
+          std::memcpy(&matrix.data().index(nnz_head+nnz), &matrix.data().index(tail), nnz_tail*sizeof(Index));
+        }
+        else
+        {
+          for(Index i=nnz_tail-1; i>=0; --i)
+          {
+            matrix.data().value(nnz_head+nnz+i) = matrix.data().value(tail+i);
+            matrix.data().index(nnz_head+nnz+i) = matrix.data().index(tail+i);
+          }
+        }
+
+        std::memcpy(&matrix.data().value(nnz_head), &tmp.data().value(0), nnz*sizeof(Scalar));
+        std::memcpy(&matrix.data().index(nnz_head), &tmp.data().index(0), nnz*sizeof(Index));
+      }
+
+      // update outer index pointers
+      Index p = nnz_head;
+      for(Index k=1; k<m_outerSize.value(); ++k)
+      {
+        matrix._outerIndexPtr()[m_outerStart+k] = p;
+        p += tmp.innerVector(k).nonZeros();
+      }
+      std::ptrdiff_t offset = nnz - nnz_previous;
+      for(Index k = m_outerStart + m_outerSize.value(); k<=matrix.outerSize(); ++k)
+      {
+        matrix._outerIndexPtr()[k] += offset;
+      }
+
+      return *this;
+    }
+
+    inline SparseInnerVectorSet& operator=(const SparseInnerVectorSet& other)
+    {
+      return operator=<SparseInnerVectorSet>(other);
+    }
+
+    inline const Scalar* _valuePtr() const
+    { return m_matrix._valuePtr() + m_matrix._outerIndexPtr()[m_outerStart]; }
+    inline Scalar* _valuePtr()
+    { return m_matrix.const_cast_derived()._valuePtr() + m_matrix._outerIndexPtr()[m_outerStart]; }
+
+    inline const Index* _innerIndexPtr() const
+    { return m_matrix._innerIndexPtr() + m_matrix._outerIndexPtr()[m_outerStart]; }
+    inline Index* _innerIndexPtr()
+    { return m_matrix.const_cast_derived()._innerIndexPtr() + m_matrix._outerIndexPtr()[m_outerStart]; }
+
+    inline const Index* _outerIndexPtr() const
+    { return m_matrix._outerIndexPtr() + m_outerStart; }
+    inline Index* _outerIndexPtr()
+    { return m_matrix.const_cast_derived()._outerIndexPtr() + m_outerStart; }
+
+    Index nonZeros() const
+    {
+      return  std::size_t(m_matrix._outerIndexPtr()[m_outerStart+m_outerSize.value()])
+            - std::size_t(m_matrix._outerIndexPtr()[m_outerStart]);
+    }
+
+    const Scalar& lastCoeff() const
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(SparseInnerVectorSet);
+      eigen_assert(nonZeros()>0);
+      return m_matrix._valuePtr()[m_matrix._outerIndexPtr()[m_outerStart+1]-1];
+    }
+
+//     template<typename Sparse>
+//     inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+//     {
+//       return *this;
+//     }
+
+    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+  protected:
+
+    const typename MatrixType::Nested m_matrix;
+    Index m_outerStart;
+    const internal::variable_if_dynamic<Index, Size> m_outerSize;
+
+};
+
+//----------
+
+/** \returns the i-th row of the matrix \c *this. For row-major matrix only. */
+template<typename Derived>
+SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::row(Index i)
+{
+  EIGEN_STATIC_ASSERT(IsRowMajor,THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES);
+  return innerVector(i);
+}
+
+/** \returns the i-th row of the matrix \c *this. For row-major matrix only.
+  * (read-only version) */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::row(Index i) const
+{
+  EIGEN_STATIC_ASSERT(IsRowMajor,THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES);
+  return innerVector(i);
+}
+
+/** \returns the i-th column of the matrix \c *this. For column-major matrix only. */
+template<typename Derived>
+SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::col(Index i)
+{
+  EIGEN_STATIC_ASSERT(!IsRowMajor,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  return innerVector(i);
+}
+
+/** \returns the i-th column of the matrix \c *this. For column-major matrix only.
+  * (read-only version) */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::col(Index i) const
+{
+  EIGEN_STATIC_ASSERT(!IsRowMajor,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  return innerVector(i);
+}
+
+/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+  * is col-major (resp. row-major).
+  */
+template<typename Derived>
+SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::innerVector(Index outer)
+{ return SparseInnerVectorSet<Derived,1>(derived(), outer); }
+
+/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+  * is col-major (resp. row-major). Read-only.
+  */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::innerVector(Index outer) const
+{ return SparseInnerVectorSet<Derived,1>(derived(), outer); }
+
+//----------
+
+/** \returns the i-th row of the matrix \c *this. For row-major matrix only. */
+template<typename Derived>
+SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::subrows(Index start, Index size)
+{
+  EIGEN_STATIC_ASSERT(IsRowMajor,THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES);
+  return innerVectors(start, size);
+}
+
+/** \returns the i-th row of the matrix \c *this. For row-major matrix only.
+  * (read-only version) */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::subrows(Index start, Index size) const
+{
+  EIGEN_STATIC_ASSERT(IsRowMajor,THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES);
+  return innerVectors(start, size);
+}
+
+/** \returns the i-th column of the matrix \c *this. For column-major matrix only. */
+template<typename Derived>
+SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::subcols(Index start, Index size)
+{
+  EIGEN_STATIC_ASSERT(!IsRowMajor,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  return innerVectors(start, size);
+}
+
+/** \returns the i-th column of the matrix \c *this. For column-major matrix only.
+  * (read-only version) */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::subcols(Index start, Index size) const
+{
+  EIGEN_STATIC_ASSERT(!IsRowMajor,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  return innerVectors(start, size);
+}
+
+/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+  * is col-major (resp. row-major).
+  */
+template<typename Derived>
+SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::innerVectors(Index outerStart, Index outerSize)
+{ return SparseInnerVectorSet<Derived,Dynamic>(derived(), outerStart, outerSize); }
+
+/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+  * is col-major (resp. row-major). Read-only.
+  */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::innerVectors(Index outerStart, Index outerSize) const
+{ return SparseInnerVectorSet<Derived,Dynamic>(derived(), outerStart, outerSize); }
+
+#endif // EIGEN_SPARSE_BLOCK_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseCwiseBinaryOp.h b/extern/Eigen3/Eigen/src/Sparse/SparseCwiseBinaryOp.h
new file mode 100644
index 00000000000..cde5bbc0300
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseCwiseBinaryOp.h
@@ -0,0 +1,375 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_CWISE_BINARY_OP_H
+#define EIGEN_SPARSE_CWISE_BINARY_OP_H
+
+// Here we have to handle 3 cases:
+//  1 - sparse op dense
+//  2 - dense op sparse
+//  3 - sparse op sparse
+// We also need to implement a 4th iterator for:
+//  4 - dense op dense
+// Finally, we also need to distinguish between the product and other operations :
+//                configuration      returned mode
+//  1 - sparse op dense    product      sparse
+//                         generic      dense
+//  2 - dense op sparse    product      sparse
+//                         generic      dense
+//  3 - sparse op sparse   product      sparse
+//                         generic      sparse
+//  4 - dense op dense     product      dense
+//                         generic      dense
+
+namespace internal {
+
+template<> struct promote_storage_type<Dense,Sparse>
+{ typedef Sparse ret; };
+
+template<> struct promote_storage_type<Sparse,Dense>
+{ typedef Sparse ret; };
+
+template<typename BinaryOp, typename Lhs, typename Rhs, typename Derived,
+  typename _LhsStorageMode = typename traits<Lhs>::StorageKind,
+  typename _RhsStorageMode = typename traits<Rhs>::StorageKind>
+class sparse_cwise_binary_op_inner_iterator_selector;
+
+} // end namespace internal
+
+template<typename BinaryOp, typename Lhs, typename Rhs>
+class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Sparse>
+  : public SparseMatrixBase<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
+{
+  public:
+    class InnerIterator;
+    typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Derived)
+};
+
+template<typename BinaryOp, typename Lhs, typename Rhs>
+class CwiseBinaryOpImpl<BinaryOp,Lhs,Rhs,Sparse>::InnerIterator
+  : public internal::sparse_cwise_binary_op_inner_iterator_selector<BinaryOp,Lhs,Rhs,typename CwiseBinaryOpImpl<BinaryOp,Lhs,Rhs,Sparse>::InnerIterator>
+{
+  public:
+    typedef typename Lhs::Index Index;
+    typedef internal::sparse_cwise_binary_op_inner_iterator_selector<
+      BinaryOp,Lhs,Rhs, InnerIterator> Base;
+
+    EIGEN_STRONG_INLINE InnerIterator(const CwiseBinaryOpImpl& binOp, Index outer)
+      : Base(binOp.derived(),outer)
+    {}
+};
+
+/***************************************************************************
+* Implementation of inner-iterators
+***************************************************************************/
+
+// template<typename T> struct internal::func_is_conjunction { enum { ret = false }; };
+// template<typename T> struct internal::func_is_conjunction<internal::scalar_product_op<T> > { enum { ret = true }; };
+
+// TODO generalize the internal::scalar_product_op specialization to all conjunctions if any !
+
+namespace internal {
+
+// sparse - sparse  (generic)
+template<typename BinaryOp, typename Lhs, typename Rhs, typename Derived>
+class sparse_cwise_binary_op_inner_iterator_selector<BinaryOp, Lhs, Rhs, Derived, Sparse, Sparse>
+{
+    typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> CwiseBinaryXpr;
+    typedef typename traits<CwiseBinaryXpr>::Scalar Scalar;
+    typedef typename traits<CwiseBinaryXpr>::_LhsNested _LhsNested;
+    typedef typename traits<CwiseBinaryXpr>::_RhsNested _RhsNested;
+    typedef typename _LhsNested::InnerIterator LhsIterator;
+    typedef typename _RhsNested::InnerIterator RhsIterator;
+    typedef typename Lhs::Index Index;
+
+  public:
+
+    EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer)
+      : m_lhsIter(xpr.lhs(),outer), m_rhsIter(xpr.rhs(),outer), m_functor(xpr.functor())
+    {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE Derived& operator++()
+    {
+      if (m_lhsIter && m_rhsIter && (m_lhsIter.index() == m_rhsIter.index()))
+      {
+        m_id = m_lhsIter.index();
+        m_value = m_functor(m_lhsIter.value(), m_rhsIter.value());
+        ++m_lhsIter;
+        ++m_rhsIter;
+      }
+      else if (m_lhsIter && (!m_rhsIter || (m_lhsIter.index() < m_rhsIter.index())))
+      {
+        m_id = m_lhsIter.index();
+        m_value = m_functor(m_lhsIter.value(), Scalar(0));
+        ++m_lhsIter;
+      }
+      else if (m_rhsIter && (!m_lhsIter || (m_lhsIter.index() > m_rhsIter.index())))
+      {
+        m_id = m_rhsIter.index();
+        m_value = m_functor(Scalar(0), m_rhsIter.value());
+        ++m_rhsIter;
+      }
+      else
+      {
+        m_value = 0; // this is to avoid a compilation warning
+        m_id = -1;
+      }
+      return *static_cast<Derived*>(this);
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_value; }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index row() const { return Lhs::IsRowMajor ? m_lhsIter.row() : index(); }
+    EIGEN_STRONG_INLINE Index col() const { return Lhs::IsRowMajor ? index() : m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id>=0; }
+
+  protected:
+    LhsIterator m_lhsIter;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    Index m_id;
+};
+
+// sparse - sparse  (product)
+template<typename T, typename Lhs, typename Rhs, typename Derived>
+class sparse_cwise_binary_op_inner_iterator_selector<scalar_product_op<T>, Lhs, Rhs, Derived, Sparse, Sparse>
+{
+    typedef scalar_product_op<T> BinaryFunc;
+    typedef CwiseBinaryOp<BinaryFunc, Lhs, Rhs> CwiseBinaryXpr;
+    typedef typename CwiseBinaryXpr::Scalar Scalar;
+    typedef typename traits<CwiseBinaryXpr>::_LhsNested _LhsNested;
+    typedef typename _LhsNested::InnerIterator LhsIterator;
+    typedef typename traits<CwiseBinaryXpr>::_RhsNested _RhsNested;
+    typedef typename _RhsNested::InnerIterator RhsIterator;
+    typedef typename Lhs::Index Index;
+  public:
+
+    EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer)
+      : m_lhsIter(xpr.lhs(),outer), m_rhsIter(xpr.rhs(),outer), m_functor(xpr.functor())
+    {
+      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index()))
+      {
+        if (m_lhsIter.index() < m_rhsIter.index())
+          ++m_lhsIter;
+        else
+          ++m_rhsIter;
+      }
+    }
+
+    EIGEN_STRONG_INLINE Derived& operator++()
+    {
+      ++m_lhsIter;
+      ++m_rhsIter;
+      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index()))
+      {
+        if (m_lhsIter.index() < m_rhsIter.index())
+          ++m_lhsIter;
+        else
+          ++m_rhsIter;
+      }
+      return *static_cast<Derived*>(this);
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(m_lhsIter.value(), m_rhsIter.value()); }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_lhsIter.index(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return (m_lhsIter && m_rhsIter); }
+
+  protected:
+    LhsIterator m_lhsIter;
+    RhsIterator m_rhsIter;
+    const BinaryFunc& m_functor;
+};
+
+// sparse - dense  (product)
+template<typename T, typename Lhs, typename Rhs, typename Derived>
+class sparse_cwise_binary_op_inner_iterator_selector<scalar_product_op<T>, Lhs, Rhs, Derived, Sparse, Dense>
+{
+    typedef scalar_product_op<T> BinaryFunc;
+    typedef CwiseBinaryOp<BinaryFunc, Lhs, Rhs> CwiseBinaryXpr;
+    typedef typename CwiseBinaryXpr::Scalar Scalar;
+    typedef typename traits<CwiseBinaryXpr>::_LhsNested _LhsNested;
+    typedef typename traits<CwiseBinaryXpr>::RhsNested RhsNested;
+    typedef typename _LhsNested::InnerIterator LhsIterator;
+    typedef typename Lhs::Index Index;
+    enum { IsRowMajor = (int(Lhs::Flags)&RowMajorBit)==RowMajorBit };
+  public:
+
+    EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer)
+      : m_rhs(xpr.rhs()), m_lhsIter(xpr.lhs(),outer), m_functor(xpr.functor()), m_outer(outer)
+    {}
+
+    EIGEN_STRONG_INLINE Derived& operator++()
+    {
+      ++m_lhsIter;
+      return *static_cast<Derived*>(this);
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const
+    { return m_functor(m_lhsIter.value(),
+                       m_rhs.coeff(IsRowMajor?m_outer:m_lhsIter.index(),IsRowMajor?m_lhsIter.index():m_outer)); }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_lhsIter.index(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_lhsIter; }
+
+  protected:
+    const RhsNested m_rhs;
+    LhsIterator m_lhsIter;
+    const BinaryFunc m_functor;
+    const Index m_outer;
+};
+
+// sparse - dense  (product)
+template<typename T, typename Lhs, typename Rhs, typename Derived>
+class sparse_cwise_binary_op_inner_iterator_selector<scalar_product_op<T>, Lhs, Rhs, Derived, Dense, Sparse>
+{
+    typedef scalar_product_op<T> BinaryFunc;
+    typedef CwiseBinaryOp<BinaryFunc, Lhs, Rhs> CwiseBinaryXpr;
+    typedef typename CwiseBinaryXpr::Scalar Scalar;
+    typedef typename traits<CwiseBinaryXpr>::_RhsNested _RhsNested;
+    typedef typename _RhsNested::InnerIterator RhsIterator;
+    typedef typename Lhs::Index Index;
+
+    enum { IsRowMajor = (int(Rhs::Flags)&RowMajorBit)==RowMajorBit };
+  public:
+
+    EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer)
+      : m_xpr(xpr), m_rhsIter(xpr.rhs(),outer), m_functor(xpr.functor()), m_outer(outer)
+    {}
+
+    EIGEN_STRONG_INLINE Derived& operator++()
+    {
+      ++m_rhsIter;
+      return *static_cast<Derived*>(this);
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const
+    { return m_functor(m_xpr.lhs().coeff(IsRowMajor?m_outer:m_rhsIter.index(),IsRowMajor?m_rhsIter.index():m_outer), m_rhsIter.value()); }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_rhsIter.index(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_rhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_rhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_rhsIter; }
+
+  protected:
+    const CwiseBinaryXpr& m_xpr;
+    RhsIterator m_rhsIter;
+    const BinaryFunc& m_functor;
+    const Index m_outer;
+};
+
+} // end namespace internal
+
+/***************************************************************************
+* Implementation of SparseMatrixBase and SparseCwise functions/operators
+***************************************************************************/
+
+// template<typename Derived>
+// template<typename OtherDerived>
+// EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename internal::traits<Derived>::Scalar>,
+//                                  Derived, OtherDerived>
+// SparseMatrixBase<Derived>::operator-(const SparseMatrixBase<OtherDerived> &other) const
+// {
+//   return CwiseBinaryOp<internal::scalar_difference_op<Scalar>,
+//                        Derived, OtherDerived>(derived(), other.derived());
+// }
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+SparseMatrixBase<Derived>::operator-=(const SparseMatrixBase<OtherDerived> &other)
+{
+  return *this = derived() - other.derived();
+}
+
+// template<typename Derived>
+// template<typename OtherDerived>
+// EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename internal::traits<Derived>::Scalar>, Derived, OtherDerived>
+// SparseMatrixBase<Derived>::operator+(const SparseMatrixBase<OtherDerived> &other) const
+// {
+//   return CwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, OtherDerived>(derived(), other.derived());
+// }
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+SparseMatrixBase<Derived>::operator+=(const SparseMatrixBase<OtherDerived>& other)
+{
+  return *this = derived() + other.derived();
+}
+
+// template<typename ExpressionType>
+// template<typename OtherDerived>
+// EIGEN_STRONG_INLINE const EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE
+// SparseCwise<ExpressionType>::operator*(const SparseMatrixBase<OtherDerived> &other) const
+// {
+//   return EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE(_expression(), other.derived());
+// }
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE const EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE
+SparseMatrixBase<Derived>::cwiseProduct(const MatrixBase<OtherDerived> &other) const
+{
+  return EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE(derived(), other.derived());
+}
+
+// template<typename ExpressionType>
+// template<typename OtherDerived>
+// EIGEN_STRONG_INLINE const EIGEN_SPARSE_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op)
+// SparseCwise<ExpressionType>::operator/(const SparseMatrixBase<OtherDerived> &other) const
+// {
+//   return EIGEN_SPARSE_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op)(_expression(), other.derived());
+// }
+//
+// template<typename ExpressionType>
+// template<typename OtherDerived>
+// EIGEN_STRONG_INLINE const EIGEN_SPARSE_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op)
+// SparseCwise<ExpressionType>::operator/(const MatrixBase<OtherDerived> &other) const
+// {
+//   return EIGEN_SPARSE_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op)(_expression(), other.derived());
+// }
+
+// template<typename ExpressionType>
+// template<typename OtherDerived>
+// inline ExpressionType& SparseCwise<ExpressionType>::operator*=(const SparseMatrixBase<OtherDerived> &other)
+// {
+//   return m_matrix.const_cast_derived() = _expression() * other.derived();
+// }
+
+
+#endif // EIGEN_SPARSE_CWISE_BINARY_OP_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseCwiseUnaryOp.h b/extern/Eigen3/Eigen/src/Sparse/SparseCwiseUnaryOp.h
new file mode 100644
index 00000000000..aa068835fbb
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseCwiseUnaryOp.h
@@ -0,0 +1,146 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_CWISE_UNARY_OP_H
+#define EIGEN_SPARSE_CWISE_UNARY_OP_H
+
+// template<typename UnaryOp, typename MatrixType>
+// struct internal::traits<SparseCwiseUnaryOp<UnaryOp, MatrixType> > : internal::traits<MatrixType>
+// {
+//   typedef typename internal::result_of<
+//                      UnaryOp(typename MatrixType::Scalar)
+//                    >::type Scalar;
+//   typedef typename MatrixType::Nested MatrixTypeNested;
+//   typedef typename internal::remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
+//   enum {
+//     CoeffReadCost = _MatrixTypeNested::CoeffReadCost + internal::functor_traits<UnaryOp>::Cost
+//   };
+// };
+
+template<typename UnaryOp, typename MatrixType>
+class CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>
+  : public SparseMatrixBase<CwiseUnaryOp<UnaryOp, MatrixType> >
+{
+  public:
+
+    class InnerIterator;
+//     typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
+
+    typedef CwiseUnaryOp<UnaryOp, MatrixType> Derived;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Derived)
+};
+
+template<typename UnaryOp, typename MatrixType>
+class CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::InnerIterator
+{
+    typedef typename CwiseUnaryOpImpl::Scalar Scalar;
+    typedef typename internal::traits<Derived>::_XprTypeNested _MatrixTypeNested;
+    typedef typename _MatrixTypeNested::InnerIterator MatrixTypeIterator;
+    typedef typename MatrixType::Index Index;
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const CwiseUnaryOpImpl& unaryOp, Index outer)
+      : m_iter(unaryOp.derived().nestedExpression(),outer), m_functor(unaryOp.derived().functor())
+    {}
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    { ++m_iter; return *this; }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(m_iter.value()); }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_iter.index(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_iter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_iter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_iter; }
+
+  protected:
+    MatrixTypeIterator m_iter;
+    const UnaryOp m_functor;
+};
+
+template<typename ViewOp, typename MatrixType>
+class CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>
+  : public SparseMatrixBase<CwiseUnaryView<ViewOp, MatrixType> >
+{
+  public:
+
+    class InnerIterator;
+//     typedef typename internal::remove_reference<LhsNested>::type _LhsNested;
+
+    typedef CwiseUnaryView<ViewOp, MatrixType> Derived;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Derived)
+};
+
+template<typename ViewOp, typename MatrixType>
+class CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::InnerIterator
+{
+    typedef typename CwiseUnaryViewImpl::Scalar Scalar;
+    typedef typename internal::traits<Derived>::_MatrixTypeNested _MatrixTypeNested;
+    typedef typename _MatrixTypeNested::InnerIterator MatrixTypeIterator;
+    typedef typename MatrixType::Index Index;
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const CwiseUnaryViewImpl& unaryView, Index outer)
+      : m_iter(unaryView.derived().nestedExpression(),outer), m_functor(unaryView.derived().functor())
+    {}
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    { ++m_iter; return *this; }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(m_iter.value()); }
+    EIGEN_STRONG_INLINE Scalar& valueRef() { return m_functor(m_iter.valueRef()); }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_iter.index(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_iter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_iter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_iter; }
+
+  protected:
+    MatrixTypeIterator m_iter;
+    const ViewOp m_functor;
+};
+
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived&
+SparseMatrixBase<Derived>::operator*=(const Scalar& other)
+{
+  for (Index j=0; j<outerSize(); ++j)
+    for (typename Derived::InnerIterator i(derived(),j); i; ++i)
+      i.valueRef() *= other;
+  return derived();
+}
+
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived&
+SparseMatrixBase<Derived>::operator/=(const Scalar& other)
+{
+  for (Index j=0; j<outerSize(); ++j)
+    for (typename Derived::InnerIterator i(derived(),j); i; ++i)
+      i.valueRef() /= other;
+  return derived();
+}
+
+#endif // EIGEN_SPARSE_CWISE_UNARY_OP_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseDenseProduct.h b/extern/Eigen3/Eigen/src/Sparse/SparseDenseProduct.h
new file mode 100644
index 00000000000..0f77aa5be99
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseDenseProduct.h
@@ -0,0 +1,231 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEDENSEPRODUCT_H
+#define EIGEN_SPARSEDENSEPRODUCT_H
+
+template<typename Lhs, typename Rhs, int InnerSize> struct SparseDenseProductReturnType
+{
+  typedef SparseTimeDenseProduct<Lhs,Rhs> Type;
+};
+
+template<typename Lhs, typename Rhs> struct SparseDenseProductReturnType<Lhs,Rhs,1>
+{
+  typedef SparseDenseOuterProduct<Lhs,Rhs,false> Type;
+};
+
+template<typename Lhs, typename Rhs, int InnerSize> struct DenseSparseProductReturnType
+{
+  typedef DenseTimeSparseProduct<Lhs,Rhs> Type;
+};
+
+template<typename Lhs, typename Rhs> struct DenseSparseProductReturnType<Lhs,Rhs,1>
+{
+  typedef SparseDenseOuterProduct<Rhs,Lhs,true> Type;
+};
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, bool Tr>
+struct traits<SparseDenseOuterProduct<Lhs,Rhs,Tr> >
+{
+  typedef Sparse StorageKind;
+  typedef typename scalar_product_traits<typename traits<Lhs>::Scalar,
+                                            typename traits<Rhs>::Scalar>::ReturnType Scalar;
+  typedef typename Lhs::Index Index;
+  typedef typename Lhs::Nested LhsNested;
+  typedef typename Rhs::Nested RhsNested;
+  typedef typename remove_all<LhsNested>::type _LhsNested;
+  typedef typename remove_all<RhsNested>::type _RhsNested;
+
+  enum {
+    LhsCoeffReadCost = traits<_LhsNested>::CoeffReadCost,
+    RhsCoeffReadCost = traits<_RhsNested>::CoeffReadCost,
+
+    RowsAtCompileTime    = Tr ? int(traits<Rhs>::RowsAtCompileTime)     : int(traits<Lhs>::RowsAtCompileTime),
+    ColsAtCompileTime    = Tr ? int(traits<Lhs>::ColsAtCompileTime)     : int(traits<Rhs>::ColsAtCompileTime),
+    MaxRowsAtCompileTime = Tr ? int(traits<Rhs>::MaxRowsAtCompileTime)  : int(traits<Lhs>::MaxRowsAtCompileTime),
+    MaxColsAtCompileTime = Tr ? int(traits<Lhs>::MaxColsAtCompileTime)  : int(traits<Rhs>::MaxColsAtCompileTime),
+
+    Flags = Tr ? RowMajorBit : 0,
+
+    CoeffReadCost = LhsCoeffReadCost + RhsCoeffReadCost + NumTraits<Scalar>::MulCost
+  };
+};
+
+} // end namespace internal
+
+template<typename Lhs, typename Rhs, bool Tr>
+class SparseDenseOuterProduct
+ : public SparseMatrixBase<SparseDenseOuterProduct<Lhs,Rhs,Tr> >
+{
+  public:
+
+    typedef SparseMatrixBase<SparseDenseOuterProduct> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(SparseDenseOuterProduct)
+    typedef internal::traits<SparseDenseOuterProduct> Traits;
+
+  private:
+
+    typedef typename Traits::LhsNested LhsNested;
+    typedef typename Traits::RhsNested RhsNested;
+    typedef typename Traits::_LhsNested _LhsNested;
+    typedef typename Traits::_RhsNested _RhsNested;
+
+  public:
+
+    class InnerIterator;
+
+    EIGEN_STRONG_INLINE SparseDenseOuterProduct(const Lhs& lhs, const Rhs& rhs)
+      : m_lhs(lhs), m_rhs(rhs)
+    {
+      EIGEN_STATIC_ASSERT(!Tr,YOU_MADE_A_PROGRAMMING_MISTAKE);
+    }
+
+    EIGEN_STRONG_INLINE SparseDenseOuterProduct(const Rhs& rhs, const Lhs& lhs)
+      : m_lhs(lhs), m_rhs(rhs)
+    {
+      EIGEN_STATIC_ASSERT(Tr,YOU_MADE_A_PROGRAMMING_MISTAKE);
+    }
+
+    EIGEN_STRONG_INLINE Index rows() const { return Tr ? m_rhs.rows() : m_lhs.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return Tr ? m_lhs.cols() : m_rhs.cols(); }
+
+    EIGEN_STRONG_INLINE const _LhsNested& lhs() const { return m_lhs; }
+    EIGEN_STRONG_INLINE const _RhsNested& rhs() const { return m_rhs; }
+
+  protected:
+    LhsNested m_lhs;
+    RhsNested m_rhs;
+};
+
+template<typename Lhs, typename Rhs, bool Transpose>
+class SparseDenseOuterProduct<Lhs,Rhs,Transpose>::InnerIterator : public _LhsNested::InnerIterator
+{
+    typedef typename _LhsNested::InnerIterator Base;
+  public:
+    EIGEN_STRONG_INLINE InnerIterator(const SparseDenseOuterProduct& prod, Index outer)
+      : Base(prod.lhs(), 0), m_outer(outer), m_factor(prod.rhs().coeff(outer))
+    {
+    }
+
+    inline Index outer() const { return m_outer; }
+    inline Index row() const { return Transpose ? Base::row() : m_outer; }
+    inline Index col() const { return Transpose ? m_outer : Base::row(); }
+
+    inline Scalar value() const { return Base::value() * m_factor; }
+
+  protected:
+    int m_outer;
+    Scalar m_factor;
+};
+
+namespace internal {
+template<typename Lhs, typename Rhs>
+struct traits<SparseTimeDenseProduct<Lhs,Rhs> >
+ : traits<ProductBase<SparseTimeDenseProduct<Lhs,Rhs>, Lhs, Rhs> >
+{
+  typedef Dense StorageKind;
+  typedef MatrixXpr XprKind;
+};
+} // end namespace internal
+
+template<typename Lhs, typename Rhs>
+class SparseTimeDenseProduct
+  : public ProductBase<SparseTimeDenseProduct<Lhs,Rhs>, Lhs, Rhs>
+{
+  public:
+    EIGEN_PRODUCT_PUBLIC_INTERFACE(SparseTimeDenseProduct)
+
+    SparseTimeDenseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
+    {}
+
+    template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
+    {
+      typedef typename internal::remove_all<Lhs>::type _Lhs;
+      typedef typename internal::remove_all<Rhs>::type _Rhs;
+      typedef typename _Lhs::InnerIterator LhsInnerIterator;
+      enum { LhsIsRowMajor = (_Lhs::Flags&RowMajorBit)==RowMajorBit };
+      for(Index j=0; j<m_lhs.outerSize(); ++j)
+      {
+        typename Rhs::Scalar rhs_j = alpha * m_rhs.coeff(LhsIsRowMajor ? 0 : j,0);
+        typename Dest::RowXpr dest_j(dest.row(LhsIsRowMajor ? j : 0));
+        for(LhsInnerIterator it(m_lhs,j); it ;++it)
+        {
+          if(LhsIsRowMajor)                   dest_j += (alpha*it.value()) * m_rhs.row(it.index());
+          else if(Rhs::ColsAtCompileTime==1)  dest.coeffRef(it.index()) += it.value() * rhs_j;
+          else                                dest.row(it.index()) += (alpha*it.value()) * m_rhs.row(j);
+        }
+      }
+    }
+
+  private:
+    SparseTimeDenseProduct& operator=(const SparseTimeDenseProduct&);
+};
+
+
+// dense = dense * sparse
+namespace internal {
+template<typename Lhs, typename Rhs>
+struct traits<DenseTimeSparseProduct<Lhs,Rhs> >
+ : traits<ProductBase<DenseTimeSparseProduct<Lhs,Rhs>, Lhs, Rhs> >
+{
+  typedef Dense StorageKind;
+};
+} // end namespace internal
+
+template<typename Lhs, typename Rhs>
+class DenseTimeSparseProduct
+  : public ProductBase<DenseTimeSparseProduct<Lhs,Rhs>, Lhs, Rhs>
+{
+  public:
+    EIGEN_PRODUCT_PUBLIC_INTERFACE(DenseTimeSparseProduct)
+
+    DenseTimeSparseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
+    {}
+
+    template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
+    {
+      typedef typename internal::remove_all<Rhs>::type _Rhs;
+      typedef typename _Rhs::InnerIterator RhsInnerIterator;
+      enum { RhsIsRowMajor = (_Rhs::Flags&RowMajorBit)==RowMajorBit };
+      for(Index j=0; j<m_rhs.outerSize(); ++j)
+        for(RhsInnerIterator i(m_rhs,j); i; ++i)
+          dest.col(RhsIsRowMajor ? i.index() : j) += (alpha*i.value()) * m_lhs.col(RhsIsRowMajor ? j : i.index());
+    }
+
+  private:
+    DenseTimeSparseProduct& operator=(const DenseTimeSparseProduct&);
+};
+
+// sparse * dense
+template<typename Derived>
+template<typename OtherDerived>
+inline const typename SparseDenseProductReturnType<Derived,OtherDerived>::Type
+SparseMatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
+{
+  return typename SparseDenseProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
+}
+
+#endif // EIGEN_SPARSEDENSEPRODUCT_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseDiagonalProduct.h b/extern/Eigen3/Eigen/src/Sparse/SparseDiagonalProduct.h
new file mode 100644
index 00000000000..fb9a29c051b
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseDiagonalProduct.h
@@ -0,0 +1,195 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_DIAGONAL_PRODUCT_H
+#define EIGEN_SPARSE_DIAGONAL_PRODUCT_H
+
+// The product of a diagonal matrix with a sparse matrix can be easily
+// implemented using expression template.
+// We have two consider very different cases:
+// 1 - diag * row-major sparse
+//     => each inner vector <=> scalar * sparse vector product
+//     => so we can reuse CwiseUnaryOp::InnerIterator
+// 2 - diag * col-major sparse
+//     => each inner vector <=> densevector * sparse vector cwise product
+//     => again, we can reuse specialization of CwiseBinaryOp::InnerIterator
+//        for that particular case
+// The two other cases are symmetric.
+
+namespace internal {
+
+template<typename Lhs, typename Rhs>
+struct traits<SparseDiagonalProduct<Lhs, Rhs> >
+{
+  typedef typename remove_all<Lhs>::type _Lhs;
+  typedef typename remove_all<Rhs>::type _Rhs;
+  typedef typename _Lhs::Scalar Scalar;
+  typedef typename promote_index_type<typename traits<Lhs>::Index,
+                                         typename traits<Rhs>::Index>::type Index;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = _Lhs::RowsAtCompileTime,
+    ColsAtCompileTime = _Rhs::ColsAtCompileTime,
+
+    MaxRowsAtCompileTime = _Lhs::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = _Rhs::MaxColsAtCompileTime,
+
+    SparseFlags = is_diagonal<_Lhs>::ret ? int(_Rhs::Flags) : int(_Lhs::Flags),
+    Flags = (SparseFlags&RowMajorBit),
+    CoeffReadCost = Dynamic
+  };
+};
+
+enum {SDP_IsDiagonal, SDP_IsSparseRowMajor, SDP_IsSparseColMajor};
+template<typename Lhs, typename Rhs, typename SparseDiagonalProductType, int RhsMode, int LhsMode>
+class sparse_diagonal_product_inner_iterator_selector;
+
+} // end namespace internal
+
+template<typename Lhs, typename Rhs>
+class SparseDiagonalProduct
+  : public SparseMatrixBase<SparseDiagonalProduct<Lhs,Rhs> >,
+    internal::no_assignment_operator
+{
+    typedef typename Lhs::Nested LhsNested;
+    typedef typename Rhs::Nested RhsNested;
+
+    typedef typename internal::remove_all<LhsNested>::type _LhsNested;
+    typedef typename internal::remove_all<RhsNested>::type _RhsNested;
+
+    enum {
+      LhsMode = internal::is_diagonal<_LhsNested>::ret ? internal::SDP_IsDiagonal
+              : (_LhsNested::Flags&RowMajorBit) ? internal::SDP_IsSparseRowMajor : internal::SDP_IsSparseColMajor,
+      RhsMode = internal::is_diagonal<_RhsNested>::ret ? internal::SDP_IsDiagonal
+              : (_RhsNested::Flags&RowMajorBit) ? internal::SDP_IsSparseRowMajor : internal::SDP_IsSparseColMajor
+    };
+
+  public:
+
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseDiagonalProduct)
+
+    typedef internal::sparse_diagonal_product_inner_iterator_selector
+                <_LhsNested,_RhsNested,SparseDiagonalProduct,LhsMode,RhsMode> InnerIterator;
+
+    EIGEN_STRONG_INLINE SparseDiagonalProduct(const Lhs& lhs, const Rhs& rhs)
+      : m_lhs(lhs), m_rhs(rhs)
+    {
+      eigen_assert(lhs.cols() == rhs.rows() && "invalid sparse matrix * diagonal matrix product");
+    }
+
+    EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); }
+
+    EIGEN_STRONG_INLINE const _LhsNested& lhs() const { return m_lhs; }
+    EIGEN_STRONG_INLINE const _RhsNested& rhs() const { return m_rhs; }
+
+  protected:
+    LhsNested m_lhs;
+    RhsNested m_rhs;
+};
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, typename SparseDiagonalProductType>
+class sparse_diagonal_product_inner_iterator_selector
+<Lhs,Rhs,SparseDiagonalProductType,SDP_IsDiagonal,SDP_IsSparseRowMajor>
+  : public CwiseUnaryOp<scalar_multiple_op<typename Lhs::Scalar>,const Rhs>::InnerIterator
+{
+    typedef typename CwiseUnaryOp<scalar_multiple_op<typename Lhs::Scalar>,const Rhs>::InnerIterator Base;
+    typedef typename Lhs::Index Index;
+  public:
+    inline sparse_diagonal_product_inner_iterator_selector(
+              const SparseDiagonalProductType& expr, Index outer)
+      : Base(expr.rhs()*(expr.lhs().diagonal().coeff(outer)), outer)
+    {}
+};
+
+template<typename Lhs, typename Rhs, typename SparseDiagonalProductType>
+class sparse_diagonal_product_inner_iterator_selector
+<Lhs,Rhs,SparseDiagonalProductType,SDP_IsDiagonal,SDP_IsSparseColMajor>
+  : public CwiseBinaryOp<
+      scalar_product_op<typename Lhs::Scalar>,
+      SparseInnerVectorSet<Rhs,1>,
+      typename Lhs::DiagonalVectorType>::InnerIterator
+{
+    typedef typename CwiseBinaryOp<
+      scalar_product_op<typename Lhs::Scalar>,
+      SparseInnerVectorSet<Rhs,1>,
+      typename Lhs::DiagonalVectorType>::InnerIterator Base;
+    typedef typename Lhs::Index Index;
+  public:
+    inline sparse_diagonal_product_inner_iterator_selector(
+              const SparseDiagonalProductType& expr, Index outer)
+      : Base(expr.rhs().innerVector(outer) .cwiseProduct(expr.lhs().diagonal()), 0)
+    {}
+};
+
+template<typename Lhs, typename Rhs, typename SparseDiagonalProductType>
+class sparse_diagonal_product_inner_iterator_selector
+<Lhs,Rhs,SparseDiagonalProductType,SDP_IsSparseColMajor,SDP_IsDiagonal>
+  : public CwiseUnaryOp<scalar_multiple_op<typename Rhs::Scalar>,const Lhs>::InnerIterator
+{
+    typedef typename CwiseUnaryOp<scalar_multiple_op<typename Rhs::Scalar>,const Lhs>::InnerIterator Base;
+    typedef typename Lhs::Index Index;
+  public:
+    inline sparse_diagonal_product_inner_iterator_selector(
+              const SparseDiagonalProductType& expr, Index outer)
+      : Base(expr.lhs()*expr.rhs().diagonal().coeff(outer), outer)
+    {}
+};
+
+template<typename Lhs, typename Rhs, typename SparseDiagonalProductType>
+class sparse_diagonal_product_inner_iterator_selector
+<Lhs,Rhs,SparseDiagonalProductType,SDP_IsSparseRowMajor,SDP_IsDiagonal>
+  : public CwiseBinaryOp<
+      scalar_product_op<typename Rhs::Scalar>,
+      SparseInnerVectorSet<Lhs,1>,
+      Transpose<const typename Rhs::DiagonalVectorType> >::InnerIterator
+{
+    typedef typename CwiseBinaryOp<
+      scalar_product_op<typename Rhs::Scalar>,
+      SparseInnerVectorSet<Lhs,1>,
+      Transpose<const typename Rhs::DiagonalVectorType> >::InnerIterator Base;
+    typedef typename Lhs::Index Index;
+  public:
+    inline sparse_diagonal_product_inner_iterator_selector(
+              const SparseDiagonalProductType& expr, Index outer)
+      : Base(expr.lhs().innerVector(outer) .cwiseProduct(expr.rhs().diagonal().transpose()), 0)
+    {}
+};
+
+} // end namespace internal
+
+// SparseMatrixBase functions
+
+template<typename Derived>
+template<typename OtherDerived>
+const SparseDiagonalProduct<Derived,OtherDerived>
+SparseMatrixBase<Derived>::operator*(const DiagonalBase<OtherDerived> &other) const
+{
+  return SparseDiagonalProduct<Derived,OtherDerived>(this->derived(), other.derived());
+}
+
+#endif // EIGEN_SPARSE_DIAGONAL_PRODUCT_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseDot.h b/extern/Eigen3/Eigen/src/Sparse/SparseDot.h
new file mode 100644
index 00000000000..1f10f71a402
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseDot.h
@@ -0,0 +1,97 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_DOT_H
+#define EIGEN_SPARSE_DOT_H
+
+template<typename Derived>
+template<typename OtherDerived>
+typename internal::traits<Derived>::Scalar
+SparseMatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(size() == other.size());
+  eigen_assert(other.size()>0 && "you are using a non initialized vector");
+
+  typename Derived::InnerIterator i(derived(),0);
+  Scalar res = 0;
+  while (i)
+  {
+    res += internal::conj(i.value()) * other.coeff(i.index());
+    ++i;
+  }
+  return res;
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+typename internal::traits<Derived>::Scalar
+SparseMatrixBase<Derived>::dot(const SparseMatrixBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(size() == other.size());
+
+  typename Derived::InnerIterator i(derived(),0);
+  typename OtherDerived::InnerIterator j(other.derived(),0);
+  Scalar res = 0;
+  while (i && j)
+  {
+    if (i.index()==j.index())
+    {
+      res += internal::conj(i.value()) * j.value();
+      ++i; ++j;
+    }
+    else if (i.index()<j.index())
+      ++i;
+    else
+      ++j;
+  }
+  return res;
+}
+
+template<typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+SparseMatrixBase<Derived>::squaredNorm() const
+{
+  return internal::real((*this).cwiseAbs2().sum());
+}
+
+template<typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+SparseMatrixBase<Derived>::norm() const
+{
+  return internal::sqrt(squaredNorm());
+}
+
+#endif // EIGEN_SPARSE_DOT_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseFuzzy.h b/extern/Eigen3/Eigen/src/Sparse/SparseFuzzy.h
new file mode 100644
index 00000000000..f00b3d6469b
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseFuzzy.h
@@ -0,0 +1,41 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_FUZZY_H
+#define EIGEN_SPARSE_FUZZY_H
+
+// template<typename Derived>
+// template<typename OtherDerived>
+// bool SparseMatrixBase<Derived>::isApprox(
+//   const OtherDerived& other,
+//   typename NumTraits<Scalar>::Real prec
+// ) const
+// {
+//   const typename internal::nested<Derived,2>::type nested(derived());
+//   const typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
+//   return    (nested - otherNested).cwise().abs2().sum()
+//          <= prec * prec * (std::min)(nested.cwise().abs2().sum(), otherNested.cwise().abs2().sum());
+// }
+
+#endif // EIGEN_SPARSE_FUZZY_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseMatrix.h b/extern/Eigen3/Eigen/src/Sparse/SparseMatrix.h
new file mode 100644
index 00000000000..0e175ec6e71
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseMatrix.h
@@ -0,0 +1,651 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEMATRIX_H
+#define EIGEN_SPARSEMATRIX_H
+
+/** \ingroup Sparse_Module
+  *
+  * \class SparseMatrix
+  *
+  * \brief The main sparse matrix class
+  *
+  * This class implements a sparse matrix using the very common compressed row/column storage
+  * scheme.
+  *
+  * \tparam _Scalar the scalar type, i.e. the type of the coefficients
+  * \tparam _Options Union of bit flags controlling the storage scheme. Currently the only possibility
+  *                 is RowMajor. The default is 0 which means column-major.
+  * \tparam _Index the type of the indices. Default is \c int.
+  *
+  * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_SPARSEMATRIX_PLUGIN.
+  */
+
+namespace internal {
+template<typename _Scalar, int _Options, typename _Index>
+struct traits<SparseMatrix<_Scalar, _Options, _Index> >
+{
+  typedef _Scalar Scalar;
+  typedef _Index Index;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = Dynamic,
+    Flags = _Options | NestByRefBit | LvalueBit,
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    SupportedAccessPatterns = InnerRandomAccessPattern
+  };
+};
+
+} // end namespace internal
+
+template<typename _Scalar, int _Options, typename _Index>
+class SparseMatrix
+  : public SparseMatrixBase<SparseMatrix<_Scalar, _Options, _Index> >
+{
+  public:
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseMatrix)
+//     using Base::operator=;
+    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseMatrix, +=)
+    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseMatrix, -=)
+    // FIXME: why are these operator already alvailable ???
+    // EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(SparseMatrix, *=)
+    // EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(SparseMatrix, /=)
+
+    typedef MappedSparseMatrix<Scalar,Flags> Map;
+    using Base::IsRowMajor;
+    typedef CompressedStorage<Scalar,Index> Storage;
+    enum {
+      Options = _Options
+    };
+
+  protected:
+
+    typedef SparseMatrix<Scalar,(Flags&~RowMajorBit)|(IsRowMajor?RowMajorBit:0)> TransposedSparseMatrix;
+
+    Index m_outerSize;
+    Index m_innerSize;
+    Index* m_outerIndex;
+    CompressedStorage<Scalar,Index> m_data;
+
+  public:
+
+    inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
+    inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
+
+    inline Index innerSize() const { return m_innerSize; }
+    inline Index outerSize() const { return m_outerSize; }
+    inline Index innerNonZeros(Index j) const { return m_outerIndex[j+1]-m_outerIndex[j]; }
+
+    inline const Scalar* _valuePtr() const { return &m_data.value(0); }
+    inline Scalar* _valuePtr() { return &m_data.value(0); }
+
+    inline const Index* _innerIndexPtr() const { return &m_data.index(0); }
+    inline Index* _innerIndexPtr() { return &m_data.index(0); }
+
+    inline const Index* _outerIndexPtr() const { return m_outerIndex; }
+    inline Index* _outerIndexPtr() { return m_outerIndex; }
+
+    inline Storage& data() { return m_data; }
+    inline const Storage& data() const { return m_data; }
+
+    inline Scalar coeff(Index row, Index col) const
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+      return m_data.atInRange(m_outerIndex[outer], m_outerIndex[outer+1], inner);
+    }
+
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index start = m_outerIndex[outer];
+      Index end = m_outerIndex[outer+1];
+      eigen_assert(end>=start && "you probably called coeffRef on a non finalized matrix");
+      eigen_assert(end>start && "coeffRef cannot be called on a zero coefficient");
+      const Index p = m_data.searchLowerIndex(start,end-1,inner);
+      eigen_assert((p<end) && (m_data.index(p)==inner) && "coeffRef cannot be called on a zero coefficient");
+      return m_data.value(p);
+    }
+
+  public:
+
+    class InnerIterator;
+
+    /** Removes all non zeros */
+    inline void setZero()
+    {
+      m_data.clear();
+      memset(m_outerIndex, 0, (m_outerSize+1)*sizeof(Index));
+    }
+
+    /** \returns the number of non zero coefficients */
+    inline Index nonZeros() const  { return static_cast<Index>(m_data.size()); }
+
+    /** Preallocates \a reserveSize non zeros */
+    inline void reserve(Index reserveSize)
+    {
+      m_data.reserve(reserveSize);
+    }
+
+    //--- low level purely coherent filling ---
+
+    /** \returns a reference to the non zero coefficient at position \a row, \a col assuming that:
+      * - the nonzero does not already exist
+      * - the new coefficient is the last one according to the storage order
+      *
+      * Before filling a given inner vector you must call the statVec(Index) function.
+      *
+      * After an insertion session, you should call the finalize() function.
+      *
+      * \sa insert, insertBackByOuterInner, startVec */
+    inline Scalar& insertBack(Index row, Index col)
+    {
+      return insertBackByOuterInner(IsRowMajor?row:col, IsRowMajor?col:row);
+    }
+
+    /** \sa insertBack, startVec */
+    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
+    {
+      eigen_assert(size_t(m_outerIndex[outer+1]) == m_data.size() && "Invalid ordered insertion (invalid outer index)");
+      eigen_assert( (m_outerIndex[outer+1]-m_outerIndex[outer]==0 || m_data.index(m_data.size()-1)<inner) && "Invalid ordered insertion (invalid inner index)");
+      Index p = m_outerIndex[outer+1];
+      ++m_outerIndex[outer+1];
+      m_data.append(0, inner);
+      return m_data.value(p);
+    }
+
+    /** \warning use it only if you know what you are doing */
+    inline Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner)
+    {
+      Index p = m_outerIndex[outer+1];
+      ++m_outerIndex[outer+1];
+      m_data.append(0, inner);
+      return m_data.value(p);
+    }
+
+    /** \sa insertBack, insertBackByOuterInner */
+    inline void startVec(Index outer)
+    {
+      eigen_assert(m_outerIndex[outer]==int(m_data.size()) && "You must call startVec for each inner vector sequentially");
+      eigen_assert(m_outerIndex[outer+1]==0 && "You must call startVec for each inner vector sequentially");
+      m_outerIndex[outer+1] = m_outerIndex[outer];
+    }
+
+    //---
+
+    /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
+      * The non zero coefficient must \b not already exist.
+      *
+      * \warning This function can be extremely slow if the non zero coefficients
+      * are not inserted in a coherent order.
+      *
+      * After an insertion session, you should call the finalize() function.
+      */
+    EIGEN_DONT_INLINE Scalar& insert(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index previousOuter = outer;
+      if (m_outerIndex[outer+1]==0)
+      {
+        // we start a new inner vector
+        while (previousOuter>=0 && m_outerIndex[previousOuter]==0)
+        {
+          m_outerIndex[previousOuter] = static_cast<Index>(m_data.size());
+          --previousOuter;
+        }
+        m_outerIndex[outer+1] = m_outerIndex[outer];
+      }
+
+      // here we have to handle the tricky case where the outerIndex array
+      // starts with: [ 0 0 0 0 0 1 ...] and we are inserting in, e.g.,
+      // the 2nd inner vector...
+      bool isLastVec = (!(previousOuter==-1 && m_data.size()!=0))
+                    && (size_t(m_outerIndex[outer+1]) == m_data.size());
+
+      size_t startId = m_outerIndex[outer];
+      // FIXME let's make sure sizeof(long int) == sizeof(size_t)
+      size_t p = m_outerIndex[outer+1];
+      ++m_outerIndex[outer+1];
+
+      float reallocRatio = 1;
+      if (m_data.allocatedSize()<=m_data.size())
+      {
+        // if there is no preallocated memory, let's reserve a minimum of 32 elements
+        if (m_data.size()==0)
+        {
+          m_data.reserve(32);
+        }
+        else
+        {
+          // we need to reallocate the data, to reduce multiple reallocations
+          // we use a smart resize algorithm based on the current filling ratio
+          // in addition, we use float to avoid integers overflows
+          float nnzEstimate = float(m_outerIndex[outer])*float(m_outerSize)/float(outer+1);
+          reallocRatio = (nnzEstimate-float(m_data.size()))/float(m_data.size());
+          // furthermore we bound the realloc ratio to:
+          //   1) reduce multiple minor realloc when the matrix is almost filled
+          //   2) avoid to allocate too much memory when the matrix is almost empty
+          reallocRatio = (std::min)((std::max)(reallocRatio,1.5f),8.f);
+        }
+      }
+      m_data.resize(m_data.size()+1,reallocRatio);
+
+      if (!isLastVec)
+      {
+        if (previousOuter==-1)
+        {
+          // oops wrong guess.
+          // let's correct the outer offsets
+          for (Index k=0; k<=(outer+1); ++k)
+            m_outerIndex[k] = 0;
+          Index k=outer+1;
+          while(m_outerIndex[k]==0)
+            m_outerIndex[k++] = 1;
+          while (k<=m_outerSize && m_outerIndex[k]!=0)
+            m_outerIndex[k++]++;
+          p = 0;
+          --k;
+          k = m_outerIndex[k]-1;
+          while (k>0)
+          {
+            m_data.index(k) = m_data.index(k-1);
+            m_data.value(k) = m_data.value(k-1);
+            k--;
+          }
+        }
+        else
+        {
+          // we are not inserting into the last inner vec
+          // update outer indices:
+          Index j = outer+2;
+          while (j<=m_outerSize && m_outerIndex[j]!=0)
+            m_outerIndex[j++]++;
+          --j;
+          // shift data of last vecs:
+          Index k = m_outerIndex[j]-1;
+          while (k>=Index(p))
+          {
+            m_data.index(k) = m_data.index(k-1);
+            m_data.value(k) = m_data.value(k-1);
+            k--;
+          }
+        }
+      }
+
+      while ( (p > startId) && (m_data.index(p-1) > inner) )
+      {
+        m_data.index(p) = m_data.index(p-1);
+        m_data.value(p) = m_data.value(p-1);
+        --p;
+      }
+
+      m_data.index(p) = inner;
+      return (m_data.value(p) = 0);
+    }
+
+
+
+
+    /** Must be called after inserting a set of non zero entries.
+      */
+    inline void finalize()
+    {
+      Index size = static_cast<Index>(m_data.size());
+      Index i = m_outerSize;
+      // find the last filled column
+      while (i>=0 && m_outerIndex[i]==0)
+        --i;
+      ++i;
+      while (i<=m_outerSize)
+      {
+        m_outerIndex[i] = size;
+        ++i;
+      }
+    }
+
+    /** Suppress all nonzeros which are smaller than \a reference under the tolerence \a epsilon */
+    void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision())
+    {
+      prune(default_prunning_func(reference,epsilon));
+    }
+    
+    /** Suppress all nonzeros which do not satisfy the predicate \a keep.
+      * The functor type \a KeepFunc must implement the following function:
+      * \code
+      * bool operator() (const Index& row, const Index& col, const Scalar& value) const;
+      * \endcode
+      * \sa prune(Scalar,RealScalar)
+      */
+    template<typename KeepFunc>
+    void prune(const KeepFunc& keep = KeepFunc())
+    {
+      Index k = 0;
+      for(Index j=0; j<m_outerSize; ++j)
+      {
+        Index previousStart = m_outerIndex[j];
+        m_outerIndex[j] = k;
+        Index end = m_outerIndex[j+1];
+        for(Index i=previousStart; i<end; ++i)
+        {
+          if(keep(IsRowMajor?j:m_data.index(i), IsRowMajor?m_data.index(i):j, m_data.value(i)))
+          {
+            m_data.value(k) = m_data.value(i);
+            m_data.index(k) = m_data.index(i);
+            ++k;
+          }
+        }
+      }
+      m_outerIndex[m_outerSize] = k;
+      m_data.resize(k,0);
+    }
+
+    /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero
+      * \sa resizeNonZeros(Index), reserve(), setZero()
+      */
+    void resize(Index rows, Index cols)
+    {
+      const Index outerSize = IsRowMajor ? rows : cols;
+      m_innerSize = IsRowMajor ? cols : rows;
+      m_data.clear();
+      if (m_outerSize != outerSize || m_outerSize==0)
+      {
+        delete[] m_outerIndex;
+        m_outerIndex = new Index [outerSize+1];
+        m_outerSize = outerSize;
+      }
+      memset(m_outerIndex, 0, (m_outerSize+1)*sizeof(Index));
+    }
+
+    /** Low level API
+      * Resize the nonzero vector to \a size */
+    void resizeNonZeros(Index size)
+    {
+      m_data.resize(size);
+    }
+
+    /** Default constructor yielding an empty \c 0 \c x \c 0 matrix */
+    inline SparseMatrix()
+      : m_outerSize(-1), m_innerSize(0), m_outerIndex(0)
+    {
+      resize(0, 0);
+    }
+
+    /** Constructs a \a rows \c x \a cols empty matrix */
+    inline SparseMatrix(Index rows, Index cols)
+      : m_outerSize(0), m_innerSize(0), m_outerIndex(0)
+    {
+      resize(rows, cols);
+    }
+
+    /** Constructs a sparse matrix from the sparse expression \a other */
+    template<typename OtherDerived>
+    inline SparseMatrix(const SparseMatrixBase<OtherDerived>& other)
+      : m_outerSize(0), m_innerSize(0), m_outerIndex(0)
+    {
+      *this = other.derived();
+    }
+
+    /** Copy constructor */
+    inline SparseMatrix(const SparseMatrix& other)
+      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0)
+    {
+      *this = other.derived();
+    }
+
+    /** Swap the content of two sparse matrices of same type (optimization) */
+    inline void swap(SparseMatrix& other)
+    {
+      //EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n");
+      std::swap(m_outerIndex, other.m_outerIndex);
+      std::swap(m_innerSize, other.m_innerSize);
+      std::swap(m_outerSize, other.m_outerSize);
+      m_data.swap(other.m_data);
+    }
+
+    inline SparseMatrix& operator=(const SparseMatrix& other)
+    {
+//       std::cout << "SparseMatrix& operator=(const SparseMatrix& other)\n";
+      if (other.isRValue())
+      {
+        swap(other.const_cast_derived());
+      }
+      else
+      {
+        resize(other.rows(), other.cols());
+        memcpy(m_outerIndex, other.m_outerIndex, (m_outerSize+1)*sizeof(Index));
+        m_data = other.m_data;
+      }
+      return *this;
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename Lhs, typename Rhs>
+    inline SparseMatrix& operator=(const SparseSparseProduct<Lhs,Rhs>& product)
+    { return Base::operator=(product); }
+    
+    template<typename OtherDerived>
+    inline SparseMatrix& operator=(const ReturnByValue<OtherDerived>& other)
+    { return Base::operator=(other); }
+    
+    template<typename OtherDerived>
+    inline SparseMatrix& operator=(const EigenBase<OtherDerived>& other)
+    { return Base::operator=(other); }
+    #endif
+
+    template<typename OtherDerived>
+    EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+      const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+      if (needToTranspose)
+      {
+        // two passes algorithm:
+        //  1 - compute the number of coeffs per dest inner vector
+        //  2 - do the actual copy/eval
+        // Since each coeff of the rhs has to be evaluated twice, let's evaluate it if needed
+        typedef typename internal::nested<OtherDerived,2>::type OtherCopy;
+        typedef typename internal::remove_all<OtherCopy>::type _OtherCopy;
+        OtherCopy otherCopy(other.derived());
+
+        resize(other.rows(), other.cols());
+        Eigen::Map<Matrix<Index, Dynamic, 1> > (m_outerIndex,outerSize()).setZero();
+        // pass 1
+        // FIXME the above copy could be merged with that pass
+        for (Index j=0; j<otherCopy.outerSize(); ++j)
+          for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
+            ++m_outerIndex[it.index()];
+
+        // prefix sum
+        Index count = 0;
+        VectorXi positions(outerSize());
+        for (Index j=0; j<outerSize(); ++j)
+        {
+          Index tmp = m_outerIndex[j];
+          m_outerIndex[j] = count;
+          positions[j] = count;
+          count += tmp;
+        }
+        m_outerIndex[outerSize()] = count;
+        // alloc
+        m_data.resize(count);
+        // pass 2
+        for (Index j=0; j<otherCopy.outerSize(); ++j)
+        {
+          for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
+          {
+            Index pos = positions[it.index()]++;
+            m_data.index(pos) = j;
+            m_data.value(pos) = it.value();
+          }
+        }
+        return *this;
+      }
+      else
+      {
+        // there is no special optimization
+        return SparseMatrixBase<SparseMatrix>::operator=(other.derived());
+      }
+    }
+
+    friend std::ostream & operator << (std::ostream & s, const SparseMatrix& m)
+    {
+      EIGEN_DBG_SPARSE(
+        s << "Nonzero entries:\n";
+        for (Index i=0; i<m.nonZeros(); ++i)
+        {
+          s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
+        }
+        s << std::endl;
+        s << std::endl;
+        s << "Column pointers:\n";
+        for (Index i=0; i<m.outerSize(); ++i)
+        {
+          s << m.m_outerIndex[i] << " ";
+        }
+        s << " $" << std::endl;
+        s << std::endl;
+      );
+      s << static_cast<const SparseMatrixBase<SparseMatrix>&>(m);
+      return s;
+    }
+
+    /** Destructor */
+    inline ~SparseMatrix()
+    {
+      delete[] m_outerIndex;
+    }
+
+    /** Overloaded for performance */
+    Scalar sum() const;
+
+  public:
+
+    /** \deprecated use setZero() and reserve()
+      * Initializes the filling process of \c *this.
+      * \param reserveSize approximate number of nonzeros
+      * Note that the matrix \c *this is zero-ed.
+      */
+    EIGEN_DEPRECATED void startFill(Index reserveSize = 1000)
+    {
+      setZero();
+      m_data.reserve(reserveSize);
+    }
+
+    /** \deprecated use insert()
+      * Like fill() but with random inner coordinates.
+      */
+    EIGEN_DEPRECATED Scalar& fillrand(Index row, Index col)
+    {
+      return insert(row,col);
+    }
+
+    /** \deprecated use insert()
+      */
+    EIGEN_DEPRECATED Scalar& fill(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      if (m_outerIndex[outer+1]==0)
+      {
+        // we start a new inner vector
+        Index i = outer;
+        while (i>=0 && m_outerIndex[i]==0)
+        {
+          m_outerIndex[i] = m_data.size();
+          --i;
+        }
+        m_outerIndex[outer+1] = m_outerIndex[outer];
+      }
+      else
+      {
+        eigen_assert(m_data.index(m_data.size()-1)<inner && "wrong sorted insertion");
+      }
+//       std::cerr << size_t(m_outerIndex[outer+1]) << " == " << m_data.size() << "\n";
+      assert(size_t(m_outerIndex[outer+1]) == m_data.size());
+      Index p = m_outerIndex[outer+1];
+      ++m_outerIndex[outer+1];
+
+      m_data.append(0, inner);
+      return m_data.value(p);
+    }
+
+    /** \deprecated use finalize */
+    EIGEN_DEPRECATED void endFill() { finalize(); }
+    
+#   ifdef EIGEN_SPARSEMATRIX_PLUGIN
+#     include EIGEN_SPARSEMATRIX_PLUGIN
+#   endif
+
+private:
+  struct default_prunning_func {
+    default_prunning_func(Scalar ref, RealScalar eps) : reference(ref), epsilon(eps) {}
+    inline bool operator() (const Index&, const Index&, const Scalar& value) const
+    {
+      return !internal::isMuchSmallerThan(value, reference, epsilon);
+    }
+    Scalar reference;
+    RealScalar epsilon;
+  };
+};
+
+template<typename Scalar, int _Options, typename _Index>
+class SparseMatrix<Scalar,_Options,_Index>::InnerIterator
+{
+  public:
+    InnerIterator(const SparseMatrix& mat, Index outer)
+      : m_values(mat._valuePtr()), m_indices(mat._innerIndexPtr()), m_outer(outer), m_id(mat.m_outerIndex[outer]), m_end(mat.m_outerIndex[outer+1])
+    {}
+
+    inline InnerIterator& operator++() { m_id++; return *this; }
+
+    inline const Scalar& value() const { return m_values[m_id]; }
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id]); }
+
+    inline Index index() const { return m_indices[m_id]; }
+    inline Index outer() const { return m_outer; }
+    inline Index row() const { return IsRowMajor ? m_outer : index(); }
+    inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+    inline operator bool() const { return (m_id < m_end); }
+
+  protected:
+    const Scalar* m_values;
+    const Index* m_indices;
+    const Index m_outer;
+    Index m_id;
+    const Index m_end;
+};
+
+#endif // EIGEN_SPARSEMATRIX_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseMatrixBase.h b/extern/Eigen3/Eigen/src/Sparse/SparseMatrixBase.h
new file mode 100644
index 00000000000..c01981bc935
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseMatrixBase.h
@@ -0,0 +1,706 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEMATRIXBASE_H
+#define EIGEN_SPARSEMATRIXBASE_H
+
+/** \ingroup Sparse_Module
+  *
+  * \class SparseMatrixBase
+  *
+  * \brief Base class of any sparse matrices or sparse expressions
+  *
+  * \tparam Derived
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_SPARSEMATRIXBASE_PLUGIN.
+  */
+template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
+{
+  public:
+
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::Index Index;
+
+    typedef SparseMatrixBase StorageBaseType;
+    typedef EigenBase<Derived> Base;
+    
+    template<typename OtherDerived>
+    Derived& operator=(const EigenBase<OtherDerived> &other)
+    {
+      other.derived().evalTo(derived());
+      return derived();
+    }
+    
+//     using Base::operator=;
+
+    enum {
+
+      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+        /**< The number of rows at compile-time. This is just a copy of the value provided
+          * by the \a Derived type. If a value is not known at compile-time,
+          * it is set to the \a Dynamic constant.
+          * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
+
+      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+        /**< The number of columns at compile-time. This is just a copy of the value provided
+          * by the \a Derived type. If a value is not known at compile-time,
+          * it is set to the \a Dynamic constant.
+          * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
+
+
+      SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
+                                                   internal::traits<Derived>::ColsAtCompileTime>::ret),
+        /**< This is equal to the number of coefficients, i.e. the number of
+          * rows times the number of columns, or to \a Dynamic if this is not
+          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
+
+      MaxRowsAtCompileTime = RowsAtCompileTime,
+      MaxColsAtCompileTime = ColsAtCompileTime,
+
+      MaxSizeAtCompileTime = (internal::size_at_compile_time<MaxRowsAtCompileTime,
+                                                      MaxColsAtCompileTime>::ret),
+
+      IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1,
+        /**< This is set to true if either the number of rows or the number of
+          * columns is known at compile-time to be equal to 1. Indeed, in that case,
+          * we are dealing with a column-vector (if there is only one column) or with
+          * a row-vector (if there is only one row). */
+
+      Flags = internal::traits<Derived>::Flags,
+        /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
+          * constructed from this one. See the \ref flags "list of flags".
+          */
+
+      CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
+        /**< This is a rough measure of how expensive it is to read one coefficient from
+          * this expression.
+          */
+
+      IsRowMajor = Flags&RowMajorBit ? 1 : 0,
+
+      #ifndef EIGEN_PARSED_BY_DOXYGEN
+      _HasDirectAccess = (int(Flags)&DirectAccessBit) ? 1 : 0 // workaround sunCC
+      #endif
+    };
+
+    /* \internal the return type of MatrixBase::conjugate() */
+//     typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
+//                         const SparseCwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, Derived>,
+//                         const Derived&
+//                      >::type ConjugateReturnType;
+    /* \internal the return type of MatrixBase::real() */
+//     typedef SparseCwiseUnaryOp<internal::scalar_real_op<Scalar>, Derived> RealReturnType;
+    /* \internal the return type of MatrixBase::imag() */
+//     typedef SparseCwiseUnaryOp<internal::scalar_imag_op<Scalar>, Derived> ImagReturnType;
+    /** \internal the return type of MatrixBase::adjoint() */
+    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
+                        CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, Eigen::Transpose<const Derived> >,
+                        Transpose<const Derived>
+                     >::type AdjointReturnType;
+
+
+    typedef SparseMatrix<Scalar, Flags&RowMajorBit ? RowMajor : ColMajor> PlainObject;
+
+#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::SparseMatrixBase
+#   include "../plugins/CommonCwiseUnaryOps.h"
+#   include "../plugins/CommonCwiseBinaryOps.h"
+#   include "../plugins/MatrixCwiseUnaryOps.h"
+#   include "../plugins/MatrixCwiseBinaryOps.h"
+#   ifdef EIGEN_SPARSEMATRIXBASE_PLUGIN
+#     include EIGEN_SPARSEMATRIXBASE_PLUGIN
+#   endif
+#   undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** This is the "real scalar" type; if the \a Scalar type is already real numbers
+      * (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
+      * \a Scalar is \a std::complex<T> then RealScalar is \a T.
+      *
+      * \sa class NumTraits
+      */
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    /** \internal the return type of coeff()
+      */
+    typedef typename internal::conditional<_HasDirectAccess, const Scalar&, Scalar>::type CoeffReturnType;
+
+    /** \internal Represents a matrix with all coefficients equal to one another*/
+    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Matrix<Scalar,Dynamic,Dynamic> > ConstantReturnType;
+
+    /** type of the equivalent square matrix */
+    typedef Matrix<Scalar,EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime),
+                          EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType;
+
+    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    inline Derived& derived() { return *static_cast<Derived*>(this); }
+    inline Derived& const_cast_derived() const
+    { return *static_cast<Derived*>(const_cast<SparseMatrixBase*>(this)); }
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+    /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
+    inline Index rows() const { return derived().rows(); }
+    /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
+    inline Index cols() const { return derived().cols(); }
+    /** \returns the number of coefficients, which is \a rows()*cols().
+      * \sa rows(), cols(), SizeAtCompileTime. */
+    inline Index size() const { return rows() * cols(); }
+    /** \returns the number of nonzero coefficients which is in practice the number
+      * of stored coefficients. */
+    inline Index nonZeros() const { return derived().nonZeros(); }
+    /** \returns true if either the number of rows or the number of columns is equal to 1.
+      * In other words, this function returns
+      * \code rows()==1 || cols()==1 \endcode
+      * \sa rows(), cols(), IsVectorAtCompileTime. */
+    inline bool isVector() const { return rows()==1 || cols()==1; }
+    /** \returns the size of the storage major dimension,
+      * i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
+    Index outerSize() const { return (int(Flags)&RowMajorBit) ? this->rows() : this->cols(); }
+    /** \returns the size of the inner dimension according to the storage order,
+      * i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
+    Index innerSize() const { return (int(Flags)&RowMajorBit) ? this->cols() : this->rows(); }
+
+    bool isRValue() const { return m_isRValue; }
+    Derived& markAsRValue() { m_isRValue = true; return derived(); }
+
+    SparseMatrixBase() : m_isRValue(false) { /* TODO check flags */ }
+    
+    inline Derived& operator=(const Derived& other)
+    {
+//       std::cout << "Derived& operator=(const Derived& other)\n";
+//       if (other.isRValue())
+//         derived().swap(other.const_cast_derived());
+//       else
+        this->operator=<Derived>(other);
+      return derived();
+    }
+    
+    template<typename OtherDerived>
+    Derived& operator=(const ReturnByValue<OtherDerived>& other)
+    {
+      other.evalTo(derived());
+      return derived();
+    }
+
+
+    template<typename OtherDerived>
+    inline void assignGeneric(const OtherDerived& other)
+    {
+//       std::cout << "Derived& operator=(const MatrixBase<OtherDerived>& other)\n";
+      //const bool transpose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+      eigen_assert(( ((internal::traits<Derived>::SupportedAccessPatterns&OuterRandomAccessPattern)==OuterRandomAccessPattern) ||
+                  (!((Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit)))) &&
+                  "the transpose operation is supposed to be handled in SparseMatrix::operator=");
+
+      enum { Flip = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit) };
+
+      const Index outerSize = other.outerSize();
+      //typedef typename internal::conditional<transpose, LinkedVectorMatrix<Scalar,Flags&RowMajorBit>, Derived>::type TempType;
+      // thanks to shallow copies, we always eval to a tempary
+      Derived temp(other.rows(), other.cols());
+
+      temp.reserve((std::max)(this->rows(),this->cols())*2);
+      for (Index j=0; j<outerSize; ++j)
+      {
+        temp.startVec(j);
+        for (typename OtherDerived::InnerIterator it(other.derived(), j); it; ++it)
+        {
+          Scalar v = it.value();
+          if (v!=Scalar(0))
+            temp.insertBackByOuterInner(Flip?it.index():j,Flip?j:it.index()) = v;
+        }
+      }
+      temp.finalize();
+
+      derived() = temp.markAsRValue();
+    }
+
+
+    template<typename OtherDerived>
+    inline Derived& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+//       std::cout << typeid(OtherDerived).name() << "\n";
+//       std::cout << Flags << " " << OtherDerived::Flags << "\n";
+      const bool transpose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+//       std::cout << "eval transpose = " << transpose << "\n";
+      const Index outerSize = (int(OtherDerived::Flags) & RowMajorBit) ? other.rows() : other.cols();
+      if ((!transpose) && other.isRValue())
+      {
+        // eval without temporary
+        derived().resize(other.rows(), other.cols());
+        derived().setZero();
+        derived().reserve((std::max)(this->rows(),this->cols())*2);
+        for (Index j=0; j<outerSize; ++j)
+        {
+          derived().startVec(j);
+          for (typename OtherDerived::InnerIterator it(other.derived(), j); it; ++it)
+          {
+            Scalar v = it.value();
+            if (v!=Scalar(0))
+              derived().insertBackByOuterInner(j,it.index()) = v;
+          }
+        }
+        derived().finalize();
+      }
+      else
+      {
+        assignGeneric(other.derived());
+      }
+      return derived();
+    }
+
+    template<typename Lhs, typename Rhs>
+    inline Derived& operator=(const SparseSparseProduct<Lhs,Rhs>& product);
+
+    template<typename Lhs, typename Rhs>
+    inline void _experimentalNewProduct(const Lhs& lhs, const Rhs& rhs);
+
+    friend std::ostream & operator << (std::ostream & s, const SparseMatrixBase& m)
+    {
+      if (Flags&RowMajorBit)
+      {
+        for (Index row=0; row<m.outerSize(); ++row)
+        {
+          Index col = 0;
+          for (typename Derived::InnerIterator it(m.derived(), row); it; ++it)
+          {
+            for ( ; col<it.index(); ++col)
+              s << "0 ";
+            s << it.value() << " ";
+            ++col;
+          }
+          for ( ; col<m.cols(); ++col)
+            s << "0 ";
+          s << std::endl;
+        }
+      }
+      else
+      {
+        if (m.cols() == 1) {
+          Index row = 0;
+          for (typename Derived::InnerIterator it(m.derived(), 0); it; ++it)
+          {
+            for ( ; row<it.index(); ++row)
+              s << "0" << std::endl;
+            s << it.value() << std::endl;
+            ++row;
+          }
+          for ( ; row<m.rows(); ++row)
+            s << "0" << std::endl;
+        }
+        else
+        {
+          SparseMatrix<Scalar, RowMajorBit> trans = m.derived();
+          s << trans;
+        }
+      }
+      return s;
+    }
+
+//     const SparseCwiseUnaryOp<internal::scalar_opposite_op<typename internal::traits<Derived>::Scalar>,Derived> operator-() const;
+
+//     template<typename OtherDerived>
+//     const CwiseBinaryOp<internal::scalar_sum_op<typename internal::traits<Derived>::Scalar>, Derived, OtherDerived>
+//     operator+(const SparseMatrixBase<OtherDerived> &other) const;
+
+//     template<typename OtherDerived>
+//     const CwiseBinaryOp<internal::scalar_difference_op<typename internal::traits<Derived>::Scalar>, Derived, OtherDerived>
+//     operator-(const SparseMatrixBase<OtherDerived> &other) const;
+
+    template<typename OtherDerived>
+    Derived& operator+=(const SparseMatrixBase<OtherDerived>& other);
+    template<typename OtherDerived>
+    Derived& operator-=(const SparseMatrixBase<OtherDerived>& other);
+
+//     template<typename Lhs,typename Rhs>
+//     Derived& operator+=(const Flagged<Product<Lhs,Rhs,CacheFriendlyProduct>, 0, EvalBeforeNestingBit | EvalBeforeAssigningBit>& other);
+
+    Derived& operator*=(const Scalar& other);
+    Derived& operator/=(const Scalar& other);
+
+    #define EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE \
+      CwiseBinaryOp< \
+        internal::scalar_product_op< \
+          typename internal::scalar_product_traits< \
+            typename internal::traits<Derived>::Scalar, \
+            typename internal::traits<OtherDerived>::Scalar \
+          >::ReturnType \
+        >, \
+        Derived, \
+        OtherDerived \
+      >
+
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE const EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE
+    cwiseProduct(const MatrixBase<OtherDerived> &other) const;
+
+//     const SparseCwiseUnaryOp<internal::scalar_multiple_op<typename internal::traits<Derived>::Scalar>, Derived>
+//     operator*(const Scalar& scalar) const;
+//     const SparseCwiseUnaryOp<internal::scalar_quotient1_op<typename internal::traits<Derived>::Scalar>, Derived>
+//     operator/(const Scalar& scalar) const;
+
+//     inline friend const SparseCwiseUnaryOp<internal::scalar_multiple_op<typename internal::traits<Derived>::Scalar>, Derived>
+//     operator*(const Scalar& scalar, const SparseMatrixBase& matrix)
+//     { return matrix*scalar; }
+
+
+    // sparse * sparse
+    template<typename OtherDerived>
+    const typename SparseSparseProductReturnType<Derived,OtherDerived>::Type
+    operator*(const SparseMatrixBase<OtherDerived> &other) const;
+
+    // sparse * diagonal
+    template<typename OtherDerived>
+    const SparseDiagonalProduct<Derived,OtherDerived>
+    operator*(const DiagonalBase<OtherDerived> &other) const;
+
+    // diagonal * sparse
+    template<typename OtherDerived> friend
+    const SparseDiagonalProduct<OtherDerived,Derived>
+    operator*(const DiagonalBase<OtherDerived> &lhs, const SparseMatrixBase& rhs)
+    { return SparseDiagonalProduct<OtherDerived,Derived>(lhs.derived(), rhs.derived()); }
+
+    /** dense * sparse (return a dense object unless it is an outer product) */
+    template<typename OtherDerived> friend
+    const typename DenseSparseProductReturnType<OtherDerived,Derived>::Type
+    operator*(const MatrixBase<OtherDerived>& lhs, const Derived& rhs)
+    { return typename DenseSparseProductReturnType<OtherDerived,Derived>::Type(lhs.derived(),rhs); }
+
+    /** sparse * dense (returns a dense object unless it is an outer product) */
+    template<typename OtherDerived>
+    const typename SparseDenseProductReturnType<Derived,OtherDerived>::Type
+    operator*(const MatrixBase<OtherDerived> &other) const;
+
+    template<typename OtherDerived>
+    Derived& operator*=(const SparseMatrixBase<OtherDerived>& other);
+
+    #ifdef EIGEN2_SUPPORT
+    // deprecated
+    template<typename OtherDerived>
+    typename internal::plain_matrix_type_column_major<OtherDerived>::type
+    solveTriangular(const MatrixBase<OtherDerived>& other) const;
+
+    // deprecated
+    template<typename OtherDerived>
+    void solveTriangularInPlace(MatrixBase<OtherDerived>& other) const;
+//     template<typename OtherDerived>
+//     void solveTriangularInPlace(SparseMatrixBase<OtherDerived>& other) const;
+    #endif // EIGEN2_SUPPORT
+
+    template<int Mode>
+    inline const SparseTriangularView<Derived, Mode> triangularView() const;
+
+    template<unsigned int UpLo> inline const SparseSelfAdjointView<Derived, UpLo> selfadjointView() const;
+    template<unsigned int UpLo> inline SparseSelfAdjointView<Derived, UpLo> selfadjointView();
+
+    template<typename OtherDerived> Scalar dot(const MatrixBase<OtherDerived>& other) const;
+    template<typename OtherDerived> Scalar dot(const SparseMatrixBase<OtherDerived>& other) const;
+    RealScalar squaredNorm() const;
+    RealScalar norm()  const;
+//     const PlainObject normalized() const;
+//     void normalize();
+
+    Transpose<Derived> transpose() { return derived(); }
+    const Transpose<const Derived> transpose() const { return derived(); }
+    // void transposeInPlace();
+    const AdjointReturnType adjoint() const { return transpose(); }
+
+    // sub-vector
+    SparseInnerVectorSet<Derived,1> row(Index i);
+    const SparseInnerVectorSet<Derived,1> row(Index i) const;
+    SparseInnerVectorSet<Derived,1> col(Index j);
+    const SparseInnerVectorSet<Derived,1> col(Index j) const;
+    SparseInnerVectorSet<Derived,1> innerVector(Index outer);
+    const SparseInnerVectorSet<Derived,1> innerVector(Index outer) const;
+
+    // set of sub-vectors
+    SparseInnerVectorSet<Derived,Dynamic> subrows(Index start, Index size);
+    const SparseInnerVectorSet<Derived,Dynamic> subrows(Index start, Index size) const;
+    SparseInnerVectorSet<Derived,Dynamic> subcols(Index start, Index size);
+    const SparseInnerVectorSet<Derived,Dynamic> subcols(Index start, Index size) const;
+    SparseInnerVectorSet<Derived,Dynamic> innerVectors(Index outerStart, Index outerSize);
+    const SparseInnerVectorSet<Derived,Dynamic> innerVectors(Index outerStart, Index outerSize) const;
+
+//     typename BlockReturnType<Derived>::Type block(int startRow, int startCol, int blockRows, int blockCols);
+//     const typename BlockReturnType<Derived>::Type
+//     block(int startRow, int startCol, int blockRows, int blockCols) const;
+//
+//     typename BlockReturnType<Derived>::SubVectorType segment(int start, int size);
+//     const typename BlockReturnType<Derived>::SubVectorType segment(int start, int size) const;
+//
+//     typename BlockReturnType<Derived,Dynamic>::SubVectorType start(int size);
+//     const typename BlockReturnType<Derived,Dynamic>::SubVectorType start(int size) const;
+//
+//     typename BlockReturnType<Derived,Dynamic>::SubVectorType end(int size);
+//     const typename BlockReturnType<Derived,Dynamic>::SubVectorType end(int size) const;
+//
+//     template<int BlockRows, int BlockCols>
+//     typename BlockReturnType<Derived, BlockRows, BlockCols>::Type block(int startRow, int startCol);
+//     template<int BlockRows, int BlockCols>
+//     const typename BlockReturnType<Derived, BlockRows, BlockCols>::Type block(int startRow, int startCol) const;
+
+//     template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType start(void);
+//     template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType start() const;
+
+//     template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType end();
+//     template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType end() const;
+
+//     template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType segment(int start);
+//     template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType segment(int start) const;
+
+//     Diagonal<Derived> diagonal();
+//     const Diagonal<Derived> diagonal() const;
+
+//     template<unsigned int Mode> Part<Derived, Mode> part();
+//     template<unsigned int Mode> const Part<Derived, Mode> part() const;
+
+
+//     static const ConstantReturnType Constant(int rows, int cols, const Scalar& value);
+//     static const ConstantReturnType Constant(int size, const Scalar& value);
+//     static const ConstantReturnType Constant(const Scalar& value);
+
+//     template<typename CustomNullaryOp>
+//     static const CwiseNullaryOp<CustomNullaryOp, Derived> NullaryExpr(int rows, int cols, const CustomNullaryOp& func);
+//     template<typename CustomNullaryOp>
+//     static const CwiseNullaryOp<CustomNullaryOp, Derived> NullaryExpr(int size, const CustomNullaryOp& func);
+//     template<typename CustomNullaryOp>
+//     static const CwiseNullaryOp<CustomNullaryOp, Derived> NullaryExpr(const CustomNullaryOp& func);
+
+//     static const ConstantReturnType Zero(int rows, int cols);
+//     static const ConstantReturnType Zero(int size);
+//     static const ConstantReturnType Zero();
+//     static const ConstantReturnType Ones(int rows, int cols);
+//     static const ConstantReturnType Ones(int size);
+//     static const ConstantReturnType Ones();
+//     static const IdentityReturnType Identity();
+//     static const IdentityReturnType Identity(int rows, int cols);
+//     static const BasisReturnType Unit(int size, int i);
+//     static const BasisReturnType Unit(int i);
+//     static const BasisReturnType UnitX();
+//     static const BasisReturnType UnitY();
+//     static const BasisReturnType UnitZ();
+//     static const BasisReturnType UnitW();
+
+//     const DiagonalMatrix<Derived> asDiagonal() const;
+
+//     Derived& setConstant(const Scalar& value);
+//     Derived& setZero();
+//     Derived& setOnes();
+//     Derived& setRandom();
+//     Derived& setIdentity();
+
+      /** \internal use operator= */
+      template<typename DenseDerived>
+      void evalTo(MatrixBase<DenseDerived>& dst) const
+      {
+        dst.setZero();
+        for (Index j=0; j<outerSize(); ++j)
+          for (typename Derived::InnerIterator i(derived(),j); i; ++i)
+            dst.coeffRef(i.row(),i.col()) = i.value();
+      }
+
+      Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> toDense() const
+      {
+        return derived();
+      }
+
+    template<typename OtherDerived>
+    bool isApprox(const SparseMatrixBase<OtherDerived>& other,
+                  RealScalar prec = NumTraits<Scalar>::dummy_precision()) const
+    { return toDense().isApprox(other.toDense(),prec); }
+
+    template<typename OtherDerived>
+    bool isApprox(const MatrixBase<OtherDerived>& other,
+                  RealScalar prec = NumTraits<Scalar>::dummy_precision()) const
+    { return toDense().isApprox(other,prec); }
+//     bool isMuchSmallerThan(const RealScalar& other,
+//                            RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
+//     template<typename OtherDerived>
+//     bool isMuchSmallerThan(const MatrixBase<OtherDerived>& other,
+//                            RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
+
+//     bool isApproxToConstant(const Scalar& value, RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
+//     bool isZero(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
+//     bool isOnes(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
+//     bool isIdentity(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
+//     bool isDiagonal(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
+
+//     bool isUpper(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
+//     bool isLower(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
+
+//     template<typename OtherDerived>
+//     bool isOrthogonal(const MatrixBase<OtherDerived>& other,
+//                       RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
+//     bool isUnitary(RealScalar prec = NumTraits<Scalar>::dummy_precision()) const;
+
+//     template<typename OtherDerived>
+//     inline bool operator==(const MatrixBase<OtherDerived>& other) const
+//     { return (cwise() == other).all(); }
+
+//     template<typename OtherDerived>
+//     inline bool operator!=(const MatrixBase<OtherDerived>& other) const
+//     { return (cwise() != other).any(); }
+
+
+//     template<typename NewType>
+//     const SparseCwiseUnaryOp<internal::scalar_cast_op<typename internal::traits<Derived>::Scalar, NewType>, Derived> cast() const;
+
+    /** \returns the matrix or vector obtained by evaluating this expression.
+      *
+      * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
+      * a const reference, in order to avoid a useless copy.
+      */
+    inline const typename internal::eval<Derived>::type eval() const
+    { return typename internal::eval<Derived>::type(derived()); }
+
+//     template<typename OtherDerived>
+//     void swap(MatrixBase<OtherDerived> const & other);
+
+//     template<unsigned int Added>
+//     const SparseFlagged<Derived, Added, 0> marked() const;
+//     const Flagged<Derived, 0, EvalBeforeNestingBit | EvalBeforeAssigningBit> lazy() const;
+
+    /** \returns number of elements to skip to pass from one row (resp. column) to another
+      * for a row-major (resp. column-major) matrix.
+      * Combined with coeffRef() and the \ref flags flags, it allows a direct access to the data
+      * of the underlying matrix.
+      */
+//     inline int stride(void) const { return derived().stride(); }
+
+// FIXME
+//     ConjugateReturnType conjugate() const;
+//     const RealReturnType real() const;
+//     const ImagReturnType imag() const;
+
+//     template<typename CustomUnaryOp>
+//     const SparseCwiseUnaryOp<CustomUnaryOp, Derived> unaryExpr(const CustomUnaryOp& func = CustomUnaryOp()) const;
+
+//     template<typename CustomBinaryOp, typename OtherDerived>
+//     const CwiseBinaryOp<CustomBinaryOp, Derived, OtherDerived>
+//     binaryExpr(const MatrixBase<OtherDerived> &other, const CustomBinaryOp& func = CustomBinaryOp()) const;
+
+
+    Scalar sum() const;
+//     Scalar trace() const;
+
+//     typename internal::traits<Derived>::Scalar minCoeff() const;
+//     typename internal::traits<Derived>::Scalar maxCoeff() const;
+
+//     typename internal::traits<Derived>::Scalar minCoeff(int* row, int* col = 0) const;
+//     typename internal::traits<Derived>::Scalar maxCoeff(int* row, int* col = 0) const;
+
+//     template<typename BinaryOp>
+//     typename internal::result_of<BinaryOp(typename internal::traits<Derived>::Scalar)>::type
+//     redux(const BinaryOp& func) const;
+
+//     template<typename Visitor>
+//     void visit(Visitor& func) const;
+
+
+//     const SparseCwise<Derived> cwise() const;
+//     SparseCwise<Derived> cwise();
+
+//     inline const WithFormat<Derived> format(const IOFormat& fmt) const;
+
+/////////// Array module ///////////
+    /*
+    bool all(void) const;
+    bool any(void) const;
+
+    const VectorwiseOp<Derived,Horizontal> rowwise() const;
+    const VectorwiseOp<Derived,Vertical> colwise() const;
+
+    static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random(int rows, int cols);
+    static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random(int size);
+    static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random();
+
+    template<typename ThenDerived,typename ElseDerived>
+    const Select<Derived,ThenDerived,ElseDerived>
+    select(const MatrixBase<ThenDerived>& thenMatrix,
+           const MatrixBase<ElseDerived>& elseMatrix) const;
+
+    template<typename ThenDerived>
+    inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType>
+    select(const MatrixBase<ThenDerived>& thenMatrix, typename ThenDerived::Scalar elseScalar) const;
+
+    template<typename ElseDerived>
+    inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived >
+    select(typename ElseDerived::Scalar thenScalar, const MatrixBase<ElseDerived>& elseMatrix) const;
+
+    template<int p> RealScalar lpNorm() const;
+    */
+
+
+//     template<typename OtherDerived>
+//     Scalar dot(const MatrixBase<OtherDerived>& other) const
+//     {
+//       EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+//       EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+//       EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+//         YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+//
+//       eigen_assert(derived().size() == other.size());
+//       // short version, but the assembly looks more complicated because
+//       // of the CwiseBinaryOp iterator complexity
+//       // return res = (derived().cwise() * other.derived().conjugate()).sum();
+//
+//       // optimized, generic version
+//       typename Derived::InnerIterator i(derived(),0);
+//       typename OtherDerived::InnerIterator j(other.derived(),0);
+//       Scalar res = 0;
+//       while (i && j)
+//       {
+//         if (i.index()==j.index())
+//         {
+// //           std::cerr << i.value() << " * " << j.value() << "\n";
+//           res += i.value() * internal::conj(j.value());
+//           ++i; ++j;
+//         }
+//         else if (i.index()<j.index())
+//           ++i;
+//         else
+//           ++j;
+//       }
+//       return res;
+//     }
+//
+//     Scalar sum() const
+//     {
+//       Scalar res = 0;
+//       for (typename Derived::InnerIterator iter(*this,0); iter; ++iter)
+//       {
+//         res += iter.value();
+//       }
+//       return res;
+//     }
+
+  protected:
+
+    bool m_isRValue;
+};
+
+#endif // EIGEN_SPARSEMATRIXBASE_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseProduct.h b/extern/Eigen3/Eigen/src/Sparse/SparseProduct.h
new file mode 100644
index 00000000000..1c1f54706ac
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseProduct.h
@@ -0,0 +1,141 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEPRODUCT_H
+#define EIGEN_SPARSEPRODUCT_H
+
+template<typename Lhs, typename Rhs>
+struct SparseSparseProductReturnType
+{
+  typedef typename internal::traits<Lhs>::Scalar Scalar;
+  enum {
+    LhsRowMajor = internal::traits<Lhs>::Flags & RowMajorBit,
+    RhsRowMajor = internal::traits<Rhs>::Flags & RowMajorBit,
+    TransposeRhs = (!LhsRowMajor) && RhsRowMajor,
+    TransposeLhs = LhsRowMajor && (!RhsRowMajor)
+  };
+
+  typedef typename internal::conditional<TransposeLhs,
+    SparseMatrix<Scalar,0>,
+    const typename internal::nested<Lhs,Rhs::RowsAtCompileTime>::type>::type LhsNested;
+
+  typedef typename internal::conditional<TransposeRhs,
+    SparseMatrix<Scalar,0>,
+    const typename internal::nested<Rhs,Lhs::RowsAtCompileTime>::type>::type RhsNested;
+
+  typedef SparseSparseProduct<LhsNested, RhsNested> Type;
+};
+
+namespace internal {
+template<typename LhsNested, typename RhsNested>
+struct traits<SparseSparseProduct<LhsNested, RhsNested> >
+{
+  typedef MatrixXpr XprKind;
+  // clean the nested types:
+  typedef typename remove_all<LhsNested>::type _LhsNested;
+  typedef typename remove_all<RhsNested>::type _RhsNested;
+  typedef typename _LhsNested::Scalar Scalar;
+  typedef typename promote_index_type<typename traits<_LhsNested>::Index,
+                                         typename traits<_RhsNested>::Index>::type Index;
+
+  enum {
+    LhsCoeffReadCost = _LhsNested::CoeffReadCost,
+    RhsCoeffReadCost = _RhsNested::CoeffReadCost,
+    LhsFlags = _LhsNested::Flags,
+    RhsFlags = _RhsNested::Flags,
+
+    RowsAtCompileTime    = _LhsNested::RowsAtCompileTime,
+    ColsAtCompileTime    = _RhsNested::ColsAtCompileTime,
+    MaxRowsAtCompileTime = _LhsNested::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = _RhsNested::MaxColsAtCompileTime,
+
+    InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(_LhsNested::ColsAtCompileTime, _RhsNested::RowsAtCompileTime),
+
+    EvalToRowMajor = (RhsFlags & LhsFlags & RowMajorBit),
+
+    RemovedBits = ~(EvalToRowMajor ? 0 : RowMajorBit),
+
+    Flags = (int(LhsFlags | RhsFlags) & HereditaryBits & RemovedBits)
+          | EvalBeforeAssigningBit
+          | EvalBeforeNestingBit,
+
+    CoeffReadCost = Dynamic
+  };
+
+  typedef Sparse StorageKind;
+};
+
+} // end namespace internal
+
+template<typename LhsNested, typename RhsNested>
+class SparseSparseProduct : internal::no_assignment_operator,
+  public SparseMatrixBase<SparseSparseProduct<LhsNested, RhsNested> >
+{
+  public:
+
+    typedef SparseMatrixBase<SparseSparseProduct> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(SparseSparseProduct)
+
+  private:
+
+    typedef typename internal::traits<SparseSparseProduct>::_LhsNested _LhsNested;
+    typedef typename internal::traits<SparseSparseProduct>::_RhsNested _RhsNested;
+
+  public:
+
+    template<typename Lhs, typename Rhs>
+    EIGEN_STRONG_INLINE SparseSparseProduct(const Lhs& lhs, const Rhs& rhs)
+      : m_lhs(lhs), m_rhs(rhs)
+    {
+      eigen_assert(lhs.cols() == rhs.rows());
+
+      enum {
+        ProductIsValid = _LhsNested::ColsAtCompileTime==Dynamic
+                      || _RhsNested::RowsAtCompileTime==Dynamic
+                      || int(_LhsNested::ColsAtCompileTime)==int(_RhsNested::RowsAtCompileTime),
+        AreVectors = _LhsNested::IsVectorAtCompileTime && _RhsNested::IsVectorAtCompileTime,
+        SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(_LhsNested,_RhsNested)
+      };
+      // note to the lost user:
+      //    * for a dot product use: v1.dot(v2)
+      //    * for a coeff-wise product use: v1.cwise()*v2
+      EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
+        INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
+      EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
+        INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
+      EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
+    }
+
+    EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); }
+
+    EIGEN_STRONG_INLINE const _LhsNested& lhs() const { return m_lhs; }
+    EIGEN_STRONG_INLINE const _RhsNested& rhs() const { return m_rhs; }
+
+  protected:
+    LhsNested m_lhs;
+    RhsNested m_rhs;
+};
+
+#endif // EIGEN_SPARSEPRODUCT_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseRedux.h b/extern/Eigen3/Eigen/src/Sparse/SparseRedux.h
new file mode 100644
index 00000000000..afc49de7aad
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseRedux.h
@@ -0,0 +1,56 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEREDUX_H
+#define EIGEN_SPARSEREDUX_H
+
+template<typename Derived>
+typename internal::traits<Derived>::Scalar
+SparseMatrixBase<Derived>::sum() const
+{
+  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
+  Scalar res = 0;
+  for (Index j=0; j<outerSize(); ++j)
+    for (typename Derived::InnerIterator iter(derived(),j); iter; ++iter)
+      res += iter.value();
+  return res;
+}
+
+template<typename _Scalar, int _Options, typename _Index>
+typename internal::traits<SparseMatrix<_Scalar,_Options,_Index> >::Scalar
+SparseMatrix<_Scalar,_Options,_Index>::sum() const
+{
+  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
+  return Matrix<Scalar,1,Dynamic>::Map(&m_data.value(0), m_data.size()).sum();
+}
+
+template<typename _Scalar, int _Options, typename _Index>
+typename internal::traits<SparseVector<_Scalar,_Options, _Index> >::Scalar
+SparseVector<_Scalar,_Options,_Index>::sum() const
+{
+  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
+  return Matrix<Scalar,1,Dynamic>::Map(&m_data.value(0), m_data.size()).sum();
+}
+
+#endif // EIGEN_SPARSEREDUX_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseSelfAdjointView.h b/extern/Eigen3/Eigen/src/Sparse/SparseSelfAdjointView.h
new file mode 100644
index 00000000000..d82044c789c
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseSelfAdjointView.h
@@ -0,0 +1,454 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_SELFADJOINTVIEW_H
+#define EIGEN_SPARSE_SELFADJOINTVIEW_H
+
+/** \class SparseSelfAdjointView
+  *
+  *
+  * \brief Pseudo expression to manipulate a triangular sparse matrix as a selfadjoint matrix.
+  *
+  * \param MatrixType the type of the dense matrix storing the coefficients
+  * \param UpLo can be either \c #Lower or \c #Upper
+  *
+  * This class is an expression of a sefladjoint matrix from a triangular part of a matrix
+  * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
+  * and most of the time this is the only way that it is used.
+  *
+  * \sa SparseMatrixBase::selfadjointView()
+  */
+template<typename Lhs, typename Rhs, int UpLo>
+class SparseSelfAdjointTimeDenseProduct;
+
+template<typename Lhs, typename Rhs, int UpLo>
+class DenseTimeSparseSelfAdjointProduct;
+
+template<typename MatrixType,int UpLo>
+class SparseSymmetricPermutationProduct;
+
+namespace internal {
+  
+template<typename MatrixType, unsigned int UpLo>
+struct traits<SparseSelfAdjointView<MatrixType,UpLo> > : traits<MatrixType> {
+};
+
+template<int SrcUpLo,int DstUpLo,typename MatrixType,int DestOrder>
+void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm = 0);
+
+template<int UpLo,typename MatrixType,int DestOrder>
+void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm = 0);
+
+}
+
+template<typename MatrixType, unsigned int UpLo> class SparseSelfAdjointView
+  : public EigenBase<SparseSelfAdjointView<MatrixType,UpLo> >
+{
+  public:
+
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::Index Index;
+    typedef Matrix<Index,Dynamic,1> VectorI;
+    typedef typename MatrixType::Nested MatrixTypeNested;
+    typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
+
+    inline SparseSelfAdjointView(const MatrixType& matrix) : m_matrix(matrix)
+    {
+      eigen_assert(rows()==cols() && "SelfAdjointView is only for squared matrices");
+    }
+
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+
+    /** \internal \returns a reference to the nested matrix */
+    const _MatrixTypeNested& matrix() const { return m_matrix; }
+    _MatrixTypeNested& matrix() { return m_matrix.const_cast_derived(); }
+
+    /** Efficient sparse self-adjoint matrix times dense vector/matrix product */
+    template<typename OtherDerived>
+    SparseSelfAdjointTimeDenseProduct<MatrixType,OtherDerived,UpLo>
+    operator*(const MatrixBase<OtherDerived>& rhs) const
+    {
+      return SparseSelfAdjointTimeDenseProduct<MatrixType,OtherDerived,UpLo>(m_matrix, rhs.derived());
+    }
+
+    /** Efficient dense vector/matrix times sparse self-adjoint matrix product */
+    template<typename OtherDerived> friend
+    DenseTimeSparseSelfAdjointProduct<OtherDerived,MatrixType,UpLo>
+    operator*(const MatrixBase<OtherDerived>& lhs, const SparseSelfAdjointView& rhs)
+    {
+      return DenseTimeSparseSelfAdjointProduct<OtherDerived,_MatrixTypeNested,UpLo>(lhs.derived(), rhs.m_matrix);
+    }
+
+    /** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
+      * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
+      *
+      * \returns a reference to \c *this
+      *
+      * Note that it is faster to set alpha=0 than initializing the matrix to zero
+      * and then keep the default value alpha=1.
+      *
+      * To perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
+      * call this function with u.adjoint().
+      */
+    template<typename DerivedU>
+    SparseSelfAdjointView& rankUpdate(const SparseMatrixBase<DerivedU>& u, Scalar alpha = Scalar(1));
+    
+    /** \internal triggered by sparse_matrix = SparseSelfadjointView; */
+    template<typename DestScalar> void evalTo(SparseMatrix<DestScalar>& _dest) const
+    {
+      internal::permute_symm_to_fullsymm<UpLo>(m_matrix, _dest);
+    }
+    
+    template<typename DestScalar> void evalTo(DynamicSparseMatrix<DestScalar>& _dest) const
+    {
+      // TODO directly evaluate into _dest;
+      SparseMatrix<DestScalar> tmp(_dest.rows(),_dest.cols());
+      internal::permute_symm_to_fullsymm<UpLo>(m_matrix, tmp);
+      _dest = tmp;
+    }
+    
+    /** \returns an expression of P^-1 H P */
+    SparseSymmetricPermutationProduct<_MatrixTypeNested,UpLo> twistedBy(const PermutationMatrix<Dynamic>& perm) const
+    {
+      return SparseSymmetricPermutationProduct<_MatrixTypeNested,UpLo>(m_matrix, perm);
+    }
+    
+    template<typename SrcMatrixType,int SrcUpLo>
+    SparseSelfAdjointView& operator=(const SparseSymmetricPermutationProduct<SrcMatrixType,SrcUpLo>& permutedMatrix)
+    {
+      permutedMatrix.evalTo(*this);
+      return *this;
+    }
+    
+
+    // const SparseLLT<PlainObject, UpLo> llt() const;
+    // const SparseLDLT<PlainObject, UpLo> ldlt() const;
+
+  protected:
+
+    const typename MatrixType::Nested m_matrix;
+    mutable VectorI m_countPerRow;
+    mutable VectorI m_countPerCol;
+};
+
+/***************************************************************************
+* Implementation of SparseMatrixBase methods
+***************************************************************************/
+
+template<typename Derived>
+template<unsigned int UpLo>
+const SparseSelfAdjointView<Derived, UpLo> SparseMatrixBase<Derived>::selfadjointView() const
+{
+  return derived();
+}
+
+template<typename Derived>
+template<unsigned int UpLo>
+SparseSelfAdjointView<Derived, UpLo> SparseMatrixBase<Derived>::selfadjointView()
+{
+  return derived();
+}
+
+/***************************************************************************
+* Implementation of SparseSelfAdjointView methods
+***************************************************************************/
+
+template<typename MatrixType, unsigned int UpLo>
+template<typename DerivedU>
+SparseSelfAdjointView<MatrixType,UpLo>&
+SparseSelfAdjointView<MatrixType,UpLo>::rankUpdate(const SparseMatrixBase<DerivedU>& u, Scalar alpha)
+{
+  SparseMatrix<Scalar,MatrixType::Flags&RowMajorBit?RowMajor:ColMajor> tmp = u * u.adjoint();
+  if(alpha==Scalar(0))
+    m_matrix.const_cast_derived() = tmp.template triangularView<UpLo>();
+  else
+    m_matrix.const_cast_derived() += alpha * tmp.template triangularView<UpLo>();
+
+  return *this;
+}
+
+/***************************************************************************
+* Implementation of sparse self-adjoint time dense matrix
+***************************************************************************/
+
+namespace internal {
+template<typename Lhs, typename Rhs, int UpLo>
+struct traits<SparseSelfAdjointTimeDenseProduct<Lhs,Rhs,UpLo> >
+ : traits<ProductBase<SparseSelfAdjointTimeDenseProduct<Lhs,Rhs,UpLo>, Lhs, Rhs> >
+{
+  typedef Dense StorageKind;
+};
+}
+
+template<typename Lhs, typename Rhs, int UpLo>
+class SparseSelfAdjointTimeDenseProduct
+  : public ProductBase<SparseSelfAdjointTimeDenseProduct<Lhs,Rhs,UpLo>, Lhs, Rhs>
+{
+  public:
+    EIGEN_PRODUCT_PUBLIC_INTERFACE(SparseSelfAdjointTimeDenseProduct)
+
+    SparseSelfAdjointTimeDenseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
+    {}
+
+    template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
+    {
+      // TODO use alpha
+      eigen_assert(alpha==Scalar(1) && "alpha != 1 is not implemented yet, sorry");
+      typedef typename internal::remove_all<Lhs>::type _Lhs;
+      typedef typename internal::remove_all<Rhs>::type _Rhs;
+      typedef typename _Lhs::InnerIterator LhsInnerIterator;
+      enum {
+        LhsIsRowMajor = (_Lhs::Flags&RowMajorBit)==RowMajorBit,
+        ProcessFirstHalf =
+                 ((UpLo&(Upper|Lower))==(Upper|Lower))
+              || ( (UpLo&Upper) && !LhsIsRowMajor)
+              || ( (UpLo&Lower) && LhsIsRowMajor),
+        ProcessSecondHalf = !ProcessFirstHalf
+      };
+      for (Index j=0; j<m_lhs.outerSize(); ++j)
+      {
+        LhsInnerIterator i(m_lhs,j);
+        if (ProcessSecondHalf && i && (i.index()==j))
+        {
+          dest.row(j) += i.value() * m_rhs.row(j);
+          ++i;
+        }
+        Block<Dest,1,Dest::ColsAtCompileTime> dest_j(dest.row(LhsIsRowMajor ? j : 0));
+        for(; (ProcessFirstHalf ? i && i.index() < j : i) ; ++i)
+        {
+          Index a = LhsIsRowMajor ? j : i.index();
+          Index b = LhsIsRowMajor ? i.index() : j;
+          typename Lhs::Scalar v = i.value();
+          dest.row(a) += (v) * m_rhs.row(b);
+          dest.row(b) += internal::conj(v) * m_rhs.row(a);
+        }
+        if (ProcessFirstHalf && i && (i.index()==j))
+          dest.row(j) += i.value() * m_rhs.row(j);
+      }
+    }
+
+  private:
+    SparseSelfAdjointTimeDenseProduct& operator=(const SparseSelfAdjointTimeDenseProduct&);
+};
+
+namespace internal {
+template<typename Lhs, typename Rhs, int UpLo>
+struct traits<DenseTimeSparseSelfAdjointProduct<Lhs,Rhs,UpLo> >
+ : traits<ProductBase<DenseTimeSparseSelfAdjointProduct<Lhs,Rhs,UpLo>, Lhs, Rhs> >
+{};
+}
+
+template<typename Lhs, typename Rhs, int UpLo>
+class DenseTimeSparseSelfAdjointProduct
+  : public ProductBase<DenseTimeSparseSelfAdjointProduct<Lhs,Rhs,UpLo>, Lhs, Rhs>
+{
+  public:
+    EIGEN_PRODUCT_PUBLIC_INTERFACE(DenseTimeSparseSelfAdjointProduct)
+
+    DenseTimeSparseSelfAdjointProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
+    {}
+
+    template<typename Dest> void scaleAndAddTo(Dest& /*dest*/, Scalar /*alpha*/) const
+    {
+      // TODO
+    }
+
+  private:
+    DenseTimeSparseSelfAdjointProduct& operator=(const DenseTimeSparseSelfAdjointProduct&);
+};
+
+/***************************************************************************
+* Implementation of symmetric copies and permutations
+***************************************************************************/
+namespace internal {
+  
+template<typename MatrixType, int UpLo>
+struct traits<SparseSymmetricPermutationProduct<MatrixType,UpLo> > : traits<MatrixType> {
+};
+
+template<int UpLo,typename MatrixType,int DestOrder>
+void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm)
+{
+  typedef typename MatrixType::Index Index;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef SparseMatrix<Scalar,DestOrder,Index> Dest;
+  typedef Matrix<Index,Dynamic,1> VectorI;
+  
+  Dest& dest(_dest.derived());
+  enum {
+    StorageOrderMatch = int(Dest::IsRowMajor) == int(MatrixType::IsRowMajor)
+  };
+  eigen_assert(perm==0);
+  Index size = mat.rows();
+  VectorI count;
+  count.resize(size);
+  count.setZero();
+  dest.resize(size,size);
+  for(Index j = 0; j<size; ++j)
+  {
+    Index jp = perm ? perm[j] : j;
+    for(typename MatrixType::InnerIterator it(mat,j); it; ++it)
+    {
+      Index i = it.index();
+      Index ip = perm ? perm[i] : i;
+      if(i==j)
+        count[ip]++;
+      else if((UpLo==Lower && i>j) || (UpLo==Upper && i<j))
+      {
+        count[ip]++;
+        count[jp]++;
+      }
+    }
+  }
+  Index nnz = count.sum();
+  
+  // reserve space
+  dest.reserve(nnz);
+  dest._outerIndexPtr()[0] = 0;
+  for(Index j=0; j<size; ++j)
+    dest._outerIndexPtr()[j+1] = dest._outerIndexPtr()[j] + count[j];
+  for(Index j=0; j<size; ++j)
+    count[j] = dest._outerIndexPtr()[j];
+  
+  // copy data
+  for(Index j = 0; j<size; ++j)
+  {
+    Index jp = perm ? perm[j] : j;
+    for(typename MatrixType::InnerIterator it(mat,j); it; ++it)
+    {
+      Index i = it.index();
+      Index ip = perm ? perm[i] : i;
+      if(i==j)
+      {
+        int k = count[ip]++;
+        dest._innerIndexPtr()[k] = ip;
+        dest._valuePtr()[k] = it.value();
+      }
+      else if((UpLo==Lower && i>j) || (UpLo==Upper && i<j))
+      {
+        int k = count[jp]++;
+        dest._innerIndexPtr()[k] = ip;
+        dest._valuePtr()[k] = it.value();
+        k = count[ip]++;
+        dest._innerIndexPtr()[k] = jp;
+        dest._valuePtr()[k] = internal::conj(it.value());
+      }
+    }
+  }
+}
+
+template<int SrcUpLo,int DstUpLo,typename MatrixType,int DestOrder>
+void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm)
+{
+  typedef typename MatrixType::Index Index;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef SparseMatrix<Scalar,DestOrder,Index> Dest;
+  Dest& dest(_dest.derived());
+  typedef Matrix<Index,Dynamic,1> VectorI;
+  //internal::conj_if<SrcUpLo!=DstUpLo> cj;
+  
+  Index size = mat.rows();
+  VectorI count(size);
+  count.setZero();
+  dest.resize(size,size);
+  for(Index j = 0; j<size; ++j)
+  {
+    Index jp = perm ? perm[j] : j;
+    for(typename MatrixType::InnerIterator it(mat,j); it; ++it)
+    {
+      Index i = it.index();
+      if((SrcUpLo==Lower && i<j) || (SrcUpLo==Upper && i>j))
+        continue;
+                  
+      Index ip = perm ? perm[i] : i;
+      count[DstUpLo==Lower ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
+    }
+  }
+  dest._outerIndexPtr()[0] = 0;
+  for(Index j=0; j<size; ++j)
+    dest._outerIndexPtr()[j+1] = dest._outerIndexPtr()[j] + count[j];
+  dest.resizeNonZeros(dest._outerIndexPtr()[size]);
+  for(Index j=0; j<size; ++j)
+    count[j] = dest._outerIndexPtr()[j];
+  
+  for(Index j = 0; j<size; ++j)
+  {
+    Index jp = perm ? perm[j] : j;
+    for(typename MatrixType::InnerIterator it(mat,j); it; ++it)
+    {
+      Index i = it.index();
+      if((SrcUpLo==Lower && i<j) || (SrcUpLo==Upper && i>j))
+        continue;
+                  
+      Index ip = perm? perm[i] : i;
+      Index k = count[DstUpLo==Lower ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
+      dest._innerIndexPtr()[k] = DstUpLo==Lower ? (std::max)(ip,jp) : (std::min)(ip,jp);
+      
+      if((DstUpLo==Lower && ip<jp) || (DstUpLo==Upper && ip>jp))
+        dest._valuePtr()[k] = conj(it.value());
+      else
+        dest._valuePtr()[k] = it.value();
+    }
+  }
+}
+
+}
+
+template<typename MatrixType,int UpLo>
+class SparseSymmetricPermutationProduct
+  : public EigenBase<SparseSymmetricPermutationProduct<MatrixType,UpLo> >
+{
+    typedef PermutationMatrix<Dynamic> Perm;
+  public:
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::Index Index;
+    typedef Matrix<Index,Dynamic,1> VectorI;
+    typedef typename MatrixType::Nested MatrixTypeNested;
+    typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
+    
+    SparseSymmetricPermutationProduct(const MatrixType& mat, const Perm& perm)
+      : m_matrix(mat), m_perm(perm)
+    {}
+    
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+    
+    template<typename DestScalar> void evalTo(SparseMatrix<DestScalar>& _dest) const
+    {
+      internal::permute_symm_to_fullsymm<UpLo>(m_matrix,_dest,m_perm.indices().data());
+    }
+    
+    template<typename DestType,unsigned int DestUpLo> void evalTo(SparseSelfAdjointView<DestType,DestUpLo>& dest) const
+    {
+      internal::permute_symm_to_symm<UpLo,DestUpLo>(m_matrix,dest.matrix(),m_perm.indices().data());
+    }
+    
+  protected:
+    const MatrixTypeNested m_matrix;
+    const Perm& m_perm;
+
+};
+
+#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseSparseProduct.h b/extern/Eigen3/Eigen/src/Sparse/SparseSparseProduct.h
new file mode 100644
index 00000000000..19abcd1f8e4
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseSparseProduct.h
@@ -0,0 +1,401 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSESPARSEPRODUCT_H
+#define EIGEN_SPARSESPARSEPRODUCT_H
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, typename ResultType>
+static void sparse_product_impl2(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+{
+  typedef typename remove_all<Lhs>::type::Scalar Scalar;
+  typedef typename remove_all<Lhs>::type::Index Index;
+
+  // make sure to call innerSize/outerSize since we fake the storage order.
+  Index rows = lhs.innerSize();
+  Index cols = rhs.outerSize();
+  eigen_assert(lhs.outerSize() == rhs.innerSize());
+
+  std::vector<bool> mask(rows,false);
+  Matrix<Scalar,Dynamic,1> values(rows);
+  Matrix<Index,Dynamic,1>    indices(rows);
+
+  // estimate the number of non zero entries
+  float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
+  float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
+  float ratioRes = (std::min)(ratioLhs * avgNnzPerRhsColumn, 1.f);
+
+//  int t200 = rows/(log2(200)*1.39);
+//  int t = (rows*100)/139;
+
+  res.resize(rows, cols);
+  res.reserve(Index(ratioRes*rows*cols));
+  // we compute each column of the result, one after the other
+  for (Index j=0; j<cols; ++j)
+  {
+
+    res.startVec(j);
+    Index nnz = 0;
+    for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
+    {
+      Scalar y = rhsIt.value();
+      Index k = rhsIt.index();
+      for (typename Lhs::InnerIterator lhsIt(lhs, k); lhsIt; ++lhsIt)
+      {
+        Index i = lhsIt.index();
+        Scalar x = lhsIt.value();
+        if(!mask[i])
+        {
+          mask[i] = true;
+//           values[i] = x * y;
+//           indices[nnz] = i;
+          ++nnz;
+        }
+        else
+          values[i] += x * y;
+      }
+    }
+    // FIXME reserve nnz non zeros
+    // FIXME implement fast sort algorithms for very small nnz
+    // if the result is sparse enough => use a quick sort
+    // otherwise => loop through the entire vector
+    // In order to avoid to perform an expensive log2 when the
+    // result is clearly very sparse we use a linear bound up to 200.
+//     if((nnz<200 && nnz<t200) || nnz * log2(nnz) < t)
+//     {
+//       if(nnz>1) std::sort(indices.data(),indices.data()+nnz);
+//       for(int k=0; k<nnz; ++k)
+//       {
+//         int i = indices[k];
+//         res.insertBackNoCheck(j,i) = values[i];
+//         mask[i] = false;
+//       }
+//     }
+//     else
+//     {
+//       // dense path
+//       for(int i=0; i<rows; ++i)
+//       {
+//         if(mask[i])
+//         {
+//           mask[i] = false;
+//           res.insertBackNoCheck(j,i) = values[i];
+//         }
+//       }
+//     }
+
+  }
+  res.finalize();
+}
+
+// perform a pseudo in-place sparse * sparse product assuming all matrices are col major
+template<typename Lhs, typename Rhs, typename ResultType>
+static void sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+{
+//   return sparse_product_impl2(lhs,rhs,res);
+
+  typedef typename remove_all<Lhs>::type::Scalar Scalar;
+  typedef typename remove_all<Lhs>::type::Index Index;
+
+  // make sure to call innerSize/outerSize since we fake the storage order.
+  Index rows = lhs.innerSize();
+  Index cols = rhs.outerSize();
+  //int size = lhs.outerSize();
+  eigen_assert(lhs.outerSize() == rhs.innerSize());
+
+  // allocate a temporary buffer
+  AmbiVector<Scalar,Index> tempVector(rows);
+
+  // estimate the number of non zero entries
+  float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
+  float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
+  float ratioRes = (std::min)(ratioLhs * avgNnzPerRhsColumn, 1.f);
+
+  // mimics a resizeByInnerOuter:
+  if(ResultType::IsRowMajor)
+    res.resize(cols, rows);
+  else
+    res.resize(rows, cols);
+
+  res.reserve(Index(ratioRes*rows*cols));
+  for (Index j=0; j<cols; ++j)
+  {
+    // let's do a more accurate determination of the nnz ratio for the current column j of res
+    //float ratioColRes = (std::min)(ratioLhs * rhs.innerNonZeros(j), 1.f);
+    // FIXME find a nice way to get the number of nonzeros of a sub matrix (here an inner vector)
+    float ratioColRes = ratioRes;
+    tempVector.init(ratioColRes);
+    tempVector.setZero();
+    for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
+    {
+      // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
+      tempVector.restart();
+      Scalar x = rhsIt.value();
+      for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
+      {
+        tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
+      }
+    }
+    res.startVec(j);
+    for (typename AmbiVector<Scalar,Index>::Iterator it(tempVector); it; ++it)
+      res.insertBackByOuterInner(j,it.index()) = it.value();
+  }
+  res.finalize();
+}
+
+template<typename Lhs, typename Rhs, typename ResultType,
+  int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
+  int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
+  int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
+struct sparse_product_selector;
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
+{
+  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+//     std::cerr << __LINE__ << "\n";
+    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
+    sparse_product_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res);
+    res.swap(_res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+//     std::cerr << __LINE__ << "\n";
+    // we need a col-major matrix to hold the result
+    typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
+    SparseTemporaryType _res(res.rows(), res.cols());
+    sparse_product_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res);
+    res = _res;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+//     std::cerr << __LINE__ << "\n";
+    // let's transpose the product to get a column x column product
+    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
+    sparse_product_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res);
+    res.swap(_res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+//     std::cerr << "here...\n";
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+    ColMajorMatrix colLhs(lhs);
+    ColMajorMatrix colRhs(rhs);
+//     std::cerr << "more...\n";
+    sparse_product_impl<ColMajorMatrix,ColMajorMatrix,ResultType>(colLhs, colRhs, res);
+//     std::cerr << "OK.\n";
+
+    // let's transpose the product to get a column x column product
+
+//     typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
+//     SparseTemporaryType _res(res.cols(), res.rows());
+//     sparse_product_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
+//     res = _res.transpose();
+  }
+};
+
+// NOTE the 2 others cases (col row *) must never occur since they are caught
+// by ProductReturnType which transforms it to (col col *) by evaluating rhs.
+
+} // end namespace internal
+
+// sparse = sparse * sparse
+template<typename Derived>
+template<typename Lhs, typename Rhs>
+inline Derived& SparseMatrixBase<Derived>::operator=(const SparseSparseProduct<Lhs,Rhs>& product)
+{
+//   std::cerr << "there..." << typeid(Lhs).name() << "  " << typeid(Lhs).name() << " " << (Derived::Flags&&RowMajorBit) << "\n";
+  internal::sparse_product_selector<
+    typename internal::remove_all<Lhs>::type,
+    typename internal::remove_all<Rhs>::type,
+    Derived>::run(product.lhs(),product.rhs(),derived());
+  return derived();
+}
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, typename ResultType,
+  int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
+  int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
+  int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
+struct sparse_product_selector2;
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector2<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
+{
+  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    sparse_product_impl2<Lhs,Rhs,ResultType>(lhs, rhs, res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector2<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+      // prevent warnings until the code is fixed
+      EIGEN_UNUSED_VARIABLE(lhs);
+      EIGEN_UNUSED_VARIABLE(rhs);
+      EIGEN_UNUSED_VARIABLE(res);
+
+//     typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+//     RowMajorMatrix rhsRow = rhs;
+//     RowMajorMatrix resRow(res.rows(), res.cols());
+//     sparse_product_impl2<RowMajorMatrix,Lhs,RowMajorMatrix>(rhsRow, lhs, resRow);
+//     res = resRow;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector2<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+    RowMajorMatrix lhsRow = lhs;
+    RowMajorMatrix resRow(res.rows(), res.cols());
+    sparse_product_impl2<Rhs,RowMajorMatrix,RowMajorMatrix>(rhs, lhsRow, resRow);
+    res = resRow;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector2<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+    RowMajorMatrix resRow(res.rows(), res.cols());
+    sparse_product_impl2<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
+    res = resRow;
+  }
+};
+
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector2<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
+{
+  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+    ColMajorMatrix resCol(res.rows(), res.cols());
+    sparse_product_impl2<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);
+    res = resCol;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector2<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+    ColMajorMatrix lhsCol = lhs;
+    ColMajorMatrix resCol(res.rows(), res.cols());
+    sparse_product_impl2<ColMajorMatrix,Rhs,ColMajorMatrix>(lhsCol, rhs, resCol);
+    res = resCol;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector2<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+    ColMajorMatrix rhsCol = rhs;
+    ColMajorMatrix resCol(res.rows(), res.cols());
+    sparse_product_impl2<Lhs,ColMajorMatrix,ColMajorMatrix>(lhs, rhsCol, resCol);
+    res = resCol;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_product_selector2<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+//     ColMajorMatrix lhsTr(lhs);
+//     ColMajorMatrix rhsTr(rhs);
+//     ColMajorMatrix aux(res.rows(), res.cols());
+//     sparse_product_impl2<Rhs,Lhs,ColMajorMatrix>(rhs, lhs, aux);
+// //     ColMajorMatrix aux2 = aux.transpose();
+//     res = aux;
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+    ColMajorMatrix lhsCol(lhs);
+    ColMajorMatrix rhsCol(rhs);
+    ColMajorMatrix resCol(res.rows(), res.cols());
+    sparse_product_impl2<ColMajorMatrix,ColMajorMatrix,ColMajorMatrix>(lhsCol, rhsCol, resCol);
+    res = resCol;
+  }
+};
+
+} // end namespace internal
+
+template<typename Derived>
+template<typename Lhs, typename Rhs>
+inline void SparseMatrixBase<Derived>::_experimentalNewProduct(const Lhs& lhs, const Rhs& rhs)
+{
+  //derived().resize(lhs.rows(), rhs.cols());
+  internal::sparse_product_selector2<
+    typename internal::remove_all<Lhs>::type,
+    typename internal::remove_all<Rhs>::type,
+    Derived>::run(lhs,rhs,derived());
+}
+
+// sparse * sparse
+template<typename Derived>
+template<typename OtherDerived>
+inline const typename SparseSparseProductReturnType<Derived,OtherDerived>::Type
+SparseMatrixBase<Derived>::operator*(const SparseMatrixBase<OtherDerived> &other) const
+{
+  return typename SparseSparseProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
+}
+
+#endif // EIGEN_SPARSESPARSEPRODUCT_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseTranspose.h b/extern/Eigen3/Eigen/src/Sparse/SparseTranspose.h
new file mode 100644
index 00000000000..2aea2fa32c7
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseTranspose.h
@@ -0,0 +1,68 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSETRANSPOSE_H
+#define EIGEN_SPARSETRANSPOSE_H
+
+template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>
+  : public SparseMatrixBase<Transpose<MatrixType> >
+{
+    typedef typename internal::remove_all<typename MatrixType::Nested>::type _MatrixTypeNested;
+  public:
+
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Transpose<MatrixType>)
+
+    class InnerIterator;
+    class ReverseInnerIterator;
+
+    inline Index nonZeros() const { return derived().nestedExpression().nonZeros(); }
+};
+
+template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>::InnerIterator
+  : public _MatrixTypeNested::InnerIterator
+{
+    typedef typename _MatrixTypeNested::InnerIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const TransposeImpl& trans, Index outer)
+      : Base(trans.derived().nestedExpression(), outer)
+    {}
+    inline Index row() const { return Base::col(); }
+    inline Index col() const { return Base::row(); }
+};
+
+template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>::ReverseInnerIterator
+  : public _MatrixTypeNested::ReverseInnerIterator
+{
+    typedef typename _MatrixTypeNested::ReverseInnerIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE ReverseInnerIterator(const TransposeImpl& xpr, Index outer)
+      : Base(xpr.derived().nestedExpression(), outer)
+    {}
+    inline Index row() const { return Base::col(); }
+    inline Index col() const { return Base::row(); }
+};
+
+#endif // EIGEN_SPARSETRANSPOSE_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseTriangularView.h b/extern/Eigen3/Eigen/src/Sparse/SparseTriangularView.h
new file mode 100644
index 00000000000..319eaf06638
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseTriangularView.h
@@ -0,0 +1,100 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_TRIANGULARVIEW_H
+#define EIGEN_SPARSE_TRIANGULARVIEW_H
+
+namespace internal {
+  
+template<typename MatrixType, int Mode>
+struct traits<SparseTriangularView<MatrixType,Mode> >
+: public traits<MatrixType>
+{};
+
+} // namespace internal
+
+template<typename MatrixType, int Mode> class SparseTriangularView
+  : public SparseMatrixBase<SparseTriangularView<MatrixType,Mode> >
+{
+    enum { SkipFirst = (Mode==Lower && !(MatrixType::Flags&RowMajorBit))
+                    || (Mode==Upper &&  (MatrixType::Flags&RowMajorBit)) };
+  public:
+    
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseTriangularView)
+
+    class InnerIterator;
+
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+
+    typedef typename internal::conditional<internal::must_nest_by_value<MatrixType>::ret,
+        MatrixType, const MatrixType&>::type MatrixTypeNested;
+
+    inline SparseTriangularView(const MatrixType& matrix) : m_matrix(matrix) {}
+
+    /** \internal */
+    inline const MatrixType& nestedExpression() const { return m_matrix; }
+
+    template<typename OtherDerived>
+    typename internal::plain_matrix_type_column_major<OtherDerived>::type
+    solve(const MatrixBase<OtherDerived>& other) const;
+
+    template<typename OtherDerived> void solveInPlace(MatrixBase<OtherDerived>& other) const;
+    template<typename OtherDerived> void solveInPlace(SparseMatrixBase<OtherDerived>& other) const;
+
+  protected:
+    MatrixTypeNested m_matrix;
+};
+
+template<typename MatrixType, int Mode>
+class SparseTriangularView<MatrixType,Mode>::InnerIterator : public MatrixType::InnerIterator
+{
+    typedef typename MatrixType::InnerIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const SparseTriangularView& view, Index outer)
+      : Base(view.nestedExpression(), outer)
+    {
+      if(SkipFirst)
+        while((*this) && this->index()<outer)
+          ++(*this);
+    }
+    inline Index row() const { return Base::row(); }
+    inline Index col() const { return Base::col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const
+    {
+      return SkipFirst ? Base::operator bool() : (Base::operator bool() && this->index() <= this->outer());
+    }
+};
+
+template<typename Derived>
+template<int Mode>
+inline const SparseTriangularView<Derived, Mode>
+SparseMatrixBase<Derived>::triangularView() const
+{
+  return derived();
+}
+
+#endif // EIGEN_SPARSE_TRIANGULARVIEW_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseUtil.h b/extern/Eigen3/Eigen/src/Sparse/SparseUtil.h
new file mode 100644
index 00000000000..db9ae98e7a0
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseUtil.h
@@ -0,0 +1,130 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEUTIL_H
+#define EIGEN_SPARSEUTIL_H
+
+#ifdef NDEBUG
+#define EIGEN_DBG_SPARSE(X)
+#else
+#define EIGEN_DBG_SPARSE(X) X
+#endif
+
+#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, Op) \
+template<typename OtherDerived> \
+EIGEN_STRONG_INLINE Derived& operator Op(const Eigen::SparseMatrixBase<OtherDerived>& other) \
+{ \
+  return Base::operator Op(other.derived()); \
+} \
+EIGEN_STRONG_INLINE Derived& operator Op(const Derived& other) \
+{ \
+  return Base::operator Op(other); \
+}
+
+#define EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, Op) \
+template<typename Other> \
+EIGEN_STRONG_INLINE Derived& operator Op(const Other& scalar) \
+{ \
+  return Base::operator Op(scalar); \
+}
+
+#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATORS(Derived) \
+EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =) \
+EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, +=) \
+EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, -=) \
+EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, *=) \
+EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, /=)
+
+#define _EIGEN_SPARSE_PUBLIC_INTERFACE(Derived, BaseClass) \
+  typedef BaseClass Base; \
+  typedef typename Eigen::internal::traits<Derived>::Scalar Scalar; \
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; \
+  typedef typename Eigen::internal::nested<Derived>::type Nested; \
+  typedef typename Eigen::internal::traits<Derived>::StorageKind StorageKind; \
+  typedef typename Eigen::internal::traits<Derived>::Index Index; \
+  enum { RowsAtCompileTime = Eigen::internal::traits<Derived>::RowsAtCompileTime, \
+        ColsAtCompileTime = Eigen::internal::traits<Derived>::ColsAtCompileTime, \
+        Flags = Eigen::internal::traits<Derived>::Flags, \
+        CoeffReadCost = Eigen::internal::traits<Derived>::CoeffReadCost, \
+        SizeAtCompileTime = Base::SizeAtCompileTime, \
+        IsVectorAtCompileTime = Base::IsVectorAtCompileTime }; \
+  using Base::derived; \
+  using Base::const_cast_derived;
+
+#define EIGEN_SPARSE_PUBLIC_INTERFACE(Derived) \
+  _EIGEN_SPARSE_PUBLIC_INTERFACE(Derived, Eigen::SparseMatrixBase<Derived>)
+
+const int CoherentAccessPattern     = 0x1;
+const int InnerRandomAccessPattern  = 0x2 | CoherentAccessPattern;
+const int OuterRandomAccessPattern  = 0x4 | CoherentAccessPattern;
+const int RandomAccessPattern       = 0x8 | OuterRandomAccessPattern | InnerRandomAccessPattern;
+
+template<typename Derived> class SparseMatrixBase;
+template<typename _Scalar, int _Flags = 0, typename _Index = int>  class SparseMatrix;
+template<typename _Scalar, int _Flags = 0, typename _Index = int>  class DynamicSparseMatrix;
+template<typename _Scalar, int _Flags = 0, typename _Index = int>  class SparseVector;
+template<typename _Scalar, int _Flags = 0, typename _Index = int>  class MappedSparseMatrix;
+
+template<typename MatrixType, int Size>           class SparseInnerVectorSet;
+template<typename MatrixType, int Mode>           class SparseTriangularView;
+template<typename MatrixType, unsigned int UpLo>  class SparseSelfAdjointView;
+template<typename Lhs, typename Rhs>              class SparseDiagonalProduct;
+template<typename MatrixType> class SparseView;
+
+template<typename Lhs, typename Rhs>        class SparseSparseProduct;
+template<typename Lhs, typename Rhs>        class SparseTimeDenseProduct;
+template<typename Lhs, typename Rhs>        class DenseTimeSparseProduct;
+template<typename Lhs, typename Rhs, bool Transpose> class SparseDenseOuterProduct;
+
+template<typename Lhs, typename Rhs> struct SparseSparseProductReturnType;
+template<typename Lhs, typename Rhs, int InnerSize = internal::traits<Lhs>::ColsAtCompileTime> struct DenseSparseProductReturnType;
+template<typename Lhs, typename Rhs, int InnerSize = internal::traits<Lhs>::ColsAtCompileTime> struct SparseDenseProductReturnType;
+
+namespace internal {
+
+template<typename T> struct eval<T,Sparse>
+{
+    typedef typename traits<T>::Scalar _Scalar;
+    enum {
+          _Flags = traits<T>::Flags
+    };
+
+  public:
+    typedef SparseMatrix<_Scalar, _Flags> type;
+};
+
+template<typename T> struct plain_matrix_type<T,Sparse>
+{
+  typedef typename traits<T>::Scalar _Scalar;
+    enum {
+          _Flags = traits<T>::Flags
+    };
+
+  public:
+    typedef SparseMatrix<_Scalar, _Flags> type;
+};
+
+} // end namespace internal
+
+#endif // EIGEN_SPARSEUTIL_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseVector.h b/extern/Eigen3/Eigen/src/Sparse/SparseVector.h
new file mode 100644
index 00000000000..ce4bb51a27e
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseVector.h
@@ -0,0 +1,431 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEVECTOR_H
+#define EIGEN_SPARSEVECTOR_H
+
+/** \class SparseVector
+  *
+  * \brief a sparse vector class
+  *
+  * \tparam _Scalar the scalar type, i.e. the type of the coefficients
+  *
+  * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_SPARSEVECTOR_PLUGIN.
+  */
+
+namespace internal {
+template<typename _Scalar, int _Options, typename _Index>
+struct traits<SparseVector<_Scalar, _Options, _Index> >
+{
+  typedef _Scalar Scalar;
+  typedef _Index Index;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    IsColVector = _Options & RowMajorBit ? 0 : 1,
+
+    RowsAtCompileTime = IsColVector ? Dynamic : 1,
+    ColsAtCompileTime = IsColVector ? 1 : Dynamic,
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    Flags = _Options | NestByRefBit | LvalueBit,
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    SupportedAccessPatterns = InnerRandomAccessPattern
+  };
+};
+}
+
+template<typename _Scalar, int _Options, typename _Index>
+class SparseVector
+  : public SparseMatrixBase<SparseVector<_Scalar, _Options, _Index> >
+{
+  public:
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseVector)
+    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, +=)
+    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, -=)
+//     EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, =)
+
+  protected:
+  public:
+
+    typedef SparseMatrixBase<SparseVector> SparseBase;
+    enum { IsColVector = internal::traits<SparseVector>::IsColVector };
+    
+    enum {
+      Options = _Options
+    };
+
+    CompressedStorage<Scalar,Index> m_data;
+    Index m_size;
+
+    CompressedStorage<Scalar,Index>& _data() { return m_data; }
+    CompressedStorage<Scalar,Index>& _data() const { return m_data; }
+
+  public:
+
+    EIGEN_STRONG_INLINE Index rows() const { return IsColVector ? m_size : 1; }
+    EIGEN_STRONG_INLINE Index cols() const { return IsColVector ? 1 : m_size; }
+    EIGEN_STRONG_INLINE Index innerSize() const { return m_size; }
+    EIGEN_STRONG_INLINE Index outerSize() const { return 1; }
+    EIGEN_STRONG_INLINE Index innerNonZeros(Index j) const { eigen_assert(j==0); return m_size; }
+
+    EIGEN_STRONG_INLINE const Scalar* _valuePtr() const { return &m_data.value(0); }
+    EIGEN_STRONG_INLINE Scalar* _valuePtr() { return &m_data.value(0); }
+
+    EIGEN_STRONG_INLINE const Index* _innerIndexPtr() const { return &m_data.index(0); }
+    EIGEN_STRONG_INLINE Index* _innerIndexPtr() { return &m_data.index(0); }
+
+    inline Scalar coeff(Index row, Index col) const
+    {
+      eigen_assert((IsColVector ? col : row)==0);
+      return coeff(IsColVector ? row : col);
+    }
+    inline Scalar coeff(Index i) const { return m_data.at(i); }
+
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      eigen_assert((IsColVector ? col : row)==0);
+      return coeff(IsColVector ? row : col);
+    }
+
+    /** \returns a reference to the coefficient value at given index \a i
+      * This operation involes a log(rho*size) binary search. If the coefficient does not
+      * exist yet, then a sorted insertion into a sequential buffer is performed.
+      *
+      * This insertion might be very costly if the number of nonzeros above \a i is large.
+      */
+    inline Scalar& coeffRef(Index i)
+    {
+      return m_data.atWithInsertion(i);
+    }
+
+  public:
+
+    class InnerIterator;
+
+    inline void setZero() { m_data.clear(); }
+
+    /** \returns the number of non zero coefficients */
+    inline Index nonZeros() const  { return static_cast<Index>(m_data.size()); }
+
+    inline void startVec(Index outer)
+    {
+      eigen_assert(outer==0);
+    }
+
+    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
+    {
+      eigen_assert(outer==0);
+      return insertBack(inner);
+    }
+    inline Scalar& insertBack(Index i)
+    {
+      m_data.append(0, i);
+      return m_data.value(m_data.size()-1);
+    }
+
+    inline Scalar& insert(Index row, Index col)
+    {
+      Index inner = IsColVector ? row : col;
+      Index outer = IsColVector ? col : row;
+      eigen_assert(outer==0);
+      return insert(inner);
+    }
+    Scalar& insert(Index i)
+    {
+      Index startId = 0;
+      Index p = m_data.size() - 1;
+      // TODO smart realloc
+      m_data.resize(p+2,1);
+
+      while ( (p >= startId) && (m_data.index(p) > i) )
+      {
+        m_data.index(p+1) = m_data.index(p);
+        m_data.value(p+1) = m_data.value(p);
+        --p;
+      }
+      m_data.index(p+1) = i;
+      m_data.value(p+1) = 0;
+      return m_data.value(p+1);
+    }
+
+    /**
+      */
+    inline void reserve(Index reserveSize) { m_data.reserve(reserveSize); }
+
+
+    inline void finalize() {}
+
+    void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision())
+    {
+      m_data.prune(reference,epsilon);
+    }
+
+    void resize(Index rows, Index cols)
+    {
+      eigen_assert(rows==1 || cols==1);
+      resize(IsColVector ? rows : cols);
+    }
+
+    void resize(Index newSize)
+    {
+      m_size = newSize;
+      m_data.clear();
+    }
+
+    void resizeNonZeros(Index size) { m_data.resize(size); }
+
+    inline SparseVector() : m_size(0) { resize(0); }
+
+    inline SparseVector(Index size) : m_size(0) { resize(size); }
+
+    inline SparseVector(Index rows, Index cols) : m_size(0) { resize(rows,cols); }
+
+    template<typename OtherDerived>
+    inline SparseVector(const MatrixBase<OtherDerived>& other)
+      : m_size(0)
+    {
+      *this = other.derived();
+    }
+
+    template<typename OtherDerived>
+    inline SparseVector(const SparseMatrixBase<OtherDerived>& other)
+      : m_size(0)
+    {
+      *this = other.derived();
+    }
+
+    inline SparseVector(const SparseVector& other)
+      : m_size(0)
+    {
+      *this = other.derived();
+    }
+
+    inline void swap(SparseVector& other)
+    {
+      std::swap(m_size, other.m_size);
+      m_data.swap(other.m_data);
+    }
+
+    inline SparseVector& operator=(const SparseVector& other)
+    {
+      if (other.isRValue())
+      {
+        swap(other.const_cast_derived());
+      }
+      else
+      {
+        resize(other.size());
+        m_data = other.m_data;
+      }
+      return *this;
+    }
+
+    template<typename OtherDerived>
+    inline SparseVector& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+      if (int(RowsAtCompileTime)!=int(OtherDerived::RowsAtCompileTime))
+        return Base::operator=(other.transpose());
+      else
+        return Base::operator=(other);
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename Lhs, typename Rhs>
+    inline SparseVector& operator=(const SparseSparseProduct<Lhs,Rhs>& product)
+    {
+      return Base::operator=(product);
+    }
+    #endif
+
+//       const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+//       if (needToTranspose)
+//       {
+//         // two passes algorithm:
+//         //  1 - compute the number of coeffs per dest inner vector
+//         //  2 - do the actual copy/eval
+//         // Since each coeff of the rhs has to be evaluated twice, let's evauluate it if needed
+//         typedef typename internal::nested<OtherDerived,2>::type OtherCopy;
+//         OtherCopy otherCopy(other.derived());
+//         typedef typename internal::remove_all<OtherCopy>::type _OtherCopy;
+//
+//         resize(other.rows(), other.cols());
+//         Eigen::Map<VectorXi>(m_outerIndex,outerSize()).setZero();
+//         // pass 1
+//         // FIXME the above copy could be merged with that pass
+//         for (int j=0; j<otherCopy.outerSize(); ++j)
+//           for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
+//             ++m_outerIndex[it.index()];
+//
+//         // prefix sum
+//         int count = 0;
+//         VectorXi positions(outerSize());
+//         for (int j=0; j<outerSize(); ++j)
+//         {
+//           int tmp = m_outerIndex[j];
+//           m_outerIndex[j] = count;
+//           positions[j] = count;
+//           count += tmp;
+//         }
+//         m_outerIndex[outerSize()] = count;
+//         // alloc
+//         m_data.resize(count);
+//         // pass 2
+//         for (int j=0; j<otherCopy.outerSize(); ++j)
+//           for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
+//           {
+//             int pos = positions[it.index()]++;
+//             m_data.index(pos) = j;
+//             m_data.value(pos) = it.value();
+//           }
+//
+//         return *this;
+//       }
+//       else
+//       {
+//         // there is no special optimization
+//         return SparseMatrixBase<SparseMatrix>::operator=(other.derived());
+//       }
+//     }
+
+    friend std::ostream & operator << (std::ostream & s, const SparseVector& m)
+    {
+      for (Index i=0; i<m.nonZeros(); ++i)
+        s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
+      s << std::endl;
+      return s;
+    }
+
+    // this specialized version does not seems to be faster
+//     Scalar dot(const SparseVector& other) const
+//     {
+//       int i=0, j=0;
+//       Scalar res = 0;
+//       asm("#begindot");
+//       while (i<nonZeros() && j<other.nonZeros())
+//       {
+//         if (m_data.index(i)==other.m_data.index(j))
+//         {
+//           res += m_data.value(i) * internal::conj(other.m_data.value(j));
+//           ++i; ++j;
+//         }
+//         else if (m_data.index(i)<other.m_data.index(j))
+//           ++i;
+//         else
+//           ++j;
+//       }
+//       asm("#enddot");
+//       return res;
+//     }
+
+    /** Destructor */
+    inline ~SparseVector() {}
+
+    /** Overloaded for performance */
+    Scalar sum() const;
+
+  public:
+
+    /** \deprecated use setZero() and reserve() */
+    EIGEN_DEPRECATED void startFill(Index reserve)
+    {
+      setZero();
+      m_data.reserve(reserve);
+    }
+
+    /** \deprecated use insertBack(Index,Index) */
+    EIGEN_DEPRECATED Scalar& fill(Index r, Index c)
+    {
+      eigen_assert(r==0 || c==0);
+      return fill(IsColVector ? r : c);
+    }
+
+    /** \deprecated use insertBack(Index) */
+    EIGEN_DEPRECATED Scalar& fill(Index i)
+    {
+      m_data.append(0, i);
+      return m_data.value(m_data.size()-1);
+    }
+
+    /** \deprecated use insert(Index,Index) */
+    EIGEN_DEPRECATED Scalar& fillrand(Index r, Index c)
+    {
+      eigen_assert(r==0 || c==0);
+      return fillrand(IsColVector ? r : c);
+    }
+
+    /** \deprecated use insert(Index) */
+    EIGEN_DEPRECATED Scalar& fillrand(Index i)
+    {
+      return insert(i);
+    }
+
+    /** \deprecated use finalize() */
+    EIGEN_DEPRECATED void endFill() {}
+    
+#   ifdef EIGEN_SPARSEVECTOR_PLUGIN
+#     include EIGEN_SPARSEVECTOR_PLUGIN
+#   endif
+};
+
+template<typename Scalar, int _Options, typename _Index>
+class SparseVector<Scalar,_Options,_Index>::InnerIterator
+{
+  public:
+    InnerIterator(const SparseVector& vec, Index outer=0)
+      : m_data(vec.m_data), m_id(0), m_end(static_cast<Index>(m_data.size()))
+    {
+      eigen_assert(outer==0);
+    }
+
+    InnerIterator(const CompressedStorage<Scalar,Index>& data)
+      : m_data(data), m_id(0), m_end(static_cast<Index>(m_data.size()))
+    {}
+
+    template<unsigned int Added, unsigned int Removed>
+    InnerIterator(const Flagged<SparseVector,Added,Removed>& vec, Index )
+      : m_data(vec._expression().m_data), m_id(0), m_end(m_data.size())
+    {}
+
+    inline InnerIterator& operator++() { m_id++; return *this; }
+
+    inline Scalar value() const { return m_data.value(m_id); }
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_data.value(m_id)); }
+
+    inline Index index() const { return m_data.index(m_id); }
+    inline Index row() const { return IsColVector ? index() : 0; }
+    inline Index col() const { return IsColVector ? 0 : index(); }
+
+    inline operator bool() const { return (m_id < m_end); }
+
+  protected:
+    const CompressedStorage<Scalar,Index>& m_data;
+    Index m_id;
+    const Index m_end;
+};
+
+#endif // EIGEN_SPARSEVECTOR_H
diff --git a/extern/Eigen3/Eigen/src/Sparse/SparseView.h b/extern/Eigen3/Eigen/src/Sparse/SparseView.h
new file mode 100644
index 00000000000..24306561098
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/SparseView.h
@@ -0,0 +1,109 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Daniel Lowengrub <lowdanie@gmail.com>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEVIEW_H
+#define EIGEN_SPARSEVIEW_H
+
+namespace internal {
+
+template<typename MatrixType>
+struct traits<SparseView<MatrixType> > : traits<MatrixType>
+{
+  typedef int Index;
+  typedef Sparse StorageKind;
+  enum {
+    Flags = int(traits<MatrixType>::Flags) & (RowMajorBit)
+  };
+};
+
+} // end namespace internal
+
+template<typename MatrixType>
+class SparseView : public SparseMatrixBase<SparseView<MatrixType> >
+{
+  typedef typename MatrixType::Nested MatrixTypeNested;
+  typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
+public:
+  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseView)
+
+  SparseView(const MatrixType& mat, const Scalar& m_reference = Scalar(0),
+             typename NumTraits<Scalar>::Real m_epsilon = NumTraits<Scalar>::dummy_precision()) : 
+    m_matrix(mat), m_reference(m_reference), m_epsilon(m_epsilon) {}
+
+  class InnerIterator;
+
+  inline Index rows() const { return m_matrix.rows(); }
+  inline Index cols() const { return m_matrix.cols(); }
+
+  inline Index innerSize() const { return m_matrix.innerSize(); }
+  inline Index outerSize() const { return m_matrix.outerSize(); }
+
+protected:
+  const MatrixTypeNested m_matrix;
+  Scalar m_reference;
+  typename NumTraits<Scalar>::Real m_epsilon;
+};
+
+template<typename MatrixType>
+class SparseView<MatrixType>::InnerIterator : public _MatrixTypeNested::InnerIterator
+{
+public:
+  typedef typename _MatrixTypeNested::InnerIterator IterBase;
+  InnerIterator(const SparseView& view, Index outer) :
+  IterBase(view.m_matrix, outer), m_view(view)
+  {
+    incrementToNonZero();
+  }
+
+  EIGEN_STRONG_INLINE InnerIterator& operator++()
+  {
+    IterBase::operator++();
+    incrementToNonZero();
+    return *this;
+  }
+
+  using IterBase::value;
+
+protected:
+  const SparseView& m_view;
+
+private:
+  void incrementToNonZero()
+  {
+    while(internal::isMuchSmallerThan(value(), m_view.m_reference, m_view.m_epsilon) && (bool(*this)))
+      {
+        IterBase::operator++();
+      }
+  }
+};
+
+template<typename Derived>
+const SparseView<Derived> MatrixBase<Derived>::sparseView(const Scalar& m_reference,
+                                                          typename NumTraits<Scalar>::Real m_epsilon) const
+{
+  return SparseView<Derived>(derived(), m_reference, m_epsilon);
+}
+
+#endif
diff --git a/extern/Eigen3/Eigen/src/Sparse/TriangularSolver.h b/extern/Eigen3/Eigen/src/Sparse/TriangularSolver.h
new file mode 100644
index 00000000000..62bb8bb44c9
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/Sparse/TriangularSolver.h
@@ -0,0 +1,339 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSETRIANGULARSOLVER_H
+#define EIGEN_SPARSETRIANGULARSOLVER_H
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, int Mode,
+  int UpLo = (Mode & Lower)
+           ? Lower
+           : (Mode & Upper)
+           ? Upper
+           : -1,
+  int StorageOrder = int(traits<Lhs>::Flags) & RowMajorBit>
+struct sparse_solve_triangular_selector;
+
+// forward substitution, row-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,RowMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    for(int col=0 ; col<other.cols() ; ++col)
+    {
+      for(int i=0; i<lhs.rows(); ++i)
+      {
+        Scalar tmp = other.coeff(i,col);
+        Scalar lastVal = 0;
+        int lastIndex = 0;
+        for(typename Lhs::InnerIterator it(lhs, i); it; ++it)
+        {
+          lastVal = it.value();
+          lastIndex = it.index();
+          if(lastIndex==i)
+            break;
+          tmp -= lastVal * other.coeff(lastIndex,col);
+        }
+        if (Mode & UnitDiag)
+          other.coeffRef(i,col) = tmp;
+        else
+        {
+          eigen_assert(lastIndex==i);
+          other.coeffRef(i,col) = tmp/lastVal;
+        }
+      }
+    }
+  }
+};
+
+// backward substitution, row-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,RowMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    for(int col=0 ; col<other.cols() ; ++col)
+    {
+      for(int i=lhs.rows()-1 ; i>=0 ; --i)
+      {
+        Scalar tmp = other.coeff(i,col);
+        typename Lhs::InnerIterator it(lhs, i);
+        if (it && it.index() == i)
+          ++it;
+        for(; it; ++it)
+        {
+          tmp -= it.value() * other.coeff(it.index(),col);
+        }
+
+        if (Mode & UnitDiag)
+          other.coeffRef(i,col) = tmp;
+        else
+        {
+          typename Lhs::InnerIterator it(lhs, i);
+          eigen_assert(it && it.index() == i);
+          other.coeffRef(i,col) = tmp/it.value();
+        }
+      }
+    }
+  }
+};
+
+// forward substitution, col-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,ColMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    for(int col=0 ; col<other.cols() ; ++col)
+    {
+      for(int i=0; i<lhs.cols(); ++i)
+      {
+        Scalar& tmp = other.coeffRef(i,col);
+        if (tmp!=Scalar(0)) // optimization when other is actually sparse
+        {
+          typename Lhs::InnerIterator it(lhs, i);
+          if(!(Mode & UnitDiag))
+          {
+            eigen_assert(it.index()==i);
+            tmp /= it.value();
+          }
+          if (it && it.index()==i)
+            ++it;
+          for(; it; ++it)
+            other.coeffRef(it.index(), col) -= tmp * it.value();
+        }
+      }
+    }
+  }
+};
+
+// backward substitution, col-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,ColMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    for(int col=0 ; col<other.cols() ; ++col)
+    {
+      for(int i=lhs.cols()-1; i>=0; --i)
+      {
+        Scalar& tmp = other.coeffRef(i,col);
+        if (tmp!=Scalar(0)) // optimization when other is actually sparse
+        {
+          if(!(Mode & UnitDiag))
+          {
+            // FIXME lhs.coeff(i,i) might not be always efficient while it must simply be the
+            // last element of the column !
+            other.coeffRef(i,col) /= lhs.innerVector(i).lastCoeff();
+          }
+          typename Lhs::InnerIterator it(lhs, i);
+          for(; it && it.index()<i; ++it)
+            other.coeffRef(it.index(), col) -= tmp * it.value();
+        }
+      }
+    }
+  }
+};
+
+} // end namespace internal
+
+template<typename ExpressionType,int Mode>
+template<typename OtherDerived>
+void SparseTriangularView<ExpressionType,Mode>::solveInPlace(MatrixBase<OtherDerived>& other) const
+{
+  eigen_assert(m_matrix.cols() == m_matrix.rows());
+  eigen_assert(m_matrix.cols() == other.rows());
+  eigen_assert(!(Mode & ZeroDiag));
+  eigen_assert((Mode & (Upper|Lower)) != 0);
+
+  enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
+
+  typedef typename internal::conditional<copy,
+    typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
+  OtherCopy otherCopy(other.derived());
+
+  internal::sparse_solve_triangular_selector<ExpressionType, typename internal::remove_reference<OtherCopy>::type, Mode>::run(m_matrix, otherCopy);
+
+  if (copy)
+    other = otherCopy;
+}
+
+template<typename ExpressionType,int Mode>
+template<typename OtherDerived>
+typename internal::plain_matrix_type_column_major<OtherDerived>::type
+SparseTriangularView<ExpressionType,Mode>::solve(const MatrixBase<OtherDerived>& other) const
+{
+  typename internal::plain_matrix_type_column_major<OtherDerived>::type res(other);
+  solveInPlace(res);
+  return res;
+}
+
+// pure sparse path
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, int Mode,
+  int UpLo = (Mode & Lower)
+           ? Lower
+           : (Mode & Upper)
+           ? Upper
+           : -1,
+  int StorageOrder = int(Lhs::Flags) & (RowMajorBit)>
+struct sparse_solve_triangular_sparse_selector;
+
+// forward substitution, col-major
+template<typename Lhs, typename Rhs, int Mode, int UpLo>
+struct sparse_solve_triangular_sparse_selector<Lhs,Rhs,Mode,UpLo,ColMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  typedef typename promote_index_type<typename traits<Lhs>::Index,
+                                         typename traits<Rhs>::Index>::type Index;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    const bool IsLower = (UpLo==Lower);
+    AmbiVector<Scalar,Index> tempVector(other.rows()*2);
+    tempVector.setBounds(0,other.rows());
+
+    Rhs res(other.rows(), other.cols());
+    res.reserve(other.nonZeros());
+
+    for(int col=0 ; col<other.cols() ; ++col)
+    {
+      // FIXME estimate number of non zeros
+      tempVector.init(.99/*float(other.col(col).nonZeros())/float(other.rows())*/);
+      tempVector.setZero();
+      tempVector.restart();
+      for (typename Rhs::InnerIterator rhsIt(other, col); rhsIt; ++rhsIt)
+      {
+        tempVector.coeffRef(rhsIt.index()) = rhsIt.value();
+      }
+
+      for(int i=IsLower?0:lhs.cols()-1;
+          IsLower?i<lhs.cols():i>=0;
+          i+=IsLower?1:-1)
+      {
+        tempVector.restart();
+        Scalar& ci = tempVector.coeffRef(i);
+        if (ci!=Scalar(0))
+        {
+          // find
+          typename Lhs::InnerIterator it(lhs, i);
+          if(!(Mode & UnitDiag))
+          {
+            if (IsLower)
+            {
+              eigen_assert(it.index()==i);
+              ci /= it.value();
+            }
+            else
+              ci /= lhs.coeff(i,i);
+          }
+          tempVector.restart();
+          if (IsLower)
+          {
+            if (it.index()==i)
+              ++it;
+            for(; it; ++it)
+              tempVector.coeffRef(it.index()) -= ci * it.value();
+          }
+          else
+          {
+            for(; it && it.index()<i; ++it)
+              tempVector.coeffRef(it.index()) -= ci * it.value();
+          }
+        }
+      }
+
+
+      int count = 0;
+      // FIXME compute a reference value to filter zeros
+      for (typename AmbiVector<Scalar,Index>::Iterator it(tempVector/*,1e-12*/); it; ++it)
+      {
+        ++ count;
+//         std::cerr << "fill " << it.index() << ", " << col << "\n";
+//         std::cout << it.value() << "  ";
+        // FIXME use insertBack
+        res.insert(it.index(), col) = it.value();
+      }
+//       std::cout << "tempVector.nonZeros() == " << int(count) << " / " << (other.rows()) << "\n";
+    }
+    res.finalize();
+    other = res.markAsRValue();
+  }
+};
+
+} // end namespace internal
+
+template<typename ExpressionType,int Mode>
+template<typename OtherDerived>
+void SparseTriangularView<ExpressionType,Mode>::solveInPlace(SparseMatrixBase<OtherDerived>& other) const
+{
+  eigen_assert(m_matrix.cols() == m_matrix.rows());
+  eigen_assert(m_matrix.cols() == other.rows());
+  eigen_assert(!(Mode & ZeroDiag));
+  eigen_assert((Mode & (Upper|Lower)) != 0);
+
+//   enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
+
+//   typedef typename internal::conditional<copy,
+//     typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
+//   OtherCopy otherCopy(other.derived());
+
+  internal::sparse_solve_triangular_sparse_selector<ExpressionType, OtherDerived, Mode>::run(m_matrix, other.derived());
+
+//   if (copy)
+//     other = otherCopy;
+}
+
+#ifdef EIGEN2_SUPPORT
+
+// deprecated stuff:
+
+/** \deprecated */
+template<typename Derived>
+template<typename OtherDerived>
+void SparseMatrixBase<Derived>::solveTriangularInPlace(MatrixBase<OtherDerived>& other) const
+{
+  this->template triangular<Flags&(Upper|Lower)>().solveInPlace(other);
+}
+
+/** \deprecated */
+template<typename Derived>
+template<typename OtherDerived>
+typename internal::plain_matrix_type_column_major<OtherDerived>::type
+SparseMatrixBase<Derived>::solveTriangular(const MatrixBase<OtherDerived>& other) const
+{
+  typename internal::plain_matrix_type_column_major<OtherDerived>::type res(other);
+  derived().solveTriangularInPlace(res);
+  return res;
+}
+#endif // EIGEN2_SUPPORT
+
+#endif // EIGEN_SPARSETRIANGULARSOLVER_H
diff --git a/extern/Eigen3/Eigen/src/SparseCholesky/CMakeLists.txt b/extern/Eigen3/Eigen/src/SparseCholesky/CMakeLists.txt
new file mode 100644
index 00000000000..375a59d7a57
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCholesky/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_SparseCholesky_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_SparseCholesky_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SparseCholesky COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h b/extern/Eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h
new file mode 100644
index 00000000000..5a1255a27a3
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h
@@ -0,0 +1,876 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+/*
+
+NOTE: the _symbolic, and _numeric functions has been adapted from
+      the LDL library:
+
+LDL Copyright (c) 2005 by Timothy A. Davis.  All Rights Reserved.
+
+LDL License:
+
+    Your use or distribution of LDL or any modified version of
+    LDL implies that you agree to this License.
+
+    This library is free software; you can redistribute it and/or
+    modify it under the terms of the GNU Lesser General Public
+    License as published by the Free Software Foundation; either
+    version 2.1 of the License, or (at your option) any later version.
+
+    This library is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+    Lesser General Public License for more details.
+
+    You should have received a copy of the GNU Lesser General Public
+    License along with this library; if not, write to the Free Software
+    Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301
+    USA
+
+    Permission is hereby granted to use or copy this program under the
+    terms of the GNU LGPL, provided that the Copyright, this License,
+    and the Availability of the original version is retained on all copies.
+    User documentation of any code that uses this code or any modified
+    version of this code must cite the Copyright, this License, the
+    Availability note, and "Used by permission." Permission to modify
+    the code and to distribute modified code is granted, provided the
+    Copyright, this License, and the Availability note are retained,
+    and a notice that the code was modified is included.
+ */
+
+#ifndef EIGEN_SIMPLICIAL_CHOLESKY_H
+#define EIGEN_SIMPLICIAL_CHOLESKY_H
+
+namespace Eigen { 
+
+enum SimplicialCholeskyMode {
+  SimplicialCholeskyLLT,
+  SimplicialCholeskyLDLT
+};
+
+/** \ingroup SparseCholesky_Module
+  * \brief A direct sparse Cholesky factorizations
+  *
+  * These classes provide LL^T and LDL^T Cholesky factorizations of sparse matrices that are
+  * selfadjoint and positive definite. The factorization allows for solving A.X = B where
+  * X and B can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  *
+  */
+template<typename Derived>
+class SimplicialCholeskyBase : internal::noncopyable
+{
+  public:
+    typedef typename internal::traits<Derived>::MatrixType MatrixType;
+    enum { UpLo = internal::traits<Derived>::UpLo };
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::Index Index;
+    typedef SparseMatrix<Scalar,ColMajor,Index> CholMatrixType;
+    typedef Matrix<Scalar,Dynamic,1> VectorType;
+
+  public:
+
+    /** Default constructor */
+    SimplicialCholeskyBase()
+      : m_info(Success), m_isInitialized(false), m_shiftOffset(0), m_shiftScale(1)
+    {}
+
+    SimplicialCholeskyBase(const MatrixType& matrix)
+      : m_info(Success), m_isInitialized(false), m_shiftOffset(0), m_shiftScale(1)
+    {
+      derived().compute(matrix);
+    }
+
+    ~SimplicialCholeskyBase()
+    {
+    }
+
+    Derived& derived() { return *static_cast<Derived*>(this); }
+    const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    
+    inline Index cols() const { return m_matrix.cols(); }
+    inline Index rows() const { return m_matrix.rows(); }
+    
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was succesful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+    
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const internal::solve_retval<SimplicialCholeskyBase, Rhs>
+    solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "Simplicial LLT or LDLT is not initialized.");
+      eigen_assert(rows()==b.rows()
+                && "SimplicialCholeskyBase::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::solve_retval<SimplicialCholeskyBase, Rhs>(*this, b.derived());
+    }
+    
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const internal::sparse_solve_retval<SimplicialCholeskyBase, Rhs>
+    solve(const SparseMatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "Simplicial LLT or LDLT is not initialized.");
+      eigen_assert(rows()==b.rows()
+                && "SimplicialCholesky::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::sparse_solve_retval<SimplicialCholeskyBase, Rhs>(*this, b.derived());
+    }
+    
+    /** \returns the permutation P
+      * \sa permutationPinv() */
+    const PermutationMatrix<Dynamic,Dynamic,Index>& permutationP() const
+    { return m_P; }
+    
+    /** \returns the inverse P^-1 of the permutation P
+      * \sa permutationP() */
+    const PermutationMatrix<Dynamic,Dynamic,Index>& permutationPinv() const
+    { return m_Pinv; }
+
+    /** Sets the shift parameters that will be used to adjust the diagonal coefficients during the numerical factorization.
+      *
+      * During the numerical factorization, the diagonal coefficients are transformed by the following linear model:\n
+      * \c d_ii = \a offset + \a scale * \c d_ii
+      *
+      * The default is the identity transformation with \a offset=0, and \a scale=1.
+      *
+      * \returns a reference to \c *this.
+      */
+    Derived& setShift(const RealScalar& offset, const RealScalar& scale = 1)
+    {
+      m_shiftOffset = offset;
+      m_shiftScale = scale;
+      return derived();
+    }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal */
+    template<typename Stream>
+    void dumpMemory(Stream& s)
+    {
+      int total = 0;
+      s << "  L:        " << ((total+=(m_matrix.cols()+1) * sizeof(int) + m_matrix.nonZeros()*(sizeof(int)+sizeof(Scalar))) >> 20) << "Mb" << "\n";
+      s << "  diag:     " << ((total+=m_diag.size() * sizeof(Scalar)) >> 20) << "Mb" << "\n";
+      s << "  tree:     " << ((total+=m_parent.size() * sizeof(int)) >> 20) << "Mb" << "\n";
+      s << "  nonzeros: " << ((total+=m_nonZerosPerCol.size() * sizeof(int)) >> 20) << "Mb" << "\n";
+      s << "  perm:     " << ((total+=m_P.size() * sizeof(int)) >> 20) << "Mb" << "\n";
+      s << "  perm^-1:  " << ((total+=m_Pinv.size() * sizeof(int)) >> 20) << "Mb" << "\n";
+      s << "  TOTAL:    " << (total>> 20) << "Mb" << "\n";
+    }
+
+    /** \internal */
+    template<typename Rhs,typename Dest>
+    void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
+    {
+      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
+      eigen_assert(m_matrix.rows()==b.rows());
+
+      if(m_info!=Success)
+        return;
+
+      if(m_P.size()>0)
+        dest = m_Pinv * b;
+      else
+        dest = b;
+
+      if(m_matrix.nonZeros()>0) // otherwise L==I
+        derived().matrixL().solveInPlace(dest);
+
+      if(m_diag.size()>0)
+        dest = m_diag.asDiagonal().inverse() * dest;
+
+      if (m_matrix.nonZeros()>0) // otherwise I==I
+        derived().matrixU().solveInPlace(dest);
+
+      if(m_P.size()>0)
+        dest = m_P * dest;
+    }
+
+    /** \internal */
+    template<typename Rhs, typename DestScalar, int DestOptions, typename DestIndex>
+    void _solve_sparse(const Rhs& b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const
+    {
+      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
+      eigen_assert(m_matrix.rows()==b.rows());
+      
+      // we process the sparse rhs per block of NbColsAtOnce columns temporarily stored into a dense matrix.
+      static const int NbColsAtOnce = 4;
+      int rhsCols = b.cols();
+      int size = b.rows();
+      Eigen::Matrix<DestScalar,Dynamic,Dynamic> tmp(size,rhsCols);
+      for(int k=0; k<rhsCols; k+=NbColsAtOnce)
+      {
+        int actualCols = std::min<int>(rhsCols-k, NbColsAtOnce);
+        tmp.leftCols(actualCols) = b.middleCols(k,actualCols);
+        tmp.leftCols(actualCols) = derived().solve(tmp.leftCols(actualCols));
+        dest.middleCols(k,actualCols) = tmp.leftCols(actualCols).sparseView();
+      }
+    }
+
+#endif // EIGEN_PARSED_BY_DOXYGEN
+
+  protected:
+    
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    template<bool DoLDLT>
+    void compute(const MatrixType& matrix)
+    {
+      eigen_assert(matrix.rows()==matrix.cols());
+      Index size = matrix.cols();
+      CholMatrixType ap(size,size);
+      ordering(matrix, ap);
+      analyzePattern_preordered(ap, DoLDLT);
+      factorize_preordered<DoLDLT>(ap);
+    }
+    
+    template<bool DoLDLT>
+    void factorize(const MatrixType& a)
+    {
+      eigen_assert(a.rows()==a.cols());
+      int size = a.cols();
+      CholMatrixType ap(size,size);
+      ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_Pinv);
+      factorize_preordered<DoLDLT>(ap);
+    }
+
+    template<bool DoLDLT>
+    void factorize_preordered(const CholMatrixType& a);
+
+    void analyzePattern(const MatrixType& a, bool doLDLT)
+    {
+      eigen_assert(a.rows()==a.cols());
+      int size = a.cols();
+      CholMatrixType ap(size,size);
+      ordering(a, ap);
+      analyzePattern_preordered(ap,doLDLT);
+    }
+    void analyzePattern_preordered(const CholMatrixType& a, bool doLDLT);
+    
+    void ordering(const MatrixType& a, CholMatrixType& ap);
+
+    /** keeps off-diagonal entries; drops diagonal entries */
+    struct keep_diag {
+      inline bool operator() (const Index& row, const Index& col, const Scalar&) const
+      {
+        return row!=col;
+      }
+    };
+
+    mutable ComputationInfo m_info;
+    bool m_isInitialized;
+    bool m_factorizationIsOk;
+    bool m_analysisIsOk;
+    
+    CholMatrixType m_matrix;
+    VectorType m_diag;                                // the diagonal coefficients (LDLT mode)
+    VectorXi m_parent;                                // elimination tree
+    VectorXi m_nonZerosPerCol;
+    PermutationMatrix<Dynamic,Dynamic,Index> m_P;     // the permutation
+    PermutationMatrix<Dynamic,Dynamic,Index> m_Pinv;  // the inverse permutation
+
+    RealScalar m_shiftOffset;
+    RealScalar m_shiftScale;
+};
+
+template<typename _MatrixType, int _UpLo = Lower> class SimplicialLLT;
+template<typename _MatrixType, int _UpLo = Lower> class SimplicialLDLT;
+template<typename _MatrixType, int _UpLo = Lower> class SimplicialCholesky;
+
+namespace internal {
+
+template<typename _MatrixType, int _UpLo> struct traits<SimplicialLLT<_MatrixType,_UpLo> >
+{
+  typedef _MatrixType MatrixType;
+  enum { UpLo = _UpLo };
+  typedef typename MatrixType::Scalar                         Scalar;
+  typedef typename MatrixType::Index                          Index;
+  typedef SparseMatrix<Scalar, ColMajor, Index>               CholMatrixType;
+  typedef SparseTriangularView<CholMatrixType, Eigen::Lower>  MatrixL;
+  typedef SparseTriangularView<typename CholMatrixType::AdjointReturnType, Eigen::Upper>   MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return m; }
+  static inline MatrixU getU(const MatrixType& m) { return m.adjoint(); }
+};
+
+template<typename _MatrixType,int _UpLo> struct traits<SimplicialLDLT<_MatrixType,_UpLo> >
+{
+  typedef _MatrixType MatrixType;
+  enum { UpLo = _UpLo };
+  typedef typename MatrixType::Scalar                             Scalar;
+  typedef typename MatrixType::Index                              Index;
+  typedef SparseMatrix<Scalar, ColMajor, Index>                   CholMatrixType;
+  typedef SparseTriangularView<CholMatrixType, Eigen::UnitLower>  MatrixL;
+  typedef SparseTriangularView<typename CholMatrixType::AdjointReturnType, Eigen::UnitUpper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return m; }
+  static inline MatrixU getU(const MatrixType& m) { return m.adjoint(); }
+};
+
+template<typename _MatrixType, int _UpLo> struct traits<SimplicialCholesky<_MatrixType,_UpLo> >
+{
+  typedef _MatrixType MatrixType;
+  enum { UpLo = _UpLo };
+};
+
+}
+
+/** \ingroup SparseCholesky_Module
+  * \class SimplicialLLT
+  * \brief A direct sparse LLT Cholesky factorizations
+  *
+  * This class provides a LL^T Cholesky factorizations of sparse matrices that are
+  * selfadjoint and positive definite. The factorization allows for solving A.X = B where
+  * X and B can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  *
+  * \sa class SimplicialLDLT
+  */
+template<typename _MatrixType, int _UpLo>
+    class SimplicialLLT : public SimplicialCholeskyBase<SimplicialLLT<_MatrixType,_UpLo> >
+{
+public:
+    typedef _MatrixType MatrixType;
+    enum { UpLo = _UpLo };
+    typedef SimplicialCholeskyBase<SimplicialLLT> Base;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::Index Index;
+    typedef SparseMatrix<Scalar,ColMajor,Index> CholMatrixType;
+    typedef Matrix<Scalar,Dynamic,1> VectorType;
+    typedef internal::traits<SimplicialLLT> Traits;
+    typedef typename Traits::MatrixL  MatrixL;
+    typedef typename Traits::MatrixU  MatrixU;
+public:
+    /** Default constructor */
+    SimplicialLLT() : Base() {}
+    /** Constructs and performs the LLT factorization of \a matrix */
+    SimplicialLLT(const MatrixType& matrix)
+        : Base(matrix) {}
+
+    /** \returns an expression of the factor L */
+    inline const MatrixL matrixL() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
+        return Traits::getL(Base::m_matrix);
+    }
+
+    /** \returns an expression of the factor U (= L^*) */
+    inline const MatrixU matrixU() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
+        return Traits::getU(Base::m_matrix);
+    }
+    
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    SimplicialLLT& compute(const MatrixType& matrix)
+    {
+      Base::template compute<false>(matrix);
+      return *this;
+    }
+
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      *
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& a)
+    {
+      Base::analyzePattern(a, false);
+    }
+
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& a)
+    {
+      Base::template factorize<false>(a);
+    }
+
+    /** \returns the determinant of the underlying matrix from the current factorization */
+    Scalar determinant() const
+    {
+      Scalar detL = Base::m_matrix.diagonal().prod();
+      return internal::abs2(detL);
+    }
+};
+
+/** \ingroup SparseCholesky_Module
+  * \class SimplicialLDLT
+  * \brief A direct sparse LDLT Cholesky factorizations without square root.
+  *
+  * This class provides a LDL^T Cholesky factorizations without square root of sparse matrices that are
+  * selfadjoint and positive definite. The factorization allows for solving A.X = B where
+  * X and B can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  *
+  * \sa class SimplicialLLT
+  */
+template<typename _MatrixType, int _UpLo>
+    class SimplicialLDLT : public SimplicialCholeskyBase<SimplicialLDLT<_MatrixType,_UpLo> >
+{
+public:
+    typedef _MatrixType MatrixType;
+    enum { UpLo = _UpLo };
+    typedef SimplicialCholeskyBase<SimplicialLDLT> Base;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::Index Index;
+    typedef SparseMatrix<Scalar,ColMajor,Index> CholMatrixType;
+    typedef Matrix<Scalar,Dynamic,1> VectorType;
+    typedef internal::traits<SimplicialLDLT> Traits;
+    typedef typename Traits::MatrixL  MatrixL;
+    typedef typename Traits::MatrixU  MatrixU;
+public:
+    /** Default constructor */
+    SimplicialLDLT() : Base() {}
+
+    /** Constructs and performs the LLT factorization of \a matrix */
+    SimplicialLDLT(const MatrixType& matrix)
+        : Base(matrix) {}
+
+    /** \returns a vector expression of the diagonal D */
+    inline const VectorType vectorD() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+        return Base::m_diag;
+    }
+    /** \returns an expression of the factor L */
+    inline const MatrixL matrixL() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+        return Traits::getL(Base::m_matrix);
+    }
+
+    /** \returns an expression of the factor U (= L^*) */
+    inline const MatrixU matrixU() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+        return Traits::getU(Base::m_matrix);
+    }
+
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    SimplicialLDLT& compute(const MatrixType& matrix)
+    {
+      Base::template compute<true>(matrix);
+      return *this;
+    }
+    
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      *
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& a)
+    {
+      Base::analyzePattern(a, true);
+    }
+
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& a)
+    {
+      Base::template factorize<true>(a);
+    }
+
+    /** \returns the determinant of the underlying matrix from the current factorization */
+    Scalar determinant() const
+    {
+      return Base::m_diag.prod();
+    }
+};
+
+/** \deprecated use SimplicialLDLT or class SimplicialLLT
+  * \ingroup SparseCholesky_Module
+  * \class SimplicialCholesky
+  *
+  * \sa class SimplicialLDLT, class SimplicialLLT
+  */
+template<typename _MatrixType, int _UpLo>
+    class SimplicialCholesky : public SimplicialCholeskyBase<SimplicialCholesky<_MatrixType,_UpLo> >
+{
+public:
+    typedef _MatrixType MatrixType;
+    enum { UpLo = _UpLo };
+    typedef SimplicialCholeskyBase<SimplicialCholesky> Base;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::Index Index;
+    typedef SparseMatrix<Scalar,ColMajor,Index> CholMatrixType;
+    typedef Matrix<Scalar,Dynamic,1> VectorType;
+    typedef internal::traits<SimplicialCholesky> Traits;
+    typedef internal::traits<SimplicialLDLT<MatrixType,UpLo> > LDLTTraits;
+    typedef internal::traits<SimplicialLLT<MatrixType,UpLo>  > LLTTraits;
+  public:
+    SimplicialCholesky() : Base(), m_LDLT(true) {}
+
+    SimplicialCholesky(const MatrixType& matrix)
+      : Base(), m_LDLT(true)
+    {
+      compute(matrix);
+    }
+
+    SimplicialCholesky& setMode(SimplicialCholeskyMode mode)
+    {
+      switch(mode)
+      {
+      case SimplicialCholeskyLLT:
+        m_LDLT = false;
+        break;
+      case SimplicialCholeskyLDLT:
+        m_LDLT = true;
+        break;
+      default:
+        break;
+      }
+
+      return *this;
+    }
+
+    inline const VectorType vectorD() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial Cholesky not factorized");
+        return Base::m_diag;
+    }
+    inline const CholMatrixType rawMatrix() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial Cholesky not factorized");
+        return Base::m_matrix;
+    }
+    
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    SimplicialCholesky& compute(const MatrixType& matrix)
+    {
+      if(m_LDLT)
+        Base::template compute<true>(matrix);
+      else
+        Base::template compute<false>(matrix);
+      return *this;
+    }
+
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      *
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& a)
+    {
+      Base::analyzePattern(a, m_LDLT);
+    }
+
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& a)
+    {
+      if(m_LDLT)
+        Base::template factorize<true>(a);
+      else
+        Base::template factorize<false>(a);
+    }
+
+    /** \internal */
+    template<typename Rhs,typename Dest>
+    void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
+    {
+      eigen_assert(Base::m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
+      eigen_assert(Base::m_matrix.rows()==b.rows());
+
+      if(Base::m_info!=Success)
+        return;
+
+      if(Base::m_P.size()>0)
+        dest = Base::m_Pinv * b;
+      else
+        dest = b;
+
+      if(Base::m_matrix.nonZeros()>0) // otherwise L==I
+      {
+        if(m_LDLT)
+          LDLTTraits::getL(Base::m_matrix).solveInPlace(dest);
+        else
+          LLTTraits::getL(Base::m_matrix).solveInPlace(dest);
+      }
+
+      if(Base::m_diag.size()>0)
+        dest = Base::m_diag.asDiagonal().inverse() * dest;
+
+      if (Base::m_matrix.nonZeros()>0) // otherwise I==I
+      {
+        if(m_LDLT)
+          LDLTTraits::getU(Base::m_matrix).solveInPlace(dest);
+        else
+          LLTTraits::getU(Base::m_matrix).solveInPlace(dest);
+      }
+
+      if(Base::m_P.size()>0)
+        dest = Base::m_P * dest;
+    }
+    
+    Scalar determinant() const
+    {
+      if(m_LDLT)
+      {
+        return Base::m_diag.prod();
+      }
+      else
+      {
+        Scalar detL = Diagonal<const CholMatrixType>(Base::m_matrix).prod();
+        return internal::abs2(detL);
+      }
+    }
+    
+  protected:
+    bool m_LDLT;
+};
+
+template<typename Derived>
+void SimplicialCholeskyBase<Derived>::ordering(const MatrixType& a, CholMatrixType& ap)
+{
+  eigen_assert(a.rows()==a.cols());
+  const Index size = a.rows();
+  // TODO allows to configure the permutation
+  {
+    CholMatrixType C;
+    C = a.template selfadjointView<UpLo>();
+    // remove diagonal entries:
+    // seems not to be needed
+    // C.prune(keep_diag());
+    internal::minimum_degree_ordering(C, m_P);
+  }
+
+  if(m_P.size()>0)
+    m_Pinv  = m_P.inverse();
+  else
+    m_Pinv.resize(0);
+
+  ap.resize(size,size);
+  ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_Pinv);
+}
+
+template<typename Derived>
+void SimplicialCholeskyBase<Derived>::analyzePattern_preordered(const CholMatrixType& ap, bool doLDLT)
+{
+  const Index size = ap.rows();
+  m_matrix.resize(size, size);
+  m_parent.resize(size);
+  m_nonZerosPerCol.resize(size);
+  
+  ei_declare_aligned_stack_constructed_variable(Index, tags, size, 0);
+
+  for(Index k = 0; k < size; ++k)
+  {
+    /* L(k,:) pattern: all nodes reachable in etree from nz in A(0:k-1,k) */
+    m_parent[k] = -1;             /* parent of k is not yet known */
+    tags[k] = k;                  /* mark node k as visited */
+    m_nonZerosPerCol[k] = 0;      /* count of nonzeros in column k of L */
+    for(typename CholMatrixType::InnerIterator it(ap,k); it; ++it)
+    {
+      Index i = it.index();
+      if(i < k)
+      {
+        /* follow path from i to root of etree, stop at flagged node */
+        for(; tags[i] != k; i = m_parent[i])
+        {
+          /* find parent of i if not yet determined */
+          if (m_parent[i] == -1)
+            m_parent[i] = k;
+          m_nonZerosPerCol[i]++;        /* L (k,i) is nonzero */
+          tags[i] = k;                  /* mark i as visited */
+        }
+      }
+    }
+  }
+  
+  /* construct Lp index array from m_nonZerosPerCol column counts */
+  Index* Lp = m_matrix.outerIndexPtr();
+  Lp[0] = 0;
+  for(Index k = 0; k < size; ++k)
+    Lp[k+1] = Lp[k] + m_nonZerosPerCol[k] + (doLDLT ? 0 : 1);
+
+  m_matrix.resizeNonZeros(Lp[size]);
+  
+  m_isInitialized     = true;
+  m_info              = Success;
+  m_analysisIsOk      = true;
+  m_factorizationIsOk = false;
+}
+
+
+template<typename Derived>
+template<bool DoLDLT>
+void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType& ap)
+{
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  eigen_assert(ap.rows()==ap.cols());
+  const Index size = ap.rows();
+  eigen_assert(m_parent.size()==size);
+  eigen_assert(m_nonZerosPerCol.size()==size);
+
+  const Index* Lp = m_matrix.outerIndexPtr();
+  Index* Li = m_matrix.innerIndexPtr();
+  Scalar* Lx = m_matrix.valuePtr();
+
+  ei_declare_aligned_stack_constructed_variable(Scalar, y, size, 0);
+  ei_declare_aligned_stack_constructed_variable(Index,  pattern, size, 0);
+  ei_declare_aligned_stack_constructed_variable(Index,  tags, size, 0);
+  
+  bool ok = true;
+  m_diag.resize(DoLDLT ? size : 0);
+  
+  for(Index k = 0; k < size; ++k)
+  {
+    // compute nonzero pattern of kth row of L, in topological order
+    y[k] = 0.0;                     // Y(0:k) is now all zero
+    Index top = size;               // stack for pattern is empty
+    tags[k] = k;                    // mark node k as visited
+    m_nonZerosPerCol[k] = 0;        // count of nonzeros in column k of L
+    for(typename MatrixType::InnerIterator it(ap,k); it; ++it)
+    {
+      Index i = it.index();
+      if(i <= k)
+      {
+        y[i] += internal::conj(it.value());            /* scatter A(i,k) into Y (sum duplicates) */
+        Index len;
+        for(len = 0; tags[i] != k; i = m_parent[i])
+        {
+          pattern[len++] = i;     /* L(k,i) is nonzero */
+          tags[i] = k;            /* mark i as visited */
+        }
+        while(len > 0)
+          pattern[--top] = pattern[--len];
+      }
+    }
+
+    /* compute numerical values kth row of L (a sparse triangular solve) */
+
+    RealScalar d = internal::real(y[k]) * m_shiftScale + m_shiftOffset;    // get D(k,k), apply the shift function, and clear Y(k)
+    y[k] = 0.0;
+    for(; top < size; ++top)
+    {
+      Index i = pattern[top];       /* pattern[top:n-1] is pattern of L(:,k) */
+      Scalar yi = y[i];             /* get and clear Y(i) */
+      y[i] = 0.0;
+      
+      /* the nonzero entry L(k,i) */
+      Scalar l_ki;
+      if(DoLDLT)
+        l_ki = yi / m_diag[i];       
+      else
+        yi = l_ki = yi / Lx[Lp[i]];
+      
+      Index p2 = Lp[i] + m_nonZerosPerCol[i];
+      Index p;
+      for(p = Lp[i] + (DoLDLT ? 0 : 1); p < p2; ++p)
+        y[Li[p]] -= internal::conj(Lx[p]) * yi;
+      d -= internal::real(l_ki * internal::conj(yi));
+      Li[p] = k;                          /* store L(k,i) in column form of L */
+      Lx[p] = l_ki;
+      ++m_nonZerosPerCol[i];              /* increment count of nonzeros in col i */
+    }
+    if(DoLDLT)
+    {
+      m_diag[k] = d;
+      if(d == RealScalar(0))
+      {
+        ok = false;                         /* failure, D(k,k) is zero */
+        break;
+      }
+    }
+    else
+    {
+      Index p = Lp[k] + m_nonZerosPerCol[k]++;
+      Li[p] = k ;                /* store L(k,k) = sqrt (d) in column k */
+      if(d <= RealScalar(0)) {
+        ok = false;              /* failure, matrix is not positive definite */
+        break;
+      }
+      Lx[p] = internal::sqrt(d) ;
+    }
+  }
+
+  m_info = ok ? Success : NumericalIssue;
+  m_factorizationIsOk = true;
+}
+
+namespace internal {
+  
+template<typename Derived, typename Rhs>
+struct solve_retval<SimplicialCholeskyBase<Derived>, Rhs>
+  : solve_retval_base<SimplicialCholeskyBase<Derived>, Rhs>
+{
+  typedef SimplicialCholeskyBase<Derived> Dec;
+  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec().derived()._solve(rhs(),dst);
+  }
+};
+
+template<typename Derived, typename Rhs>
+struct sparse_solve_retval<SimplicialCholeskyBase<Derived>, Rhs>
+  : sparse_solve_retval_base<SimplicialCholeskyBase<Derived>, Rhs>
+{
+  typedef SimplicialCholeskyBase<Derived> Dec;
+  EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec().derived()._solve_sparse(rhs(),dst);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SIMPLICIAL_CHOLESKY_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/AmbiVector.h b/extern/Eigen3/Eigen/src/SparseCore/AmbiVector.h
new file mode 100644
index 00000000000..8ec63107a55
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/AmbiVector.h
@@ -0,0 +1,386 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_AMBIVECTOR_H
+#define EIGEN_AMBIVECTOR_H
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal
+  * Hybrid sparse/dense vector class designed for intensive read-write operations.
+  *
+  * See BasicSparseLLT and SparseProduct for usage examples.
+  */
+template<typename _Scalar, typename _Index>
+class AmbiVector
+{
+  public:
+    typedef _Scalar Scalar;
+    typedef _Index Index;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    AmbiVector(Index size)
+      : m_buffer(0), m_zero(0), m_size(0), m_allocatedSize(0), m_allocatedElements(0), m_mode(-1)
+    {
+      resize(size);
+    }
+
+    void init(double estimatedDensity);
+    void init(int mode);
+
+    Index nonZeros() const;
+
+    /** Specifies a sub-vector to work on */
+    void setBounds(Index start, Index end) { m_start = start; m_end = end; }
+
+    void setZero();
+
+    void restart();
+    Scalar& coeffRef(Index i);
+    Scalar& coeff(Index i);
+
+    class Iterator;
+
+    ~AmbiVector() { delete[] m_buffer; }
+
+    void resize(Index size)
+    {
+      if (m_allocatedSize < size)
+        reallocate(size);
+      m_size = size;
+    }
+
+    Index size() const { return m_size; }
+
+  protected:
+
+    void reallocate(Index size)
+    {
+      // if the size of the matrix is not too large, let's allocate a bit more than needed such
+      // that we can handle dense vector even in sparse mode.
+      delete[] m_buffer;
+      if (size<1000)
+      {
+        Index allocSize = (size * sizeof(ListEl))/sizeof(Scalar);
+        m_allocatedElements = (allocSize*sizeof(Scalar))/sizeof(ListEl);
+        m_buffer = new Scalar[allocSize];
+      }
+      else
+      {
+        m_allocatedElements = (size*sizeof(Scalar))/sizeof(ListEl);
+        m_buffer = new Scalar[size];
+      }
+      m_size = size;
+      m_start = 0;
+      m_end = m_size;
+    }
+
+    void reallocateSparse()
+    {
+      Index copyElements = m_allocatedElements;
+      m_allocatedElements = (std::min)(Index(m_allocatedElements*1.5),m_size);
+      Index allocSize = m_allocatedElements * sizeof(ListEl);
+      allocSize = allocSize/sizeof(Scalar) + (allocSize%sizeof(Scalar)>0?1:0);
+      Scalar* newBuffer = new Scalar[allocSize];
+      memcpy(newBuffer,  m_buffer,  copyElements * sizeof(ListEl));
+      delete[] m_buffer;
+      m_buffer = newBuffer;
+    }
+
+  protected:
+    // element type of the linked list
+    struct ListEl
+    {
+      Index next;
+      Index index;
+      Scalar value;
+    };
+
+    // used to store data in both mode
+    Scalar* m_buffer;
+    Scalar m_zero;
+    Index m_size;
+    Index m_start;
+    Index m_end;
+    Index m_allocatedSize;
+    Index m_allocatedElements;
+    Index m_mode;
+
+    // linked list mode
+    Index m_llStart;
+    Index m_llCurrent;
+    Index m_llSize;
+};
+
+/** \returns the number of non zeros in the current sub vector */
+template<typename _Scalar,typename _Index>
+_Index AmbiVector<_Scalar,_Index>::nonZeros() const
+{
+  if (m_mode==IsSparse)
+    return m_llSize;
+  else
+    return m_end - m_start;
+}
+
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::init(double estimatedDensity)
+{
+  if (estimatedDensity>0.1)
+    init(IsDense);
+  else
+    init(IsSparse);
+}
+
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::init(int mode)
+{
+  m_mode = mode;
+  if (m_mode==IsSparse)
+  {
+    m_llSize = 0;
+    m_llStart = -1;
+  }
+}
+
+/** Must be called whenever we might perform a write access
+  * with an index smaller than the previous one.
+  *
+  * Don't worry, this function is extremely cheap.
+  */
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::restart()
+{
+  m_llCurrent = m_llStart;
+}
+
+/** Set all coefficients of current subvector to zero */
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::setZero()
+{
+  if (m_mode==IsDense)
+  {
+    for (Index i=m_start; i<m_end; ++i)
+      m_buffer[i] = Scalar(0);
+  }
+  else
+  {
+    eigen_assert(m_mode==IsSparse);
+    m_llSize = 0;
+    m_llStart = -1;
+  }
+}
+
+template<typename _Scalar,typename _Index>
+_Scalar& AmbiVector<_Scalar,_Index>::coeffRef(_Index i)
+{
+  if (m_mode==IsDense)
+    return m_buffer[i];
+  else
+  {
+    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+    // TODO factorize the following code to reduce code generation
+    eigen_assert(m_mode==IsSparse);
+    if (m_llSize==0)
+    {
+      // this is the first element
+      m_llStart = 0;
+      m_llCurrent = 0;
+      ++m_llSize;
+      llElements[0].value = Scalar(0);
+      llElements[0].index = i;
+      llElements[0].next = -1;
+      return llElements[0].value;
+    }
+    else if (i<llElements[m_llStart].index)
+    {
+      // this is going to be the new first element of the list
+      ListEl& el = llElements[m_llSize];
+      el.value = Scalar(0);
+      el.index = i;
+      el.next = m_llStart;
+      m_llStart = m_llSize;
+      ++m_llSize;
+      m_llCurrent = m_llStart;
+      return el.value;
+    }
+    else
+    {
+      Index nextel = llElements[m_llCurrent].next;
+      eigen_assert(i>=llElements[m_llCurrent].index && "you must call restart() before inserting an element with lower or equal index");
+      while (nextel >= 0 && llElements[nextel].index<=i)
+      {
+        m_llCurrent = nextel;
+        nextel = llElements[nextel].next;
+      }
+
+      if (llElements[m_llCurrent].index==i)
+      {
+        // the coefficient already exists and we found it !
+        return llElements[m_llCurrent].value;
+      }
+      else
+      {
+        if (m_llSize>=m_allocatedElements)
+        {
+          reallocateSparse();
+          llElements = reinterpret_cast<ListEl*>(m_buffer);
+        }
+        eigen_internal_assert(m_llSize<m_allocatedElements && "internal error: overflow in sparse mode");
+        // let's insert a new coefficient
+        ListEl& el = llElements[m_llSize];
+        el.value = Scalar(0);
+        el.index = i;
+        el.next = llElements[m_llCurrent].next;
+        llElements[m_llCurrent].next = m_llSize;
+        ++m_llSize;
+        return el.value;
+      }
+    }
+  }
+}
+
+template<typename _Scalar,typename _Index>
+_Scalar& AmbiVector<_Scalar,_Index>::coeff(_Index i)
+{
+  if (m_mode==IsDense)
+    return m_buffer[i];
+  else
+  {
+    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+    eigen_assert(m_mode==IsSparse);
+    if ((m_llSize==0) || (i<llElements[m_llStart].index))
+    {
+      return m_zero;
+    }
+    else
+    {
+      Index elid = m_llStart;
+      while (elid >= 0 && llElements[elid].index<i)
+        elid = llElements[elid].next;
+
+      if (llElements[elid].index==i)
+        return llElements[m_llCurrent].value;
+      else
+        return m_zero;
+    }
+  }
+}
+
+/** Iterator over the nonzero coefficients */
+template<typename _Scalar,typename _Index>
+class AmbiVector<_Scalar,_Index>::Iterator
+{
+  public:
+    typedef _Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    /** Default constructor
+      * \param vec the vector on which we iterate
+      * \param epsilon the minimal value used to prune zero coefficients.
+      * In practice, all coefficients having a magnitude smaller than \a epsilon
+      * are skipped.
+      */
+    Iterator(const AmbiVector& vec, RealScalar epsilon = 0)
+      : m_vector(vec)
+    {
+      m_epsilon = epsilon;
+      m_isDense = m_vector.m_mode==IsDense;
+      if (m_isDense)
+      {
+        m_currentEl = 0;   // this is to avoid a compilation warning
+        m_cachedValue = 0; // this is to avoid a compilation warning
+        m_cachedIndex = m_vector.m_start-1;
+        ++(*this);
+      }
+      else
+      {
+        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+        m_currentEl = m_vector.m_llStart;
+        while (m_currentEl>=0 && internal::abs(llElements[m_currentEl].value)<=m_epsilon)
+          m_currentEl = llElements[m_currentEl].next;
+        if (m_currentEl<0)
+        {
+          m_cachedValue = 0; // this is to avoid a compilation warning
+          m_cachedIndex = -1;
+        }
+        else
+        {
+          m_cachedIndex = llElements[m_currentEl].index;
+          m_cachedValue = llElements[m_currentEl].value;
+        }
+      }
+    }
+
+    Index index() const { return m_cachedIndex; }
+    Scalar value() const { return m_cachedValue; }
+
+    operator bool() const { return m_cachedIndex>=0; }
+
+    Iterator& operator++()
+    {
+      if (m_isDense)
+      {
+        do {
+          ++m_cachedIndex;
+        } while (m_cachedIndex<m_vector.m_end && internal::abs(m_vector.m_buffer[m_cachedIndex])<m_epsilon);
+        if (m_cachedIndex<m_vector.m_end)
+          m_cachedValue = m_vector.m_buffer[m_cachedIndex];
+        else
+          m_cachedIndex=-1;
+      }
+      else
+      {
+        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+        do {
+          m_currentEl = llElements[m_currentEl].next;
+        } while (m_currentEl>=0 && internal::abs(llElements[m_currentEl].value)<m_epsilon);
+        if (m_currentEl<0)
+        {
+          m_cachedIndex = -1;
+        }
+        else
+        {
+          m_cachedIndex = llElements[m_currentEl].index;
+          m_cachedValue = llElements[m_currentEl].value;
+        }
+      }
+      return *this;
+    }
+
+  protected:
+    const AmbiVector& m_vector; // the target vector
+    Index m_currentEl;            // the current element in sparse/linked-list mode
+    RealScalar m_epsilon;       // epsilon used to prune zero coefficients
+    Index m_cachedIndex;          // current coordinate
+    Scalar m_cachedValue;       // current value
+    bool m_isDense;             // mode of the vector
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_AMBIVECTOR_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/CMakeLists.txt b/extern/Eigen3/Eigen/src/SparseCore/CMakeLists.txt
new file mode 100644
index 00000000000..d860452a65c
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_SparseCore_SRCS "*.h")
+
+INSTALL(FILES 
+  ${Eigen_SparseCore_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SparseCore COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/SparseCore/CompressedStorage.h b/extern/Eigen3/Eigen/src/SparseCore/CompressedStorage.h
new file mode 100644
index 00000000000..fa2bfd763cb
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/CompressedStorage.h
@@ -0,0 +1,248 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_COMPRESSED_STORAGE_H
+#define EIGEN_COMPRESSED_STORAGE_H
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal
+  * Stores a sparse set of values as a list of values and a list of indices.
+  *
+  */
+template<typename _Scalar,typename _Index>
+class CompressedStorage
+{
+  public:
+
+    typedef _Scalar Scalar;
+    typedef _Index Index;
+
+  protected:
+
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  public:
+
+    CompressedStorage()
+      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
+    {}
+
+    CompressedStorage(size_t size)
+      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
+    {
+      resize(size);
+    }
+
+    CompressedStorage(const CompressedStorage& other)
+      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
+    {
+      *this = other;
+    }
+
+    CompressedStorage& operator=(const CompressedStorage& other)
+    {
+      resize(other.size());
+      memcpy(m_values, other.m_values, m_size * sizeof(Scalar));
+      memcpy(m_indices, other.m_indices, m_size * sizeof(Index));
+      return *this;
+    }
+
+    void swap(CompressedStorage& other)
+    {
+      std::swap(m_values, other.m_values);
+      std::swap(m_indices, other.m_indices);
+      std::swap(m_size, other.m_size);
+      std::swap(m_allocatedSize, other.m_allocatedSize);
+    }
+
+    ~CompressedStorage()
+    {
+      delete[] m_values;
+      delete[] m_indices;
+    }
+
+    void reserve(size_t size)
+    {
+      size_t newAllocatedSize = m_size + size;
+      if (newAllocatedSize > m_allocatedSize)
+        reallocate(newAllocatedSize);
+    }
+
+    void squeeze()
+    {
+      if (m_allocatedSize>m_size)
+        reallocate(m_size);
+    }
+
+    void resize(size_t size, float reserveSizeFactor = 0)
+    {
+      if (m_allocatedSize<size)
+        reallocate(size + size_t(reserveSizeFactor*size));
+      m_size = size;
+    }
+
+    void append(const Scalar& v, Index i)
+    {
+      Index id = static_cast<Index>(m_size);
+      resize(m_size+1, 1);
+      m_values[id] = v;
+      m_indices[id] = i;
+    }
+
+    inline size_t size() const { return m_size; }
+    inline size_t allocatedSize() const { return m_allocatedSize; }
+    inline void clear() { m_size = 0; }
+
+    inline Scalar& value(size_t i) { return m_values[i]; }
+    inline const Scalar& value(size_t i) const { return m_values[i]; }
+
+    inline Index& index(size_t i) { return m_indices[i]; }
+    inline const Index& index(size_t i) const { return m_indices[i]; }
+
+    static CompressedStorage Map(Index* indices, Scalar* values, size_t size)
+    {
+      CompressedStorage res;
+      res.m_indices = indices;
+      res.m_values = values;
+      res.m_allocatedSize = res.m_size = size;
+      return res;
+    }
+
+    /** \returns the largest \c k such that for all \c j in [0,k) index[\c j]\<\a key */
+    inline Index searchLowerIndex(Index key) const
+    {
+      return searchLowerIndex(0, m_size, key);
+    }
+
+    /** \returns the largest \c k in [start,end) such that for all \c j in [start,k) index[\c j]\<\a key */
+    inline Index searchLowerIndex(size_t start, size_t end, Index key) const
+    {
+      while(end>start)
+      {
+        size_t mid = (end+start)>>1;
+        if (m_indices[mid]<key)
+          start = mid+1;
+        else
+          end = mid;
+      }
+      return static_cast<Index>(start);
+    }
+
+    /** \returns the stored value at index \a key
+      * If the value does not exist, then the value \a defaultValue is returned without any insertion. */
+    inline Scalar at(Index key, Scalar defaultValue = Scalar(0)) const
+    {
+      if (m_size==0)
+        return defaultValue;
+      else if (key==m_indices[m_size-1])
+        return m_values[m_size-1];
+      // ^^  optimization: let's first check if it is the last coefficient
+      // (very common in high level algorithms)
+      const size_t id = searchLowerIndex(0,m_size-1,key);
+      return ((id<m_size) && (m_indices[id]==key)) ? m_values[id] : defaultValue;
+    }
+
+    /** Like at(), but the search is performed in the range [start,end) */
+    inline Scalar atInRange(size_t start, size_t end, Index key, Scalar defaultValue = Scalar(0)) const
+    {
+      if (start>=end)
+        return Scalar(0);
+      else if (end>start && key==m_indices[end-1])
+        return m_values[end-1];
+      // ^^  optimization: let's first check if it is the last coefficient
+      // (very common in high level algorithms)
+      const size_t id = searchLowerIndex(start,end-1,key);
+      return ((id<end) && (m_indices[id]==key)) ? m_values[id] : defaultValue;
+    }
+
+    /** \returns a reference to the value at index \a key
+      * If the value does not exist, then the value \a defaultValue is inserted
+      * such that the keys are sorted. */
+    inline Scalar& atWithInsertion(Index key, Scalar defaultValue = Scalar(0))
+    {
+      size_t id = searchLowerIndex(0,m_size,key);
+      if (id>=m_size || m_indices[id]!=key)
+      {
+        resize(m_size+1,1);
+        for (size_t j=m_size-1; j>id; --j)
+        {
+          m_indices[j] = m_indices[j-1];
+          m_values[j] = m_values[j-1];
+        }
+        m_indices[id] = key;
+        m_values[id] = defaultValue;
+      }
+      return m_values[id];
+    }
+
+    void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision())
+    {
+      size_t k = 0;
+      size_t n = size();
+      for (size_t i=0; i<n; ++i)
+      {
+        if (!internal::isMuchSmallerThan(value(i), reference, epsilon))
+        {
+          value(k) = value(i);
+          index(k) = index(i);
+          ++k;
+        }
+      }
+      resize(k,0);
+    }
+
+  protected:
+
+    inline void reallocate(size_t size)
+    {
+      Scalar* newValues  = new Scalar[size];
+      Index* newIndices = new Index[size];
+      size_t copySize = (std::min)(size, m_size);
+      // copy
+      internal::smart_copy(m_values, m_values+copySize, newValues);
+      internal::smart_copy(m_indices, m_indices+copySize, newIndices);
+      // delete old stuff
+      delete[] m_values;
+      delete[] m_indices;
+      m_values = newValues;
+      m_indices = newIndices;
+      m_allocatedSize = size;
+    }
+
+  protected:
+    Scalar* m_values;
+    Index* m_indices;
+    size_t m_size;
+    size_t m_allocatedSize;
+
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMPRESSED_STORAGE_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/extern/Eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
new file mode 100644
index 00000000000..0fb4c1c97c0
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
@@ -0,0 +1,260 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
+#define EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, typename ResultType>
+static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+{
+  typedef typename remove_all<Lhs>::type::Scalar Scalar;
+  typedef typename remove_all<Lhs>::type::Index Index;
+
+  // make sure to call innerSize/outerSize since we fake the storage order.
+  Index rows = lhs.innerSize();
+  Index cols = rhs.outerSize();
+  eigen_assert(lhs.outerSize() == rhs.innerSize());
+
+  std::vector<bool> mask(rows,false);
+  Matrix<Scalar,Dynamic,1> values(rows);
+  Matrix<Index,Dynamic,1>  indices(rows);
+
+  // estimate the number of non zero entries
+  // given a rhs column containing Y non zeros, we assume that the respective Y columns
+  // of the lhs differs in average of one non zeros, thus the number of non zeros for
+  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
+  // per column of the lhs.
+  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
+  Index estimated_nnz_prod = lhs.nonZeros() + rhs.nonZeros();
+
+  res.setZero();
+  res.reserve(Index(estimated_nnz_prod));
+  // we compute each column of the result, one after the other
+  for (Index j=0; j<cols; ++j)
+  {
+
+    res.startVec(j);
+    Index nnz = 0;
+    for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
+    {
+      Scalar y = rhsIt.value();
+      Index k = rhsIt.index();
+      for (typename Lhs::InnerIterator lhsIt(lhs, k); lhsIt; ++lhsIt)
+      {
+        Index i = lhsIt.index();
+        Scalar x = lhsIt.value();
+        if(!mask[i])
+        {
+          mask[i] = true;
+          values[i] = x * y;
+          indices[nnz] = i;
+          ++nnz;
+        }
+        else
+          values[i] += x * y;
+      }
+    }
+
+    // unordered insertion
+    for(int k=0; k<nnz; ++k)
+    {
+      int i = indices[k];
+      res.insertBackByOuterInnerUnordered(j,i) = values[i];
+      mask[i] = false;
+    }
+
+#if 0
+    // alternative ordered insertion code:
+
+    int t200 = rows/(log2(200)*1.39);
+    int t = (rows*100)/139;
+
+    // FIXME reserve nnz non zeros
+    // FIXME implement fast sort algorithms for very small nnz
+    // if the result is sparse enough => use a quick sort
+    // otherwise => loop through the entire vector
+    // In order to avoid to perform an expensive log2 when the
+    // result is clearly very sparse we use a linear bound up to 200.
+    //if((nnz<200 && nnz<t200) || nnz * log2(nnz) < t)
+    //res.startVec(j);
+    if(true)
+    {
+      if(nnz>1) std::sort(indices.data(),indices.data()+nnz);
+      for(int k=0; k<nnz; ++k)
+      {
+        int i = indices[k];
+        res.insertBackByOuterInner(j,i) = values[i];
+        mask[i] = false;
+      }
+    }
+    else
+    {
+      // dense path
+      for(int i=0; i<rows; ++i)
+      {
+        if(mask[i])
+        {
+          mask[i] = false;
+          res.insertBackByOuterInner(j,i) = values[i];
+        }
+      }
+    }
+#endif
+
+  }
+  res.finalize();
+}
+
+
+} // end namespace internal
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, typename ResultType,
+  int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
+  int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
+  int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
+struct conservative_sparse_sparse_product_selector;
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
+{
+  typedef typename remove_all<Lhs>::type LhsCleaned;
+  typedef typename LhsCleaned::Scalar Scalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+    ColMajorMatrix resCol(lhs.rows(),rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);
+    // sort the non zeros:
+    RowMajorMatrix resRow(resCol);
+    res = resRow;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+     typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+     RowMajorMatrix rhsRow = rhs;
+     RowMajorMatrix resRow(lhs.rows(), rhs.cols());
+     internal::conservative_sparse_sparse_product_impl<RowMajorMatrix,Lhs,RowMajorMatrix>(rhsRow, lhs, resRow);
+     res = resRow;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+    RowMajorMatrix lhsRow = lhs;
+    RowMajorMatrix resRow(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Rhs,RowMajorMatrix,RowMajorMatrix>(rhs, lhsRow, resRow);
+    res = resRow;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+    RowMajorMatrix resRow(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
+    res = resRow;
+  }
+};
+
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
+{
+  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+    ColMajorMatrix resCol(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);
+    res = resCol;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+    ColMajorMatrix lhsCol = lhs;
+    ColMajorMatrix resCol(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<ColMajorMatrix,Rhs,ColMajorMatrix>(lhsCol, rhs, resCol);
+    res = resCol;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+    ColMajorMatrix rhsCol = rhs;
+    ColMajorMatrix resCol(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Lhs,ColMajorMatrix,ColMajorMatrix>(lhs, rhsCol, resCol);
+    res = resCol;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+    RowMajorMatrix resRow(lhs.rows(),rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
+    // sort the non zeros:
+    ColMajorMatrix resCol(resRow);
+    res = resCol;
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/CoreIterators.h b/extern/Eigen3/Eigen/src/SparseCore/CoreIterators.h
new file mode 100644
index 00000000000..ea51e92312a
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/CoreIterators.h
@@ -0,0 +1,76 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_COREITERATORS_H
+#define EIGEN_COREITERATORS_H
+
+namespace Eigen { 
+
+/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
+ */
+
+/** \ingroup SparseCore_Module
+  * \class InnerIterator
+  * \brief An InnerIterator allows to loop over the element of a sparse (or dense) matrix or expression
+  *
+  * todo
+  */
+
+// generic version for dense matrix and expressions
+template<typename Derived> class DenseBase<Derived>::InnerIterator
+{
+  protected:
+    typedef typename Derived::Scalar Scalar;
+    typedef typename Derived::Index Index;
+
+    enum { IsRowMajor = (Derived::Flags&RowMajorBit)==RowMajorBit };
+  public:
+    EIGEN_STRONG_INLINE InnerIterator(const Derived& expr, Index outer)
+      : m_expression(expr), m_inner(0), m_outer(outer), m_end(expr.innerSize())
+    {}
+
+    EIGEN_STRONG_INLINE Scalar value() const
+    {
+      return (IsRowMajor) ? m_expression.coeff(m_outer, m_inner)
+                          : m_expression.coeff(m_inner, m_outer);
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() { m_inner++; return *this; }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_inner; }
+    inline Index row() const { return IsRowMajor ? m_outer : index(); }
+    inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
+
+  protected:
+    const Derived& m_expression;
+    Index m_inner;
+    const Index m_outer;
+    const Index m_end;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_COREITERATORS_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h b/extern/Eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h
new file mode 100644
index 00000000000..fc7f9d14327
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h
@@ -0,0 +1,194 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_MAPPED_SPARSEMATRIX_H
+#define EIGEN_MAPPED_SPARSEMATRIX_H
+
+namespace Eigen { 
+
+/** \class MappedSparseMatrix
+  *
+  * \brief Sparse matrix
+  *
+  * \param _Scalar the scalar type, i.e. the type of the coefficients
+  *
+  * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
+  *
+  */
+namespace internal {
+template<typename _Scalar, int _Flags, typename _Index>
+struct traits<MappedSparseMatrix<_Scalar, _Flags, _Index> > : traits<SparseMatrix<_Scalar, _Flags, _Index> >
+{};
+}
+
+template<typename _Scalar, int _Flags, typename _Index>
+class MappedSparseMatrix
+  : public SparseMatrixBase<MappedSparseMatrix<_Scalar, _Flags, _Index> >
+{
+  public:
+    EIGEN_SPARSE_PUBLIC_INTERFACE(MappedSparseMatrix)
+    enum { IsRowMajor = Base::IsRowMajor };
+
+  protected:
+
+    Index   m_outerSize;
+    Index   m_innerSize;
+    Index   m_nnz;
+    Index*  m_outerIndex;
+    Index*  m_innerIndices;
+    Scalar* m_values;
+
+  public:
+
+    inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
+    inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
+    inline Index innerSize() const { return m_innerSize; }
+    inline Index outerSize() const { return m_outerSize; }
+
+    //----------------------------------------
+    // direct access interface
+    inline const Scalar* valuePtr() const { return m_values; }
+    inline Scalar* valuePtr() { return m_values; }
+
+    inline const Index* innerIndexPtr() const { return m_innerIndices; }
+    inline Index* innerIndexPtr() { return m_innerIndices; }
+
+    inline const Index* outerIndexPtr() const { return m_outerIndex; }
+    inline Index* outerIndexPtr() { return m_outerIndex; }
+    //----------------------------------------
+
+    inline Scalar coeff(Index row, Index col) const
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index start = m_outerIndex[outer];
+      Index end = m_outerIndex[outer+1];
+      if (start==end)
+        return Scalar(0);
+      else if (end>0 && inner==m_innerIndices[end-1])
+        return m_values[end-1];
+      // ^^  optimization: let's first check if it is the last coefficient
+      // (very common in high level algorithms)
+
+      const Index* r = std::lower_bound(&m_innerIndices[start],&m_innerIndices[end-1],inner);
+      const Index id = r-&m_innerIndices[0];
+      return ((*r==inner) && (id<end)) ? m_values[id] : Scalar(0);
+    }
+
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index start = m_outerIndex[outer];
+      Index end = m_outerIndex[outer+1];
+      eigen_assert(end>=start && "you probably called coeffRef on a non finalized matrix");
+      eigen_assert(end>start && "coeffRef cannot be called on a zero coefficient");
+      Index* r = std::lower_bound(&m_innerIndices[start],&m_innerIndices[end],inner);
+      const Index id = r-&m_innerIndices[0];
+      eigen_assert((*r==inner) && (id<end) && "coeffRef cannot be called on a zero coefficient");
+      return m_values[id];
+    }
+
+    class InnerIterator;
+    class ReverseInnerIterator;
+
+    /** \returns the number of non zero coefficients */
+    inline Index nonZeros() const  { return m_nnz; }
+
+    inline MappedSparseMatrix(Index rows, Index cols, Index nnz, Index* outerIndexPtr, Index* innerIndexPtr, Scalar* valuePtr)
+      : m_outerSize(IsRowMajor?rows:cols), m_innerSize(IsRowMajor?cols:rows), m_nnz(nnz), m_outerIndex(outerIndexPtr),
+        m_innerIndices(innerIndexPtr), m_values(valuePtr)
+    {}
+
+    /** Empty destructor */
+    inline ~MappedSparseMatrix() {}
+};
+
+template<typename Scalar, int _Flags, typename _Index>
+class MappedSparseMatrix<Scalar,_Flags,_Index>::InnerIterator
+{
+  public:
+    InnerIterator(const MappedSparseMatrix& mat, Index outer)
+      : m_matrix(mat),
+        m_outer(outer),
+        m_id(mat.outerIndexPtr()[outer]),
+        m_start(m_id),
+        m_end(mat.outerIndexPtr()[outer+1])
+    {}
+
+    inline InnerIterator& operator++() { m_id++; return *this; }
+
+    inline Scalar value() const { return m_matrix.valuePtr()[m_id]; }
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.valuePtr()[m_id]); }
+
+    inline Index index() const { return m_matrix.innerIndexPtr()[m_id]; }
+    inline Index row() const { return IsRowMajor ? m_outer : index(); }
+    inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+    inline operator bool() const { return (m_id < m_end) && (m_id>=m_start); }
+
+  protected:
+    const MappedSparseMatrix& m_matrix;
+    const Index m_outer;
+    Index m_id;
+    const Index m_start;
+    const Index m_end;
+};
+
+template<typename Scalar, int _Flags, typename _Index>
+class MappedSparseMatrix<Scalar,_Flags,_Index>::ReverseInnerIterator
+{
+  public:
+    ReverseInnerIterator(const MappedSparseMatrix& mat, Index outer)
+      : m_matrix(mat),
+        m_outer(outer),
+        m_id(mat.outerIndexPtr()[outer+1]),
+        m_start(mat.outerIndexPtr()[outer]),
+        m_end(m_id)
+    {}
+
+    inline ReverseInnerIterator& operator--() { m_id--; return *this; }
+
+    inline Scalar value() const { return m_matrix.valuePtr()[m_id-1]; }
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.valuePtr()[m_id-1]); }
+
+    inline Index index() const { return m_matrix.innerIndexPtr()[m_id-1]; }
+    inline Index row() const { return IsRowMajor ? m_outer : index(); }
+    inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+    inline operator bool() const { return (m_id <= m_end) && (m_id>m_start); }
+
+  protected:
+    const MappedSparseMatrix& m_matrix;
+    const Index m_outer;
+    Index m_id;
+    const Index m_start;
+    const Index m_end;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_MAPPED_SPARSEMATRIX_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseAssign.h b/extern/Eigen3/Eigen/src/SparseCore/SparseAssign.h
new file mode 100644
index 00000000000..e69de29bb2d
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseAssign.h
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseBlock.h b/extern/Eigen3/Eigen/src/SparseCore/SparseBlock.h
new file mode 100644
index 00000000000..189538f39cf
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseBlock.h
@@ -0,0 +1,402 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_BLOCK_H
+#define EIGEN_SPARSE_BLOCK_H
+
+namespace Eigen { 
+
+namespace internal {
+template<typename MatrixType, int Size>
+struct traits<SparseInnerVectorSet<MatrixType, Size> >
+{
+  typedef typename traits<MatrixType>::Scalar Scalar;
+  typedef typename traits<MatrixType>::Index Index;
+  typedef typename traits<MatrixType>::StorageKind StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    IsRowMajor = (int(MatrixType::Flags)&RowMajorBit)==RowMajorBit,
+    Flags = MatrixType::Flags,
+    RowsAtCompileTime = IsRowMajor ? Size : MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = IsRowMajor ? MatrixType::ColsAtCompileTime : Size,
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    CoeffReadCost = MatrixType::CoeffReadCost
+  };
+};
+} // end namespace internal
+
+template<typename MatrixType, int Size>
+class SparseInnerVectorSet : internal::no_assignment_operator,
+  public SparseMatrixBase<SparseInnerVectorSet<MatrixType, Size> >
+{
+  public:
+
+    enum { IsRowMajor = internal::traits<SparseInnerVectorSet>::IsRowMajor };
+
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseInnerVectorSet)
+    class InnerIterator: public MatrixType::InnerIterator
+    {
+      public:
+        inline InnerIterator(const SparseInnerVectorSet& xpr, Index outer)
+          : MatrixType::InnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
+        {}
+        inline Index row() const { return IsRowMajor ? m_outer : this->index(); }
+        inline Index col() const { return IsRowMajor ? this->index() : m_outer; }
+      protected:
+        Index m_outer;
+    };
+    class ReverseInnerIterator: public MatrixType::ReverseInnerIterator
+    {
+      public:
+        inline ReverseInnerIterator(const SparseInnerVectorSet& xpr, Index outer)
+          : MatrixType::ReverseInnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
+        {}
+        inline Index row() const { return IsRowMajor ? m_outer : this->index(); }
+        inline Index col() const { return IsRowMajor ? this->index() : m_outer; }
+      protected:
+        Index m_outer;
+    };
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outerStart, Index outerSize)
+      : m_matrix(matrix), m_outerStart(outerStart), m_outerSize(outerSize)
+    {
+      eigen_assert( (outerStart>=0) && ((outerStart+outerSize)<=matrix.outerSize()) );
+    }
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outer)
+      : m_matrix(matrix), m_outerStart(outer), m_outerSize(Size)
+    {
+      eigen_assert(Size!=Dynamic);
+      eigen_assert( (outer>=0) && (outer<matrix.outerSize()) );
+    }
+
+//     template<typename OtherDerived>
+//     inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+//     {
+//       return *this;
+//     }
+
+//     template<typename Sparse>
+//     inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+//     {
+//       return *this;
+//     }
+
+    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+  protected:
+
+    const typename MatrixType::Nested m_matrix;
+    Index m_outerStart;
+    const internal::variable_if_dynamic<Index, Size> m_outerSize;
+};
+
+
+/***************************************************************************
+* specialisation for SparseMatrix
+***************************************************************************/
+
+template<typename _Scalar, int _Options, typename _Index, int Size>
+class SparseInnerVectorSet<SparseMatrix<_Scalar, _Options, _Index>, Size>
+  : public SparseMatrixBase<SparseInnerVectorSet<SparseMatrix<_Scalar, _Options, _Index>, Size> >
+{
+    typedef SparseMatrix<_Scalar, _Options, _Index> MatrixType;
+  public:
+
+    enum { IsRowMajor = internal::traits<SparseInnerVectorSet>::IsRowMajor };
+
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseInnerVectorSet)
+    class InnerIterator: public MatrixType::InnerIterator
+    {
+      public:
+        inline InnerIterator(const SparseInnerVectorSet& xpr, Index outer)
+          : MatrixType::InnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
+        {}
+        inline Index row() const { return IsRowMajor ? m_outer : this->index(); }
+        inline Index col() const { return IsRowMajor ? this->index() : m_outer; }
+      protected:
+        Index m_outer;
+    };
+    class ReverseInnerIterator: public MatrixType::ReverseInnerIterator
+    {
+      public:
+        inline ReverseInnerIterator(const SparseInnerVectorSet& xpr, Index outer)
+          : MatrixType::ReverseInnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
+        {}
+        inline Index row() const { return IsRowMajor ? m_outer : this->index(); }
+        inline Index col() const { return IsRowMajor ? this->index() : m_outer; }
+      protected:
+        Index m_outer;
+    };
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outerStart, Index outerSize)
+      : m_matrix(matrix), m_outerStart(outerStart), m_outerSize(outerSize)
+    {
+      eigen_assert( (outerStart>=0) && ((outerStart+outerSize)<=matrix.outerSize()) );
+    }
+
+    inline SparseInnerVectorSet(const MatrixType& matrix, Index outer)
+      : m_matrix(matrix), m_outerStart(outer), m_outerSize(Size)
+    {
+      eigen_assert(Size==1);
+      eigen_assert( (outer>=0) && (outer<matrix.outerSize()) );
+    }
+
+    template<typename OtherDerived>
+    inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+      typedef typename internal::remove_all<typename MatrixType::Nested>::type _NestedMatrixType;
+      _NestedMatrixType& matrix = const_cast<_NestedMatrixType&>(m_matrix);;
+      // This assignement is slow if this vector set is not empty
+      // and/or it is not at the end of the nonzeros of the underlying matrix.
+
+      // 1 - eval to a temporary to avoid transposition and/or aliasing issues
+      SparseMatrix<Scalar, IsRowMajor ? RowMajor : ColMajor, Index> tmp(other);
+
+      // 2 - let's check whether there is enough allocated memory
+      Index nnz           = tmp.nonZeros();
+      Index nnz_previous  = nonZeros();
+      Index free_size     = Index(matrix.data().allocatedSize()) + nnz_previous;
+      Index nnz_head      = m_outerStart==0 ? 0 : matrix.outerIndexPtr()[m_outerStart];
+      Index tail          = m_matrix.outerIndexPtr()[m_outerStart+m_outerSize.value()];
+      Index nnz_tail      = matrix.nonZeros() - tail;
+
+      if(nnz>free_size)
+      {
+        // realloc manually to reduce copies
+        typename MatrixType::Storage newdata(m_matrix.nonZeros() - nnz_previous + nnz);
+
+        std::memcpy(&newdata.value(0), &m_matrix.data().value(0), nnz_head*sizeof(Scalar));
+        std::memcpy(&newdata.index(0), &m_matrix.data().index(0), nnz_head*sizeof(Index));
+
+        std::memcpy(&newdata.value(nnz_head), &tmp.data().value(0), nnz*sizeof(Scalar));
+        std::memcpy(&newdata.index(nnz_head), &tmp.data().index(0), nnz*sizeof(Index));
+
+        std::memcpy(&newdata.value(nnz_head+nnz), &matrix.data().value(tail), nnz_tail*sizeof(Scalar));
+        std::memcpy(&newdata.index(nnz_head+nnz), &matrix.data().index(tail), nnz_tail*sizeof(Index));
+
+        matrix.data().swap(newdata);
+      }
+      else
+      {
+        // no need to realloc, simply copy the tail at its respective position and insert tmp
+        matrix.data().resize(nnz_head + nnz + nnz_tail);
+
+        if(nnz<nnz_previous)
+        {
+          std::memcpy(&matrix.data().value(nnz_head+nnz), &matrix.data().value(tail), nnz_tail*sizeof(Scalar));
+          std::memcpy(&matrix.data().index(nnz_head+nnz), &matrix.data().index(tail), nnz_tail*sizeof(Index));
+        }
+        else
+        {
+          for(Index i=nnz_tail-1; i>=0; --i)
+          {
+            matrix.data().value(nnz_head+nnz+i) = matrix.data().value(tail+i);
+            matrix.data().index(nnz_head+nnz+i) = matrix.data().index(tail+i);
+          }
+        }
+
+        std::memcpy(&matrix.data().value(nnz_head), &tmp.data().value(0), nnz*sizeof(Scalar));
+        std::memcpy(&matrix.data().index(nnz_head), &tmp.data().index(0), nnz*sizeof(Index));
+      }
+
+      // update outer index pointers
+      Index p = nnz_head;
+      for(Index k=0; k<m_outerSize.value(); ++k)
+      {
+        matrix.outerIndexPtr()[m_outerStart+k] = p;
+        p += tmp.innerVector(k).nonZeros();
+      }
+      std::ptrdiff_t offset = nnz - nnz_previous;
+      for(Index k = m_outerStart + m_outerSize.value(); k<=matrix.outerSize(); ++k)
+      {
+        matrix.outerIndexPtr()[k] += offset;
+      }
+
+      return *this;
+    }
+
+    inline SparseInnerVectorSet& operator=(const SparseInnerVectorSet& other)
+    {
+      return operator=<SparseInnerVectorSet>(other);
+    }
+
+    inline const Scalar* valuePtr() const
+    { return m_matrix.valuePtr() + m_matrix.outerIndexPtr()[m_outerStart]; }
+    inline Scalar* valuePtr()
+    { return m_matrix.const_cast_derived().valuePtr() + m_matrix.outerIndexPtr()[m_outerStart]; }
+
+    inline const Index* innerIndexPtr() const
+    { return m_matrix.innerIndexPtr() + m_matrix.outerIndexPtr()[m_outerStart]; }
+    inline Index* innerIndexPtr()
+    { return m_matrix.const_cast_derived().innerIndexPtr() + m_matrix.outerIndexPtr()[m_outerStart]; }
+
+    inline const Index* outerIndexPtr() const
+    { return m_matrix.outerIndexPtr() + m_outerStart; }
+    inline Index* outerIndexPtr()
+    { return m_matrix.const_cast_derived().outerIndexPtr() + m_outerStart; }
+
+    Index nonZeros() const
+    {
+      if(m_matrix.isCompressed())
+        return  std::size_t(m_matrix.outerIndexPtr()[m_outerStart+m_outerSize.value()])
+              - std::size_t(m_matrix.outerIndexPtr()[m_outerStart]);
+      else if(m_outerSize.value()==0)
+        return 0;
+      else
+        return Map<const Matrix<Index,Size,1> >(m_matrix.innerNonZeroPtr()+m_outerStart, m_outerSize.value()).sum();
+    }
+
+    const Scalar& lastCoeff() const
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(SparseInnerVectorSet);
+      eigen_assert(nonZeros()>0);
+      if(m_matrix.isCompressed())
+        return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart+1]-1];
+      else
+        return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart]+m_matrix.innerNonZeroPtr()[m_outerStart]-1];
+    }
+
+//     template<typename Sparse>
+//     inline SparseInnerVectorSet& operator=(const SparseMatrixBase<OtherDerived>& other)
+//     {
+//       return *this;
+//     }
+
+    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+  protected:
+
+    typename MatrixType::Nested m_matrix;
+    Index m_outerStart;
+    const internal::variable_if_dynamic<Index, Size> m_outerSize;
+
+};
+
+//----------
+
+/** \returns the i-th row of the matrix \c *this. For row-major matrix only. */
+template<typename Derived>
+SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::row(Index i)
+{
+  EIGEN_STATIC_ASSERT(IsRowMajor,THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES);
+  return innerVector(i);
+}
+
+/** \returns the i-th row of the matrix \c *this. For row-major matrix only.
+  * (read-only version) */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::row(Index i) const
+{
+  EIGEN_STATIC_ASSERT(IsRowMajor,THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES);
+  return innerVector(i);
+}
+
+/** \returns the i-th column of the matrix \c *this. For column-major matrix only. */
+template<typename Derived>
+SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::col(Index i)
+{
+  EIGEN_STATIC_ASSERT(!IsRowMajor,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  return innerVector(i);
+}
+
+/** \returns the i-th column of the matrix \c *this. For column-major matrix only.
+  * (read-only version) */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::col(Index i) const
+{
+  EIGEN_STATIC_ASSERT(!IsRowMajor,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  return innerVector(i);
+}
+
+/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+  * is col-major (resp. row-major).
+  */
+template<typename Derived>
+SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::innerVector(Index outer)
+{ return SparseInnerVectorSet<Derived,1>(derived(), outer); }
+
+/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+  * is col-major (resp. row-major). Read-only.
+  */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,1> SparseMatrixBase<Derived>::innerVector(Index outer) const
+{ return SparseInnerVectorSet<Derived,1>(derived(), outer); }
+
+/** \returns the i-th row of the matrix \c *this. For row-major matrix only. */
+template<typename Derived>
+SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::middleRows(Index start, Index size)
+{
+  EIGEN_STATIC_ASSERT(IsRowMajor,THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES);
+  return innerVectors(start, size);
+}
+
+/** \returns the i-th row of the matrix \c *this. For row-major matrix only.
+  * (read-only version) */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::middleRows(Index start, Index size) const
+{
+  EIGEN_STATIC_ASSERT(IsRowMajor,THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES);
+  return innerVectors(start, size);
+}
+
+/** \returns the i-th column of the matrix \c *this. For column-major matrix only. */
+template<typename Derived>
+SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::middleCols(Index start, Index size)
+{
+  EIGEN_STATIC_ASSERT(!IsRowMajor,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  return innerVectors(start, size);
+}
+
+/** \returns the i-th column of the matrix \c *this. For column-major matrix only.
+  * (read-only version) */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::middleCols(Index start, Index size) const
+{
+  EIGEN_STATIC_ASSERT(!IsRowMajor,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  return innerVectors(start, size);
+}
+
+
+
+/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+  * is col-major (resp. row-major).
+  */
+template<typename Derived>
+SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::innerVectors(Index outerStart, Index outerSize)
+{ return SparseInnerVectorSet<Derived,Dynamic>(derived(), outerStart, outerSize); }
+
+/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+  * is col-major (resp. row-major). Read-only.
+  */
+template<typename Derived>
+const SparseInnerVectorSet<Derived,Dynamic> SparseMatrixBase<Derived>::innerVectors(Index outerStart, Index outerSize) const
+{ return SparseInnerVectorSet<Derived,Dynamic>(derived(), outerStart, outerSize); }
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_BLOCK_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h b/extern/Eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
new file mode 100644
index 00000000000..28167066a3b
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
@@ -0,0 +1,339 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_CWISE_BINARY_OP_H
+#define EIGEN_SPARSE_CWISE_BINARY_OP_H
+
+namespace Eigen { 
+
+// Here we have to handle 3 cases:
+//  1 - sparse op dense
+//  2 - dense op sparse
+//  3 - sparse op sparse
+// We also need to implement a 4th iterator for:
+//  4 - dense op dense
+// Finally, we also need to distinguish between the product and other operations :
+//                configuration      returned mode
+//  1 - sparse op dense    product      sparse
+//                         generic      dense
+//  2 - dense op sparse    product      sparse
+//                         generic      dense
+//  3 - sparse op sparse   product      sparse
+//                         generic      sparse
+//  4 - dense op dense     product      dense
+//                         generic      dense
+
+namespace internal {
+
+template<> struct promote_storage_type<Dense,Sparse>
+{ typedef Sparse ret; };
+
+template<> struct promote_storage_type<Sparse,Dense>
+{ typedef Sparse ret; };
+
+template<typename BinaryOp, typename Lhs, typename Rhs, typename Derived,
+  typename _LhsStorageMode = typename traits<Lhs>::StorageKind,
+  typename _RhsStorageMode = typename traits<Rhs>::StorageKind>
+class sparse_cwise_binary_op_inner_iterator_selector;
+
+} // end namespace internal
+
+template<typename BinaryOp, typename Lhs, typename Rhs>
+class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Sparse>
+  : public SparseMatrixBase<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
+{
+  public:
+    class InnerIterator;
+    class ReverseInnerIterator;
+    typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Derived)
+    CwiseBinaryOpImpl()
+    {
+      typedef typename internal::traits<Lhs>::StorageKind LhsStorageKind;
+      typedef typename internal::traits<Rhs>::StorageKind RhsStorageKind;
+      EIGEN_STATIC_ASSERT((
+                (!internal::is_same<LhsStorageKind,RhsStorageKind>::value)
+            ||  ((Lhs::Flags&RowMajorBit) == (Rhs::Flags&RowMajorBit))),
+            THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH);
+    }
+};
+
+template<typename BinaryOp, typename Lhs, typename Rhs>
+class CwiseBinaryOpImpl<BinaryOp,Lhs,Rhs,Sparse>::InnerIterator
+  : public internal::sparse_cwise_binary_op_inner_iterator_selector<BinaryOp,Lhs,Rhs,typename CwiseBinaryOpImpl<BinaryOp,Lhs,Rhs,Sparse>::InnerIterator>
+{
+  public:
+    typedef typename Lhs::Index Index;
+    typedef internal::sparse_cwise_binary_op_inner_iterator_selector<
+      BinaryOp,Lhs,Rhs, InnerIterator> Base;
+
+    EIGEN_STRONG_INLINE InnerIterator(const CwiseBinaryOpImpl& binOp, typename CwiseBinaryOpImpl::Index outer)
+      : Base(binOp.derived(),outer)
+    {}
+};
+
+/***************************************************************************
+* Implementation of inner-iterators
+***************************************************************************/
+
+// template<typename T> struct internal::func_is_conjunction { enum { ret = false }; };
+// template<typename T> struct internal::func_is_conjunction<internal::scalar_product_op<T> > { enum { ret = true }; };
+
+// TODO generalize the internal::scalar_product_op specialization to all conjunctions if any !
+
+namespace internal {
+
+// sparse - sparse  (generic)
+template<typename BinaryOp, typename Lhs, typename Rhs, typename Derived>
+class sparse_cwise_binary_op_inner_iterator_selector<BinaryOp, Lhs, Rhs, Derived, Sparse, Sparse>
+{
+    typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> CwiseBinaryXpr;
+    typedef typename traits<CwiseBinaryXpr>::Scalar Scalar;
+    typedef typename traits<CwiseBinaryXpr>::_LhsNested _LhsNested;
+    typedef typename traits<CwiseBinaryXpr>::_RhsNested _RhsNested;
+    typedef typename _LhsNested::InnerIterator LhsIterator;
+    typedef typename _RhsNested::InnerIterator RhsIterator;
+    typedef typename Lhs::Index Index;
+
+  public:
+
+    EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer)
+      : m_lhsIter(xpr.lhs(),outer), m_rhsIter(xpr.rhs(),outer), m_functor(xpr.functor())
+    {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE Derived& operator++()
+    {
+      if (m_lhsIter && m_rhsIter && (m_lhsIter.index() == m_rhsIter.index()))
+      {
+        m_id = m_lhsIter.index();
+        m_value = m_functor(m_lhsIter.value(), m_rhsIter.value());
+        ++m_lhsIter;
+        ++m_rhsIter;
+      }
+      else if (m_lhsIter && (!m_rhsIter || (m_lhsIter.index() < m_rhsIter.index())))
+      {
+        m_id = m_lhsIter.index();
+        m_value = m_functor(m_lhsIter.value(), Scalar(0));
+        ++m_lhsIter;
+      }
+      else if (m_rhsIter && (!m_lhsIter || (m_lhsIter.index() > m_rhsIter.index())))
+      {
+        m_id = m_rhsIter.index();
+        m_value = m_functor(Scalar(0), m_rhsIter.value());
+        ++m_rhsIter;
+      }
+      else
+      {
+        m_value = 0; // this is to avoid a compilation warning
+        m_id = -1;
+      }
+      return *static_cast<Derived*>(this);
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_value; }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index row() const { return Lhs::IsRowMajor ? m_lhsIter.row() : index(); }
+    EIGEN_STRONG_INLINE Index col() const { return Lhs::IsRowMajor ? index() : m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id>=0; }
+
+  protected:
+    LhsIterator m_lhsIter;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    Index m_id;
+};
+
+// sparse - sparse  (product)
+template<typename T, typename Lhs, typename Rhs, typename Derived>
+class sparse_cwise_binary_op_inner_iterator_selector<scalar_product_op<T>, Lhs, Rhs, Derived, Sparse, Sparse>
+{
+    typedef scalar_product_op<T> BinaryFunc;
+    typedef CwiseBinaryOp<BinaryFunc, Lhs, Rhs> CwiseBinaryXpr;
+    typedef typename CwiseBinaryXpr::Scalar Scalar;
+    typedef typename traits<CwiseBinaryXpr>::_LhsNested _LhsNested;
+    typedef typename _LhsNested::InnerIterator LhsIterator;
+    typedef typename traits<CwiseBinaryXpr>::_RhsNested _RhsNested;
+    typedef typename _RhsNested::InnerIterator RhsIterator;
+    typedef typename Lhs::Index Index;
+  public:
+
+    EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer)
+      : m_lhsIter(xpr.lhs(),outer), m_rhsIter(xpr.rhs(),outer), m_functor(xpr.functor())
+    {
+      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index()))
+      {
+        if (m_lhsIter.index() < m_rhsIter.index())
+          ++m_lhsIter;
+        else
+          ++m_rhsIter;
+      }
+    }
+
+    EIGEN_STRONG_INLINE Derived& operator++()
+    {
+      ++m_lhsIter;
+      ++m_rhsIter;
+      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index()))
+      {
+        if (m_lhsIter.index() < m_rhsIter.index())
+          ++m_lhsIter;
+        else
+          ++m_rhsIter;
+      }
+      return *static_cast<Derived*>(this);
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(m_lhsIter.value(), m_rhsIter.value()); }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_lhsIter.index(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return (m_lhsIter && m_rhsIter); }
+
+  protected:
+    LhsIterator m_lhsIter;
+    RhsIterator m_rhsIter;
+    const BinaryFunc& m_functor;
+};
+
+// sparse - dense  (product)
+template<typename T, typename Lhs, typename Rhs, typename Derived>
+class sparse_cwise_binary_op_inner_iterator_selector<scalar_product_op<T>, Lhs, Rhs, Derived, Sparse, Dense>
+{
+    typedef scalar_product_op<T> BinaryFunc;
+    typedef CwiseBinaryOp<BinaryFunc, Lhs, Rhs> CwiseBinaryXpr;
+    typedef typename CwiseBinaryXpr::Scalar Scalar;
+    typedef typename traits<CwiseBinaryXpr>::_LhsNested _LhsNested;
+    typedef typename traits<CwiseBinaryXpr>::RhsNested RhsNested;
+    typedef typename _LhsNested::InnerIterator LhsIterator;
+    typedef typename Lhs::Index Index;
+    enum { IsRowMajor = (int(Lhs::Flags)&RowMajorBit)==RowMajorBit };
+  public:
+
+    EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer)
+      : m_rhs(xpr.rhs()), m_lhsIter(xpr.lhs(),outer), m_functor(xpr.functor()), m_outer(outer)
+    {}
+
+    EIGEN_STRONG_INLINE Derived& operator++()
+    {
+      ++m_lhsIter;
+      return *static_cast<Derived*>(this);
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const
+    { return m_functor(m_lhsIter.value(),
+                       m_rhs.coeff(IsRowMajor?m_outer:m_lhsIter.index(),IsRowMajor?m_lhsIter.index():m_outer)); }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_lhsIter.index(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_lhsIter; }
+
+  protected:
+    RhsNested m_rhs;
+    LhsIterator m_lhsIter;
+    const BinaryFunc m_functor;
+    const Index m_outer;
+};
+
+// sparse - dense  (product)
+template<typename T, typename Lhs, typename Rhs, typename Derived>
+class sparse_cwise_binary_op_inner_iterator_selector<scalar_product_op<T>, Lhs, Rhs, Derived, Dense, Sparse>
+{
+    typedef scalar_product_op<T> BinaryFunc;
+    typedef CwiseBinaryOp<BinaryFunc, Lhs, Rhs> CwiseBinaryXpr;
+    typedef typename CwiseBinaryXpr::Scalar Scalar;
+    typedef typename traits<CwiseBinaryXpr>::_RhsNested _RhsNested;
+    typedef typename _RhsNested::InnerIterator RhsIterator;
+    typedef typename Lhs::Index Index;
+
+    enum { IsRowMajor = (int(Rhs::Flags)&RowMajorBit)==RowMajorBit };
+  public:
+
+    EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer)
+      : m_xpr(xpr), m_rhsIter(xpr.rhs(),outer), m_functor(xpr.functor()), m_outer(outer)
+    {}
+
+    EIGEN_STRONG_INLINE Derived& operator++()
+    {
+      ++m_rhsIter;
+      return *static_cast<Derived*>(this);
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const
+    { return m_functor(m_xpr.lhs().coeff(IsRowMajor?m_outer:m_rhsIter.index(),IsRowMajor?m_rhsIter.index():m_outer), m_rhsIter.value()); }
+
+    EIGEN_STRONG_INLINE Index index() const { return m_rhsIter.index(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_rhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_rhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_rhsIter; }
+
+  protected:
+    const CwiseBinaryXpr& m_xpr;
+    RhsIterator m_rhsIter;
+    const BinaryFunc& m_functor;
+    const Index m_outer;
+};
+
+} // end namespace internal
+
+/***************************************************************************
+* Implementation of SparseMatrixBase and SparseCwise functions/operators
+***************************************************************************/
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+SparseMatrixBase<Derived>::operator-=(const SparseMatrixBase<OtherDerived> &other)
+{
+  return *this = derived() - other.derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+SparseMatrixBase<Derived>::operator+=(const SparseMatrixBase<OtherDerived>& other)
+{
+  return *this = derived() + other.derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE const EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE
+SparseMatrixBase<Derived>::cwiseProduct(const MatrixBase<OtherDerived> &other) const
+{
+  return EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE(derived(), other.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_CWISE_BINARY_OP_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h b/extern/Eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
new file mode 100644
index 00000000000..2a63cf2af31
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
@@ -0,0 +1,178 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_CWISE_UNARY_OP_H
+#define EIGEN_SPARSE_CWISE_UNARY_OP_H
+
+namespace Eigen { 
+
+template<typename UnaryOp, typename MatrixType>
+class CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>
+  : public SparseMatrixBase<CwiseUnaryOp<UnaryOp, MatrixType> >
+{
+  public:
+
+    class InnerIterator;
+    class ReverseInnerIterator;
+
+    typedef CwiseUnaryOp<UnaryOp, MatrixType> Derived;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Derived)
+
+  protected:
+    typedef typename internal::traits<Derived>::_XprTypeNested _MatrixTypeNested;
+    typedef typename _MatrixTypeNested::InnerIterator MatrixTypeIterator;
+    typedef typename _MatrixTypeNested::ReverseInnerIterator MatrixTypeReverseIterator;
+};
+
+template<typename UnaryOp, typename MatrixType>
+class CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::InnerIterator
+    : public CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeIterator
+{
+    typedef typename CwiseUnaryOpImpl::Scalar Scalar;
+    typedef typename CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const CwiseUnaryOpImpl& unaryOp, typename CwiseUnaryOpImpl::Index outer)
+      : Base(unaryOp.derived().nestedExpression(),outer), m_functor(unaryOp.derived().functor())
+    {}
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    { Base::operator++(); return *this; }
+
+    EIGEN_STRONG_INLINE typename CwiseUnaryOpImpl::Scalar value() const { return m_functor(Base::value()); }
+
+  protected:
+    const UnaryOp m_functor;
+  private:
+    typename CwiseUnaryOpImpl::Scalar& valueRef();
+};
+
+template<typename UnaryOp, typename MatrixType>
+class CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::ReverseInnerIterator
+    : public CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeReverseIterator
+{
+    typedef typename CwiseUnaryOpImpl::Scalar Scalar;
+    typedef typename CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeReverseIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE ReverseInnerIterator(const CwiseUnaryOpImpl& unaryOp, typename CwiseUnaryOpImpl::Index outer)
+      : Base(unaryOp.derived().nestedExpression(),outer), m_functor(unaryOp.derived().functor())
+    {}
+
+    EIGEN_STRONG_INLINE ReverseInnerIterator& operator--()
+    { Base::operator--(); return *this; }
+
+    EIGEN_STRONG_INLINE typename CwiseUnaryOpImpl::Scalar value() const { return m_functor(Base::value()); }
+
+  protected:
+    const UnaryOp m_functor;
+  private:
+    typename CwiseUnaryOpImpl::Scalar& valueRef();
+};
+
+template<typename ViewOp, typename MatrixType>
+class CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>
+  : public SparseMatrixBase<CwiseUnaryView<ViewOp, MatrixType> >
+{
+  public:
+
+    class InnerIterator;
+    class ReverseInnerIterator;
+
+    typedef CwiseUnaryView<ViewOp, MatrixType> Derived;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Derived)
+
+  protected:
+    typedef typename internal::traits<Derived>::_MatrixTypeNested _MatrixTypeNested;
+    typedef typename _MatrixTypeNested::InnerIterator MatrixTypeIterator;
+    typedef typename _MatrixTypeNested::ReverseInnerIterator MatrixTypeReverseIterator;
+};
+
+template<typename ViewOp, typename MatrixType>
+class CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::InnerIterator
+    : public CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::MatrixTypeIterator
+{
+    typedef typename CwiseUnaryViewImpl::Scalar Scalar;
+    typedef typename CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::MatrixTypeIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const CwiseUnaryViewImpl& unaryOp, typename CwiseUnaryViewImpl::Index outer)
+      : Base(unaryOp.derived().nestedExpression(),outer), m_functor(unaryOp.derived().functor())
+    {}
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    { Base::operator++(); return *this; }
+
+    EIGEN_STRONG_INLINE typename CwiseUnaryViewImpl::Scalar value() const { return m_functor(Base::value()); }
+    EIGEN_STRONG_INLINE typename CwiseUnaryViewImpl::Scalar& valueRef() { return m_functor(Base::valueRef()); }
+
+  protected:
+    const ViewOp m_functor;
+};
+
+template<typename ViewOp, typename MatrixType>
+class CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::ReverseInnerIterator
+    : public CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::MatrixTypeReverseIterator
+{
+    typedef typename CwiseUnaryViewImpl::Scalar Scalar;
+    typedef typename CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::MatrixTypeReverseIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE ReverseInnerIterator(const CwiseUnaryViewImpl& unaryOp, typename CwiseUnaryViewImpl::Index outer)
+      : Base(unaryOp.derived().nestedExpression(),outer), m_functor(unaryOp.derived().functor())
+    {}
+
+    EIGEN_STRONG_INLINE ReverseInnerIterator& operator--()
+    { Base::operator--(); return *this; }
+
+    EIGEN_STRONG_INLINE typename CwiseUnaryViewImpl::Scalar value() const { return m_functor(Base::value()); }
+    EIGEN_STRONG_INLINE typename CwiseUnaryViewImpl::Scalar& valueRef() { return m_functor(Base::valueRef()); }
+
+  protected:
+    const ViewOp m_functor;
+};
+
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived&
+SparseMatrixBase<Derived>::operator*=(const Scalar& other)
+{
+  for (Index j=0; j<outerSize(); ++j)
+    for (typename Derived::InnerIterator i(derived(),j); i; ++i)
+      i.valueRef() *= other;
+  return derived();
+}
+
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived&
+SparseMatrixBase<Derived>::operator/=(const Scalar& other)
+{
+  for (Index j=0; j<outerSize(); ++j)
+    for (typename Derived::InnerIterator i(derived(),j); i; ++i)
+      i.valueRef() /= other;
+  return derived();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_CWISE_UNARY_OP_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseDenseProduct.h b/extern/Eigen3/Eigen/src/SparseCore/SparseDenseProduct.h
new file mode 100644
index 00000000000..00ba606be5e
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseDenseProduct.h
@@ -0,0 +1,315 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEDENSEPRODUCT_H
+#define EIGEN_SPARSEDENSEPRODUCT_H
+
+namespace Eigen { 
+
+template<typename Lhs, typename Rhs, int InnerSize> struct SparseDenseProductReturnType
+{
+  typedef SparseTimeDenseProduct<Lhs,Rhs> Type;
+};
+
+template<typename Lhs, typename Rhs> struct SparseDenseProductReturnType<Lhs,Rhs,1>
+{
+  typedef SparseDenseOuterProduct<Lhs,Rhs,false> Type;
+};
+
+template<typename Lhs, typename Rhs, int InnerSize> struct DenseSparseProductReturnType
+{
+  typedef DenseTimeSparseProduct<Lhs,Rhs> Type;
+};
+
+template<typename Lhs, typename Rhs> struct DenseSparseProductReturnType<Lhs,Rhs,1>
+{
+  typedef SparseDenseOuterProduct<Rhs,Lhs,true> Type;
+};
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, bool Tr>
+struct traits<SparseDenseOuterProduct<Lhs,Rhs,Tr> >
+{
+  typedef Sparse StorageKind;
+  typedef typename scalar_product_traits<typename traits<Lhs>::Scalar,
+                                            typename traits<Rhs>::Scalar>::ReturnType Scalar;
+  typedef typename Lhs::Index Index;
+  typedef typename Lhs::Nested LhsNested;
+  typedef typename Rhs::Nested RhsNested;
+  typedef typename remove_all<LhsNested>::type _LhsNested;
+  typedef typename remove_all<RhsNested>::type _RhsNested;
+
+  enum {
+    LhsCoeffReadCost = traits<_LhsNested>::CoeffReadCost,
+    RhsCoeffReadCost = traits<_RhsNested>::CoeffReadCost,
+
+    RowsAtCompileTime    = Tr ? int(traits<Rhs>::RowsAtCompileTime)     : int(traits<Lhs>::RowsAtCompileTime),
+    ColsAtCompileTime    = Tr ? int(traits<Lhs>::ColsAtCompileTime)     : int(traits<Rhs>::ColsAtCompileTime),
+    MaxRowsAtCompileTime = Tr ? int(traits<Rhs>::MaxRowsAtCompileTime)  : int(traits<Lhs>::MaxRowsAtCompileTime),
+    MaxColsAtCompileTime = Tr ? int(traits<Lhs>::MaxColsAtCompileTime)  : int(traits<Rhs>::MaxColsAtCompileTime),
+
+    Flags = Tr ? RowMajorBit : 0,
+
+    CoeffReadCost = LhsCoeffReadCost + RhsCoeffReadCost + NumTraits<Scalar>::MulCost
+  };
+};
+
+} // end namespace internal
+
+template<typename Lhs, typename Rhs, bool Tr>
+class SparseDenseOuterProduct
+ : public SparseMatrixBase<SparseDenseOuterProduct<Lhs,Rhs,Tr> >
+{
+  public:
+
+    typedef SparseMatrixBase<SparseDenseOuterProduct> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(SparseDenseOuterProduct)
+    typedef internal::traits<SparseDenseOuterProduct> Traits;
+
+  private:
+
+    typedef typename Traits::LhsNested LhsNested;
+    typedef typename Traits::RhsNested RhsNested;
+    typedef typename Traits::_LhsNested _LhsNested;
+    typedef typename Traits::_RhsNested _RhsNested;
+
+  public:
+
+    class InnerIterator;
+
+    EIGEN_STRONG_INLINE SparseDenseOuterProduct(const Lhs& lhs, const Rhs& rhs)
+      : m_lhs(lhs), m_rhs(rhs)
+    {
+      EIGEN_STATIC_ASSERT(!Tr,YOU_MADE_A_PROGRAMMING_MISTAKE);
+    }
+
+    EIGEN_STRONG_INLINE SparseDenseOuterProduct(const Rhs& rhs, const Lhs& lhs)
+      : m_lhs(lhs), m_rhs(rhs)
+    {
+      EIGEN_STATIC_ASSERT(Tr,YOU_MADE_A_PROGRAMMING_MISTAKE);
+    }
+
+    EIGEN_STRONG_INLINE Index rows() const { return Tr ? m_rhs.rows() : m_lhs.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return Tr ? m_lhs.cols() : m_rhs.cols(); }
+
+    EIGEN_STRONG_INLINE const _LhsNested& lhs() const { return m_lhs; }
+    EIGEN_STRONG_INLINE const _RhsNested& rhs() const { return m_rhs; }
+
+  protected:
+    LhsNested m_lhs;
+    RhsNested m_rhs;
+};
+
+template<typename Lhs, typename Rhs, bool Transpose>
+class SparseDenseOuterProduct<Lhs,Rhs,Transpose>::InnerIterator : public _LhsNested::InnerIterator
+{
+    typedef typename _LhsNested::InnerIterator Base;
+  public:
+    EIGEN_STRONG_INLINE InnerIterator(const SparseDenseOuterProduct& prod, Index outer)
+      : Base(prod.lhs(), 0), m_outer(outer), m_factor(prod.rhs().coeff(outer))
+    {
+    }
+
+    inline Index outer() const { return m_outer; }
+    inline Index row() const { return Transpose ? Base::row() : m_outer; }
+    inline Index col() const { return Transpose ? m_outer : Base::row(); }
+
+    inline Scalar value() const { return Base::value() * m_factor; }
+
+  protected:
+    int m_outer;
+    Scalar m_factor;
+};
+
+namespace internal {
+template<typename Lhs, typename Rhs>
+struct traits<SparseTimeDenseProduct<Lhs,Rhs> >
+ : traits<ProductBase<SparseTimeDenseProduct<Lhs,Rhs>, Lhs, Rhs> >
+{
+  typedef Dense StorageKind;
+  typedef MatrixXpr XprKind;
+};
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType,
+         int LhsStorageOrder = ((SparseLhsType::Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor,
+         bool ColPerCol = ((DenseRhsType::Flags&RowMajorBit)==0) || DenseRhsType::ColsAtCompileTime==1>
+struct sparse_time_dense_product_impl;
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
+struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, RowMajor, true>
+{
+  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
+  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
+  typedef typename internal::remove_all<DenseResType>::type Res;
+  typedef typename Lhs::Index Index;
+  typedef typename Lhs::InnerIterator LhsInnerIterator;
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, typename Res::Scalar alpha)
+  {
+    for(Index c=0; c<rhs.cols(); ++c)
+    {
+      int n = lhs.outerSize();
+      for(Index j=0; j<n; ++j)
+      {
+        typename Res::Scalar tmp(0);
+        for(LhsInnerIterator it(lhs,j); it ;++it)
+          tmp += it.value() * rhs.coeff(it.index(),c);
+        res.coeffRef(j,c) = alpha * tmp;
+      }
+    }
+  }
+};
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
+struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, ColMajor, true>
+{
+  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
+  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
+  typedef typename internal::remove_all<DenseResType>::type Res;
+  typedef typename Lhs::InnerIterator LhsInnerIterator;
+  typedef typename Lhs::Index Index;
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, typename Res::Scalar alpha)
+  {
+    for(Index c=0; c<rhs.cols(); ++c)
+    {
+      for(Index j=0; j<lhs.outerSize(); ++j)
+      {
+        typename Res::Scalar rhs_j = alpha * rhs.coeff(j,c);
+        for(LhsInnerIterator it(lhs,j); it ;++it)
+          res.coeffRef(it.index(),c) += it.value() * rhs_j;
+      }
+    }
+  }
+};
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
+struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, RowMajor, false>
+{
+  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
+  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
+  typedef typename internal::remove_all<DenseResType>::type Res;
+  typedef typename Lhs::InnerIterator LhsInnerIterator;
+  typedef typename Lhs::Index Index;
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, typename Res::Scalar alpha)
+  {
+    for(Index j=0; j<lhs.outerSize(); ++j)
+    {
+      typename Res::RowXpr res_j(res.row(j));
+      for(LhsInnerIterator it(lhs,j); it ;++it)
+        res_j += (alpha*it.value()) * rhs.row(it.index());
+    }
+  }
+};
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
+struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, ColMajor, false>
+{
+  typedef typename internal::remove_all<SparseLhsType>::type Lhs;
+  typedef typename internal::remove_all<DenseRhsType>::type Rhs;
+  typedef typename internal::remove_all<DenseResType>::type Res;
+  typedef typename Lhs::InnerIterator LhsInnerIterator;
+  typedef typename Lhs::Index Index;
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, typename Res::Scalar alpha)
+  {
+    for(Index j=0; j<lhs.outerSize(); ++j)
+    {
+      typename Rhs::ConstRowXpr rhs_j(rhs.row(j));
+      for(LhsInnerIterator it(lhs,j); it ;++it)
+        res.row(it.index()) += (alpha*it.value()) * rhs_j;
+    }
+  }
+};
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType,typename AlphaType>
+inline void sparse_time_dense_product(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha)
+{
+  sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType>::run(lhs, rhs, res, alpha);
+}
+
+} // end namespace internal
+
+template<typename Lhs, typename Rhs>
+class SparseTimeDenseProduct
+  : public ProductBase<SparseTimeDenseProduct<Lhs,Rhs>, Lhs, Rhs>
+{
+  public:
+    EIGEN_PRODUCT_PUBLIC_INTERFACE(SparseTimeDenseProduct)
+
+    SparseTimeDenseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
+    {}
+
+    template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
+    {
+      internal::sparse_time_dense_product(m_lhs, m_rhs, dest, alpha);
+    }
+
+  private:
+    SparseTimeDenseProduct& operator=(const SparseTimeDenseProduct&);
+};
+
+
+// dense = dense * sparse
+namespace internal {
+template<typename Lhs, typename Rhs>
+struct traits<DenseTimeSparseProduct<Lhs,Rhs> >
+ : traits<ProductBase<DenseTimeSparseProduct<Lhs,Rhs>, Lhs, Rhs> >
+{
+  typedef Dense StorageKind;
+};
+} // end namespace internal
+
+template<typename Lhs, typename Rhs>
+class DenseTimeSparseProduct
+  : public ProductBase<DenseTimeSparseProduct<Lhs,Rhs>, Lhs, Rhs>
+{
+  public:
+    EIGEN_PRODUCT_PUBLIC_INTERFACE(DenseTimeSparseProduct)
+
+    DenseTimeSparseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
+    {}
+
+    template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
+    {
+      Transpose<const _LhsNested> lhs_t(m_lhs);
+      Transpose<const _RhsNested> rhs_t(m_rhs);
+      Transpose<Dest> dest_t(dest);
+      internal::sparse_time_dense_product(rhs_t, lhs_t, dest_t, alpha);
+    }
+
+  private:
+    DenseTimeSparseProduct& operator=(const DenseTimeSparseProduct&);
+};
+
+// sparse * dense
+template<typename Derived>
+template<typename OtherDerived>
+inline const typename SparseDenseProductReturnType<Derived,OtherDerived>::Type
+SparseMatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
+{
+  return typename SparseDenseProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEDENSEPRODUCT_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h b/extern/Eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h
new file mode 100644
index 00000000000..b09c4a7151b
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h
@@ -0,0 +1,199 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_DIAGONAL_PRODUCT_H
+#define EIGEN_SPARSE_DIAGONAL_PRODUCT_H
+
+namespace Eigen { 
+
+// The product of a diagonal matrix with a sparse matrix can be easily
+// implemented using expression template.
+// We have two consider very different cases:
+// 1 - diag * row-major sparse
+//     => each inner vector <=> scalar * sparse vector product
+//     => so we can reuse CwiseUnaryOp::InnerIterator
+// 2 - diag * col-major sparse
+//     => each inner vector <=> densevector * sparse vector cwise product
+//     => again, we can reuse specialization of CwiseBinaryOp::InnerIterator
+//        for that particular case
+// The two other cases are symmetric.
+
+namespace internal {
+
+template<typename Lhs, typename Rhs>
+struct traits<SparseDiagonalProduct<Lhs, Rhs> >
+{
+  typedef typename remove_all<Lhs>::type _Lhs;
+  typedef typename remove_all<Rhs>::type _Rhs;
+  typedef typename _Lhs::Scalar Scalar;
+  typedef typename promote_index_type<typename traits<Lhs>::Index,
+                                         typename traits<Rhs>::Index>::type Index;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = _Lhs::RowsAtCompileTime,
+    ColsAtCompileTime = _Rhs::ColsAtCompileTime,
+
+    MaxRowsAtCompileTime = _Lhs::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = _Rhs::MaxColsAtCompileTime,
+
+    SparseFlags = is_diagonal<_Lhs>::ret ? int(_Rhs::Flags) : int(_Lhs::Flags),
+    Flags = (SparseFlags&RowMajorBit),
+    CoeffReadCost = Dynamic
+  };
+};
+
+enum {SDP_IsDiagonal, SDP_IsSparseRowMajor, SDP_IsSparseColMajor};
+template<typename Lhs, typename Rhs, typename SparseDiagonalProductType, int RhsMode, int LhsMode>
+class sparse_diagonal_product_inner_iterator_selector;
+
+} // end namespace internal
+
+template<typename Lhs, typename Rhs>
+class SparseDiagonalProduct
+  : public SparseMatrixBase<SparseDiagonalProduct<Lhs,Rhs> >,
+    internal::no_assignment_operator
+{
+    typedef typename Lhs::Nested LhsNested;
+    typedef typename Rhs::Nested RhsNested;
+
+    typedef typename internal::remove_all<LhsNested>::type _LhsNested;
+    typedef typename internal::remove_all<RhsNested>::type _RhsNested;
+
+    enum {
+      LhsMode = internal::is_diagonal<_LhsNested>::ret ? internal::SDP_IsDiagonal
+              : (_LhsNested::Flags&RowMajorBit) ? internal::SDP_IsSparseRowMajor : internal::SDP_IsSparseColMajor,
+      RhsMode = internal::is_diagonal<_RhsNested>::ret ? internal::SDP_IsDiagonal
+              : (_RhsNested::Flags&RowMajorBit) ? internal::SDP_IsSparseRowMajor : internal::SDP_IsSparseColMajor
+    };
+
+  public:
+
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseDiagonalProduct)
+
+    typedef internal::sparse_diagonal_product_inner_iterator_selector
+                <_LhsNested,_RhsNested,SparseDiagonalProduct,LhsMode,RhsMode> InnerIterator;
+
+    EIGEN_STRONG_INLINE SparseDiagonalProduct(const Lhs& lhs, const Rhs& rhs)
+      : m_lhs(lhs), m_rhs(rhs)
+    {
+      eigen_assert(lhs.cols() == rhs.rows() && "invalid sparse matrix * diagonal matrix product");
+    }
+
+    EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); }
+
+    EIGEN_STRONG_INLINE const _LhsNested& lhs() const { return m_lhs; }
+    EIGEN_STRONG_INLINE const _RhsNested& rhs() const { return m_rhs; }
+
+  protected:
+    LhsNested m_lhs;
+    RhsNested m_rhs;
+};
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, typename SparseDiagonalProductType>
+class sparse_diagonal_product_inner_iterator_selector
+<Lhs,Rhs,SparseDiagonalProductType,SDP_IsDiagonal,SDP_IsSparseRowMajor>
+  : public CwiseUnaryOp<scalar_multiple_op<typename Lhs::Scalar>,const Rhs>::InnerIterator
+{
+    typedef typename CwiseUnaryOp<scalar_multiple_op<typename Lhs::Scalar>,const Rhs>::InnerIterator Base;
+    typedef typename Lhs::Index Index;
+  public:
+    inline sparse_diagonal_product_inner_iterator_selector(
+              const SparseDiagonalProductType& expr, Index outer)
+      : Base(expr.rhs()*(expr.lhs().diagonal().coeff(outer)), outer)
+    {}
+};
+
+template<typename Lhs, typename Rhs, typename SparseDiagonalProductType>
+class sparse_diagonal_product_inner_iterator_selector
+<Lhs,Rhs,SparseDiagonalProductType,SDP_IsDiagonal,SDP_IsSparseColMajor>
+  : public CwiseBinaryOp<
+      scalar_product_op<typename Lhs::Scalar>,
+      SparseInnerVectorSet<Rhs,1>,
+      typename Lhs::DiagonalVectorType>::InnerIterator
+{
+    typedef typename CwiseBinaryOp<
+      scalar_product_op<typename Lhs::Scalar>,
+      SparseInnerVectorSet<Rhs,1>,
+      typename Lhs::DiagonalVectorType>::InnerIterator Base;
+    typedef typename Lhs::Index Index;
+  public:
+    inline sparse_diagonal_product_inner_iterator_selector(
+              const SparseDiagonalProductType& expr, Index outer)
+      : Base(expr.rhs().innerVector(outer) .cwiseProduct(expr.lhs().diagonal()), 0)
+    {}
+};
+
+template<typename Lhs, typename Rhs, typename SparseDiagonalProductType>
+class sparse_diagonal_product_inner_iterator_selector
+<Lhs,Rhs,SparseDiagonalProductType,SDP_IsSparseColMajor,SDP_IsDiagonal>
+  : public CwiseUnaryOp<scalar_multiple_op<typename Rhs::Scalar>,const Lhs>::InnerIterator
+{
+    typedef typename CwiseUnaryOp<scalar_multiple_op<typename Rhs::Scalar>,const Lhs>::InnerIterator Base;
+    typedef typename Lhs::Index Index;
+  public:
+    inline sparse_diagonal_product_inner_iterator_selector(
+              const SparseDiagonalProductType& expr, Index outer)
+      : Base(expr.lhs()*expr.rhs().diagonal().coeff(outer), outer)
+    {}
+};
+
+template<typename Lhs, typename Rhs, typename SparseDiagonalProductType>
+class sparse_diagonal_product_inner_iterator_selector
+<Lhs,Rhs,SparseDiagonalProductType,SDP_IsSparseRowMajor,SDP_IsDiagonal>
+  : public CwiseBinaryOp<
+      scalar_product_op<typename Rhs::Scalar>,
+      SparseInnerVectorSet<Lhs,1>,
+      Transpose<const typename Rhs::DiagonalVectorType> >::InnerIterator
+{
+    typedef typename CwiseBinaryOp<
+      scalar_product_op<typename Rhs::Scalar>,
+      SparseInnerVectorSet<Lhs,1>,
+      Transpose<const typename Rhs::DiagonalVectorType> >::InnerIterator Base;
+    typedef typename Lhs::Index Index;
+  public:
+    inline sparse_diagonal_product_inner_iterator_selector(
+              const SparseDiagonalProductType& expr, Index outer)
+      : Base(expr.lhs().innerVector(outer) .cwiseProduct(expr.rhs().diagonal().transpose()), 0)
+    {}
+};
+
+} // end namespace internal
+
+// SparseMatrixBase functions
+
+template<typename Derived>
+template<typename OtherDerived>
+const SparseDiagonalProduct<Derived,OtherDerived>
+SparseMatrixBase<Derived>::operator*(const DiagonalBase<OtherDerived> &other) const
+{
+  return SparseDiagonalProduct<Derived,OtherDerived>(this->derived(), other.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_DIAGONAL_PRODUCT_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseDot.h b/extern/Eigen3/Eigen/src/SparseCore/SparseDot.h
new file mode 100644
index 00000000000..ebb63d36b95
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseDot.h
@@ -0,0 +1,109 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_DOT_H
+#define EIGEN_SPARSE_DOT_H
+
+namespace Eigen { 
+
+template<typename Derived>
+template<typename OtherDerived>
+typename internal::traits<Derived>::Scalar
+SparseMatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(size() == other.size());
+  eigen_assert(other.size()>0 && "you are using a non initialized vector");
+
+  typename Derived::InnerIterator i(derived(),0);
+  Scalar res(0);
+  while (i)
+  {
+    res += internal::conj(i.value()) * other.coeff(i.index());
+    ++i;
+  }
+  return res;
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+typename internal::traits<Derived>::Scalar
+SparseMatrixBase<Derived>::dot(const SparseMatrixBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(size() == other.size());
+
+  typedef typename Derived::Nested  Nested;
+  typedef typename OtherDerived::Nested  OtherNested;
+  typedef typename internal::remove_all<Nested>::type  NestedCleaned;
+  typedef typename internal::remove_all<OtherNested>::type  OtherNestedCleaned;
+
+  const Nested nthis(derived());
+  const OtherNested nother(other.derived());
+
+  typename NestedCleaned::InnerIterator i(nthis,0);
+  typename OtherNestedCleaned::InnerIterator j(nother,0);
+  Scalar res(0);
+  while (i && j)
+  {
+    if (i.index()==j.index())
+    {
+      res += internal::conj(i.value()) * j.value();
+      ++i; ++j;
+    }
+    else if (i.index()<j.index())
+      ++i;
+    else
+      ++j;
+  }
+  return res;
+}
+
+template<typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+SparseMatrixBase<Derived>::squaredNorm() const
+{
+  return internal::real((*this).cwiseAbs2().sum());
+}
+
+template<typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+SparseMatrixBase<Derived>::norm() const
+{
+  return internal::sqrt(squaredNorm());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_DOT_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseFuzzy.h b/extern/Eigen3/Eigen/src/SparseCore/SparseFuzzy.h
new file mode 100644
index 00000000000..f00b3d6469b
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseFuzzy.h
@@ -0,0 +1,41 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_FUZZY_H
+#define EIGEN_SPARSE_FUZZY_H
+
+// template<typename Derived>
+// template<typename OtherDerived>
+// bool SparseMatrixBase<Derived>::isApprox(
+//   const OtherDerived& other,
+//   typename NumTraits<Scalar>::Real prec
+// ) const
+// {
+//   const typename internal::nested<Derived,2>::type nested(derived());
+//   const typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
+//   return    (nested - otherNested).cwise().abs2().sum()
+//          <= prec * prec * (std::min)(nested.cwise().abs2().sum(), otherNested.cwise().abs2().sum());
+// }
+
+#endif // EIGEN_SPARSE_FUZZY_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseMatrix.h b/extern/Eigen3/Eigen/src/SparseCore/SparseMatrix.h
new file mode 100644
index 00000000000..214f130f5ed
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseMatrix.h
@@ -0,0 +1,1127 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEMATRIX_H
+#define EIGEN_SPARSEMATRIX_H
+
+namespace Eigen { 
+
+/** \ingroup SparseCore_Module
+  *
+  * \class SparseMatrix
+  *
+  * \brief A versatible sparse matrix representation
+  *
+  * This class implements a more versatile variants of the common \em compressed row/column storage format.
+  * Each colmun's (resp. row) non zeros are stored as a pair of value with associated row (resp. colmiun) index.
+  * All the non zeros are stored in a single large buffer. Unlike the \em compressed format, there might be extra
+  * space inbetween the nonzeros of two successive colmuns (resp. rows) such that insertion of new non-zero
+  * can be done with limited memory reallocation and copies.
+  *
+  * A call to the function makeCompressed() turns the matrix into the standard \em compressed format
+  * compatible with many library.
+  *
+  * More details on this storage sceheme are given in the \ref TutorialSparse "manual pages".
+  *
+  * \tparam _Scalar the scalar type, i.e. the type of the coefficients
+  * \tparam _Options Union of bit flags controlling the storage scheme. Currently the only possibility
+  *                 is RowMajor. The default is 0 which means column-major.
+  * \tparam _Index the type of the indices. It has to be a \b signed type (e.g., short, int, std::ptrdiff_t). Default is \c int.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_SPARSEMATRIX_PLUGIN.
+  */
+
+namespace internal {
+template<typename _Scalar, int _Options, typename _Index>
+struct traits<SparseMatrix<_Scalar, _Options, _Index> >
+{
+  typedef _Scalar Scalar;
+  typedef _Index Index;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = Dynamic,
+    Flags = _Options | NestByRefBit | LvalueBit,
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    SupportedAccessPatterns = InnerRandomAccessPattern
+  };
+};
+
+template<typename _Scalar, int _Options, typename _Index, int DiagIndex>
+struct traits<Diagonal<const SparseMatrix<_Scalar, _Options, _Index>, DiagIndex> >
+{
+  typedef SparseMatrix<_Scalar, _Options, _Index> MatrixType;
+  typedef typename nested<MatrixType>::type MatrixTypeNested;
+  typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested;
+
+  typedef _Scalar Scalar;
+  typedef Dense StorageKind;
+  typedef _Index Index;
+  typedef MatrixXpr XprKind;
+
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = 1,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = 1,
+    Flags = 0,
+    CoeffReadCost = _MatrixTypeNested::CoeffReadCost*10
+  };
+};
+
+} // end namespace internal
+
+template<typename _Scalar, int _Options, typename _Index>
+class SparseMatrix
+  : public SparseMatrixBase<SparseMatrix<_Scalar, _Options, _Index> >
+{
+  public:
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseMatrix)
+    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseMatrix, +=)
+    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseMatrix, -=)
+
+    typedef MappedSparseMatrix<Scalar,Flags> Map;
+    using Base::IsRowMajor;
+    typedef internal::CompressedStorage<Scalar,Index> Storage;
+    enum {
+      Options = _Options
+    };
+
+  protected:
+
+    typedef SparseMatrix<Scalar,(Flags&~RowMajorBit)|(IsRowMajor?RowMajorBit:0)> TransposedSparseMatrix;
+
+    Index m_outerSize;
+    Index m_innerSize;
+    Index* m_outerIndex;
+    Index* m_innerNonZeros;     // optional, if null then the data is compressed
+    Storage m_data;
+    
+    Eigen::Map<Matrix<Index,Dynamic,1> > innerNonZeros() { return Eigen::Map<Matrix<Index,Dynamic,1> >(m_innerNonZeros, m_innerNonZeros?m_outerSize:0); }
+    const  Eigen::Map<const Matrix<Index,Dynamic,1> > innerNonZeros() const { return Eigen::Map<const Matrix<Index,Dynamic,1> >(m_innerNonZeros, m_innerNonZeros?m_outerSize:0); }
+
+  public:
+    
+    /** \returns whether \c *this is in compressed form. */
+    inline bool isCompressed() const { return m_innerNonZeros==0; }
+
+    /** \returns the number of rows of the matrix */
+    inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
+    /** \returns the number of columns of the matrix */
+    inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
+
+    /** \returns the number of rows (resp. columns) of the matrix if the storage order column major (resp. row major) */
+    inline Index innerSize() const { return m_innerSize; }
+    /** \returns the number of columns (resp. rows) of the matrix if the storage order column major (resp. row major) */
+    inline Index outerSize() const { return m_outerSize; }
+    
+    /** \returns a const pointer to the array of values.
+      * This function is aimed at interoperability with other libraries.
+      * \sa innerIndexPtr(), outerIndexPtr() */
+    inline const Scalar* valuePtr() const { return &m_data.value(0); }
+    /** \returns a non-const pointer to the array of values.
+      * This function is aimed at interoperability with other libraries.
+      * \sa innerIndexPtr(), outerIndexPtr() */
+    inline Scalar* valuePtr() { return &m_data.value(0); }
+
+    /** \returns a const pointer to the array of inner indices.
+      * This function is aimed at interoperability with other libraries.
+      * \sa valuePtr(), outerIndexPtr() */
+    inline const Index* innerIndexPtr() const { return &m_data.index(0); }
+    /** \returns a non-const pointer to the array of inner indices.
+      * This function is aimed at interoperability with other libraries.
+      * \sa valuePtr(), outerIndexPtr() */
+    inline Index* innerIndexPtr() { return &m_data.index(0); }
+
+    /** \returns a const pointer to the array of the starting positions of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \sa valuePtr(), innerIndexPtr() */
+    inline const Index* outerIndexPtr() const { return m_outerIndex; }
+    /** \returns a non-const pointer to the array of the starting positions of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \sa valuePtr(), innerIndexPtr() */
+    inline Index* outerIndexPtr() { return m_outerIndex; }
+
+    /** \returns a const pointer to the array of the number of non zeros of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \warning it returns the null pointer 0 in compressed mode */
+    inline const Index* innerNonZeroPtr() const { return m_innerNonZeros; }
+    /** \returns a non-const pointer to the array of the number of non zeros of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \warning it returns the null pointer 0 in compressed mode */
+    inline Index* innerNonZeroPtr() { return m_innerNonZeros; }
+
+    /** \internal */
+    inline Storage& data() { return m_data; }
+    /** \internal */
+    inline const Storage& data() const { return m_data; }
+
+    /** \returns the value of the matrix at position \a i, \a j
+      * This function returns Scalar(0) if the element is an explicit \em zero */
+    inline Scalar coeff(Index row, Index col) const
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+      Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer+1];
+      return m_data.atInRange(m_outerIndex[outer], end, inner);
+    }
+
+    /** \returns a non-const reference to the value of the matrix at position \a i, \a j
+      *
+      * If the element does not exist then it is inserted via the insert(Index,Index) function
+      * which itself turns the matrix into a non compressed form if that was not the case.
+      *
+      * This is a O(log(nnz_j)) operation (binary search) plus the cost of insert(Index,Index)
+      * function if the element does not already exist.
+      */
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index start = m_outerIndex[outer];
+      Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer+1];
+      eigen_assert(end>=start && "you probably called coeffRef on a non finalized matrix");
+      if(end<=start)
+        return insert(row,col);
+      const Index p = m_data.searchLowerIndex(start,end-1,inner);
+      if((p<end) && (m_data.index(p)==inner))
+        return m_data.value(p);
+      else
+        return insert(row,col);
+    }
+
+    /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
+      * The non zero coefficient must \b not already exist.
+      *
+      * If the matrix \c *this is in compressed mode, then \c *this is turned into uncompressed
+      * mode while reserving room for 2 non zeros per inner vector. It is strongly recommended to first
+      * call reserve(const SizesType &) to reserve a more appropriate number of elements per
+      * inner vector that better match your scenario.
+      *
+      * This function performs a sorted insertion in O(1) if the elements of each inner vector are
+      * inserted in increasing inner index order, and in O(nnz_j) for a random insertion.
+      *
+      */
+    EIGEN_DONT_INLINE Scalar& insert(Index row, Index col)
+    {
+      if(isCompressed())
+      {
+        reserve(VectorXi::Constant(outerSize(), 2));
+      }
+      return insertUncompressed(row,col);
+    }
+
+  public:
+
+    class InnerIterator;
+    class ReverseInnerIterator;
+
+    /** Removes all non zeros but keep allocated memory */
+    inline void setZero()
+    {
+      m_data.clear();
+      memset(m_outerIndex, 0, (m_outerSize+1)*sizeof(Index));
+      if(m_innerNonZeros)
+        memset(m_innerNonZeros, 0, (m_outerSize)*sizeof(Index));
+    }
+
+    /** \returns the number of non zero coefficients */
+    inline Index nonZeros() const
+    {
+      if(m_innerNonZeros)
+        return innerNonZeros().sum();
+      return static_cast<Index>(m_data.size());
+    }
+
+    /** Preallocates \a reserveSize non zeros.
+      *
+      * Precondition: the matrix must be in compressed mode. */
+    inline void reserve(Index reserveSize)
+    {
+      eigen_assert(isCompressed() && "This function does not make sense in non compressed mode.");
+      m_data.reserve(reserveSize);
+    }
+    
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** Preallocates \a reserveSize[\c j] non zeros for each column (resp. row) \c j.
+      *
+      * This function turns the matrix in non-compressed mode */
+    template<class SizesType>
+    inline void reserve(const SizesType& reserveSizes);
+    #else
+    template<class SizesType>
+    inline void reserve(const SizesType& reserveSizes, const typename SizesType::value_type& enableif = typename SizesType::value_type())
+    {
+      EIGEN_UNUSED_VARIABLE(enableif);
+      reserveInnerVectors(reserveSizes);
+    }
+    template<class SizesType>
+    inline void reserve(const SizesType& reserveSizes, const typename SizesType::Scalar& enableif = typename SizesType::Scalar())
+    {
+      EIGEN_UNUSED_VARIABLE(enableif);
+      reserveInnerVectors(reserveSizes);
+    }
+    #endif // EIGEN_PARSED_BY_DOXYGEN
+  protected:
+    template<class SizesType>
+    inline void reserveInnerVectors(const SizesType& reserveSizes)
+    {
+      
+      if(isCompressed())
+      {
+        std::size_t totalReserveSize = 0;
+        // turn the matrix into non-compressed mode
+        m_innerNonZeros = new Index[m_outerSize];
+        
+        // temporarily use m_innerSizes to hold the new starting points.
+        Index* newOuterIndex = m_innerNonZeros;
+        
+        Index count = 0;
+        for(Index j=0; j<m_outerSize; ++j)
+        {
+          newOuterIndex[j] = count;
+          count += reserveSizes[j] + (m_outerIndex[j+1]-m_outerIndex[j]);
+          totalReserveSize += reserveSizes[j];
+        }
+        m_data.reserve(totalReserveSize);
+        std::ptrdiff_t previousOuterIndex = m_outerIndex[m_outerSize];
+        for(std::ptrdiff_t j=m_outerSize-1; j>=0; --j)
+        {
+          ptrdiff_t innerNNZ = previousOuterIndex - m_outerIndex[j];
+          for(std::ptrdiff_t i=innerNNZ-1; i>=0; --i)
+          {
+            m_data.index(newOuterIndex[j]+i) = m_data.index(m_outerIndex[j]+i);
+            m_data.value(newOuterIndex[j]+i) = m_data.value(m_outerIndex[j]+i);
+          }
+          previousOuterIndex = m_outerIndex[j];
+          m_outerIndex[j] = newOuterIndex[j];
+          m_innerNonZeros[j] = innerNNZ;
+        }
+        m_outerIndex[m_outerSize] = m_outerIndex[m_outerSize-1] + m_innerNonZeros[m_outerSize-1] + reserveSizes[m_outerSize-1];
+        
+        m_data.resize(m_outerIndex[m_outerSize]);
+      }
+      else
+      {
+        Index* newOuterIndex = new Index[m_outerSize+1];
+        Index count = 0;
+        for(Index j=0; j<m_outerSize; ++j)
+        {
+          newOuterIndex[j] = count;
+          Index alreadyReserved = (m_outerIndex[j+1]-m_outerIndex[j]) - m_innerNonZeros[j];
+          Index toReserve = std::max<std::ptrdiff_t>(reserveSizes[j], alreadyReserved);
+          count += toReserve + m_innerNonZeros[j];
+        }
+        newOuterIndex[m_outerSize] = count;
+        
+        m_data.resize(count);
+        for(ptrdiff_t j=m_outerSize-1; j>=0; --j)
+        {
+          std::ptrdiff_t offset = newOuterIndex[j] - m_outerIndex[j];
+          if(offset>0)
+          {
+            std::ptrdiff_t innerNNZ = m_innerNonZeros[j];
+            for(std::ptrdiff_t i=innerNNZ-1; i>=0; --i)
+            {
+              m_data.index(newOuterIndex[j]+i) = m_data.index(m_outerIndex[j]+i);
+              m_data.value(newOuterIndex[j]+i) = m_data.value(m_outerIndex[j]+i);
+            }
+          }
+        }
+        
+        std::swap(m_outerIndex, newOuterIndex);
+        delete[] newOuterIndex;
+      }
+      
+    }
+  public:
+
+    //--- low level purely coherent filling ---
+
+    /** \internal
+      * \returns a reference to the non zero coefficient at position \a row, \a col assuming that:
+      * - the nonzero does not already exist
+      * - the new coefficient is the last one according to the storage order
+      *
+      * Before filling a given inner vector you must call the statVec(Index) function.
+      *
+      * After an insertion session, you should call the finalize() function.
+      *
+      * \sa insert, insertBackByOuterInner, startVec */
+    inline Scalar& insertBack(Index row, Index col)
+    {
+      return insertBackByOuterInner(IsRowMajor?row:col, IsRowMajor?col:row);
+    }
+
+    /** \internal
+      * \sa insertBack, startVec */
+    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
+    {
+      eigen_assert(size_t(m_outerIndex[outer+1]) == m_data.size() && "Invalid ordered insertion (invalid outer index)");
+      eigen_assert( (m_outerIndex[outer+1]-m_outerIndex[outer]==0 || m_data.index(m_data.size()-1)<inner) && "Invalid ordered insertion (invalid inner index)");
+      Index p = m_outerIndex[outer+1];
+      ++m_outerIndex[outer+1];
+      m_data.append(0, inner);
+      return m_data.value(p);
+    }
+
+    /** \internal
+      * \warning use it only if you know what you are doing */
+    inline Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner)
+    {
+      Index p = m_outerIndex[outer+1];
+      ++m_outerIndex[outer+1];
+      m_data.append(0, inner);
+      return m_data.value(p);
+    }
+
+    /** \internal
+      * \sa insertBack, insertBackByOuterInner */
+    inline void startVec(Index outer)
+    {
+      eigen_assert(m_outerIndex[outer]==int(m_data.size()) && "You must call startVec for each inner vector sequentially");
+      eigen_assert(m_outerIndex[outer+1]==0 && "You must call startVec for each inner vector sequentially");
+      m_outerIndex[outer+1] = m_outerIndex[outer];
+    }
+
+    /** \internal
+      * Must be called after inserting a set of non zero entries using the low level compressed API.
+      */
+    inline void finalize()
+    {
+      if(isCompressed())
+      {
+        Index size = static_cast<Index>(m_data.size());
+        Index i = m_outerSize;
+        // find the last filled column
+        while (i>=0 && m_outerIndex[i]==0)
+          --i;
+        ++i;
+        while (i<=m_outerSize)
+        {
+          m_outerIndex[i] = size;
+          ++i;
+        }
+      }
+    }
+
+    //---
+
+    template<typename InputIterators>
+    void setFromTriplets(const InputIterators& begin, const InputIterators& end);
+
+    void sumupDuplicates();
+
+    //---
+    
+    /** \internal
+      * same as insert(Index,Index) except that the indices are given relative to the storage order */
+    EIGEN_DONT_INLINE Scalar& insertByOuterInner(Index j, Index i)
+    {
+      return insert(IsRowMajor ? j : i, IsRowMajor ? i : j);
+    }
+
+    /** Turns the matrix into the \em compressed format.
+      */
+    void makeCompressed()
+    {
+      if(isCompressed())
+        return;
+      
+      Index oldStart = m_outerIndex[1];
+      m_outerIndex[1] = m_innerNonZeros[0];
+      for(Index j=1; j<m_outerSize; ++j)
+      {
+        Index nextOldStart = m_outerIndex[j+1];
+        std::ptrdiff_t offset = oldStart - m_outerIndex[j];
+        if(offset>0)
+        {
+          for(Index k=0; k<m_innerNonZeros[j]; ++k)
+          {
+            m_data.index(m_outerIndex[j]+k) = m_data.index(oldStart+k);
+            m_data.value(m_outerIndex[j]+k) = m_data.value(oldStart+k);
+          }
+        }
+        m_outerIndex[j+1] = m_outerIndex[j] + m_innerNonZeros[j];
+        oldStart = nextOldStart;
+      }
+      delete[] m_innerNonZeros;
+      m_innerNonZeros = 0;
+      m_data.resize(m_outerIndex[m_outerSize]);
+      m_data.squeeze();
+    }
+
+    /** Suppresses all nonzeros which are \b much \b smaller \b than \a reference under the tolerence \a epsilon */
+    void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision())
+    {
+      prune(default_prunning_func(reference,epsilon));
+    }
+    
+    /** Turns the matrix into compressed format, and suppresses all nonzeros which do not satisfy the predicate \a keep.
+      * The functor type \a KeepFunc must implement the following function:
+      * \code
+      * bool operator() (const Index& row, const Index& col, const Scalar& value) const;
+      * \endcode
+      * \sa prune(Scalar,RealScalar)
+      */
+    template<typename KeepFunc>
+    void prune(const KeepFunc& keep = KeepFunc())
+    {
+      // TODO optimize the uncompressed mode to avoid moving and allocating the data twice
+      // TODO also implement a unit test
+      makeCompressed();
+
+      Index k = 0;
+      for(Index j=0; j<m_outerSize; ++j)
+      {
+        Index previousStart = m_outerIndex[j];
+        m_outerIndex[j] = k;
+        Index end = m_outerIndex[j+1];
+        for(Index i=previousStart; i<end; ++i)
+        {
+          if(keep(IsRowMajor?j:m_data.index(i), IsRowMajor?m_data.index(i):j, m_data.value(i)))
+          {
+            m_data.value(k) = m_data.value(i);
+            m_data.index(k) = m_data.index(i);
+            ++k;
+          }
+        }
+      }
+      m_outerIndex[m_outerSize] = k;
+      m_data.resize(k,0);
+    }
+
+    /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero.
+      * \sa resizeNonZeros(Index), reserve(), setZero()
+      */
+    void resize(Index rows, Index cols)
+    {
+      const Index outerSize = IsRowMajor ? rows : cols;
+      m_innerSize = IsRowMajor ? cols : rows;
+      m_data.clear();
+      if (m_outerSize != outerSize || m_outerSize==0)
+      {
+        delete[] m_outerIndex;
+        m_outerIndex = new Index [outerSize+1];
+        m_outerSize = outerSize;
+      }
+      if(m_innerNonZeros)
+      {
+        delete[] m_innerNonZeros;
+        m_innerNonZeros = 0;
+      }
+      memset(m_outerIndex, 0, (m_outerSize+1)*sizeof(Index));
+    }
+
+    /** \internal
+      * Resize the nonzero vector to \a size */
+    void resizeNonZeros(Index size)
+    {
+      // TODO remove this function
+      m_data.resize(size);
+    }
+
+    /** \returns a const expression of the diagonal coefficients */
+    const Diagonal<const SparseMatrix> diagonal() const { return *this; }
+
+    /** Default constructor yielding an empty \c 0 \c x \c 0 matrix */
+    inline SparseMatrix()
+      : m_outerSize(-1), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      check_template_parameters();
+      resize(0, 0);
+    }
+
+    /** Constructs a \a rows \c x \a cols empty matrix */
+    inline SparseMatrix(Index rows, Index cols)
+      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      check_template_parameters();
+      resize(rows, cols);
+    }
+
+    /** Constructs a sparse matrix from the sparse expression \a other */
+    template<typename OtherDerived>
+    inline SparseMatrix(const SparseMatrixBase<OtherDerived>& other)
+      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      check_template_parameters();
+      *this = other.derived();
+    }
+
+    /** Copy constructor (it performs a deep copy) */
+    inline SparseMatrix(const SparseMatrix& other)
+      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      check_template_parameters();
+      *this = other.derived();
+    }
+
+    /** Swaps the content of two sparse matrices of the same type.
+      * This is a fast operation that simply swaps the underlying pointers and parameters. */
+    inline void swap(SparseMatrix& other)
+    {
+      //EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n");
+      std::swap(m_outerIndex, other.m_outerIndex);
+      std::swap(m_innerSize, other.m_innerSize);
+      std::swap(m_outerSize, other.m_outerSize);
+      std::swap(m_innerNonZeros, other.m_innerNonZeros);
+      m_data.swap(other.m_data);
+    }
+
+    inline SparseMatrix& operator=(const SparseMatrix& other)
+    {
+      if (other.isRValue())
+      {
+        swap(other.const_cast_derived());
+      }
+      else
+      {
+        initAssignment(other);
+        if(other.isCompressed())
+        {
+          memcpy(m_outerIndex, other.m_outerIndex, (m_outerSize+1)*sizeof(Index));
+          m_data = other.m_data;
+        }
+        else
+        {
+          Base::operator=(other);
+        }
+      }
+      return *this;
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename Lhs, typename Rhs>
+    inline SparseMatrix& operator=(const SparseSparseProduct<Lhs,Rhs>& product)
+    { return Base::operator=(product); }
+    
+    template<typename OtherDerived>
+    inline SparseMatrix& operator=(const ReturnByValue<OtherDerived>& other)
+    { return Base::operator=(other.derived()); }
+    
+    template<typename OtherDerived>
+    inline SparseMatrix& operator=(const EigenBase<OtherDerived>& other)
+    { return Base::operator=(other.derived()); }
+    #endif
+
+    template<typename OtherDerived>
+    EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+      initAssignment(other.derived());
+      const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+      if (needToTranspose)
+      {
+        // two passes algorithm:
+        //  1 - compute the number of coeffs per dest inner vector
+        //  2 - do the actual copy/eval
+        // Since each coeff of the rhs has to be evaluated twice, let's evaluate it if needed
+        typedef typename internal::nested<OtherDerived,2>::type OtherCopy;
+        typedef typename internal::remove_all<OtherCopy>::type _OtherCopy;
+        OtherCopy otherCopy(other.derived());
+
+        Eigen::Map<Matrix<Index, Dynamic, 1> > (m_outerIndex,outerSize()).setZero();
+        // pass 1
+        // FIXME the above copy could be merged with that pass
+        for (Index j=0; j<otherCopy.outerSize(); ++j)
+          for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
+            ++m_outerIndex[it.index()];
+
+        // prefix sum
+        Index count = 0;
+        VectorXi positions(outerSize());
+        for (Index j=0; j<outerSize(); ++j)
+        {
+          Index tmp = m_outerIndex[j];
+          m_outerIndex[j] = count;
+          positions[j] = count;
+          count += tmp;
+        }
+        m_outerIndex[outerSize()] = count;
+        // alloc
+        m_data.resize(count);
+        // pass 2
+        for (Index j=0; j<otherCopy.outerSize(); ++j)
+        {
+          for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
+          {
+            Index pos = positions[it.index()]++;
+            m_data.index(pos) = j;
+            m_data.value(pos) = it.value();
+          }
+        }
+        return *this;
+      }
+      else
+      {
+        // there is no special optimization
+        return Base::operator=(other.derived());
+      }
+    }
+
+    friend std::ostream & operator << (std::ostream & s, const SparseMatrix& m)
+    {
+      EIGEN_DBG_SPARSE(
+        s << "Nonzero entries:\n";
+        if(m.isCompressed())
+          for (Index i=0; i<m.nonZeros(); ++i)
+            s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
+        else
+          for (Index i=0; i<m.outerSize(); ++i)
+          {
+            int p = m.m_outerIndex[i];
+            int pe = m.m_outerIndex[i]+m.m_innerNonZeros[i];
+            Index k=p;
+            for (; k<pe; ++k)
+              s << "(" << m.m_data.value(k) << "," << m.m_data.index(k) << ") ";
+            for (; k<m.m_outerIndex[i+1]; ++k)
+              s << "(_,_) ";
+          }
+        s << std::endl;
+        s << std::endl;
+        s << "Outer pointers:\n";
+        for (Index i=0; i<m.outerSize(); ++i)
+          s << m.m_outerIndex[i] << " ";
+        s << " $" << std::endl;
+        if(!m.isCompressed())
+        {
+          s << "Inner non zeros:\n";
+          for (Index i=0; i<m.outerSize(); ++i)
+            s << m.m_innerNonZeros[i] << " ";
+          s << " $" << std::endl;
+        }
+        s << std::endl;
+      );
+      s << static_cast<const SparseMatrixBase<SparseMatrix>&>(m);
+      return s;
+    }
+
+    /** Destructor */
+    inline ~SparseMatrix()
+    {
+      delete[] m_outerIndex;
+      delete[] m_innerNonZeros;
+    }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** Overloaded for performance */
+    Scalar sum() const;
+#endif
+    
+#   ifdef EIGEN_SPARSEMATRIX_PLUGIN
+#     include EIGEN_SPARSEMATRIX_PLUGIN
+#   endif
+
+protected:
+
+    template<typename Other>
+    void initAssignment(const Other& other)
+    {
+      resize(other.rows(), other.cols());
+      if(m_innerNonZeros)
+      {
+        delete[] m_innerNonZeros;
+        m_innerNonZeros = 0;
+      }
+    }
+
+    /** \internal
+      * \sa insert(Index,Index) */
+    EIGEN_DONT_INLINE Scalar& insertCompressed(Index row, Index col)
+    {
+      eigen_assert(isCompressed());
+
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index previousOuter = outer;
+      if (m_outerIndex[outer+1]==0)
+      {
+        // we start a new inner vector
+        while (previousOuter>=0 && m_outerIndex[previousOuter]==0)
+        {
+          m_outerIndex[previousOuter] = static_cast<Index>(m_data.size());
+          --previousOuter;
+        }
+        m_outerIndex[outer+1] = m_outerIndex[outer];
+      }
+
+      // here we have to handle the tricky case where the outerIndex array
+      // starts with: [ 0 0 0 0 0 1 ...] and we are inserted in, e.g.,
+      // the 2nd inner vector...
+      bool isLastVec = (!(previousOuter==-1 && m_data.size()!=0))
+                    && (size_t(m_outerIndex[outer+1]) == m_data.size());
+
+      size_t startId = m_outerIndex[outer];
+      // FIXME let's make sure sizeof(long int) == sizeof(size_t)
+      size_t p = m_outerIndex[outer+1];
+      ++m_outerIndex[outer+1];
+
+      float reallocRatio = 1;
+      if (m_data.allocatedSize()<=m_data.size())
+      {
+        // if there is no preallocated memory, let's reserve a minimum of 32 elements
+        if (m_data.size()==0)
+        {
+          m_data.reserve(32);
+        }
+        else
+        {
+          // we need to reallocate the data, to reduce multiple reallocations
+          // we use a smart resize algorithm based on the current filling ratio
+          // in addition, we use float to avoid integers overflows
+          float nnzEstimate = float(m_outerIndex[outer])*float(m_outerSize)/float(outer+1);
+          reallocRatio = (nnzEstimate-float(m_data.size()))/float(m_data.size());
+          // furthermore we bound the realloc ratio to:
+          //   1) reduce multiple minor realloc when the matrix is almost filled
+          //   2) avoid to allocate too much memory when the matrix is almost empty
+          reallocRatio = (std::min)((std::max)(reallocRatio,1.5f),8.f);
+        }
+      }
+      m_data.resize(m_data.size()+1,reallocRatio);
+
+      if (!isLastVec)
+      {
+        if (previousOuter==-1)
+        {
+          // oops wrong guess.
+          // let's correct the outer offsets
+          for (Index k=0; k<=(outer+1); ++k)
+            m_outerIndex[k] = 0;
+          Index k=outer+1;
+          while(m_outerIndex[k]==0)
+            m_outerIndex[k++] = 1;
+          while (k<=m_outerSize && m_outerIndex[k]!=0)
+            m_outerIndex[k++]++;
+          p = 0;
+          --k;
+          k = m_outerIndex[k]-1;
+          while (k>0)
+          {
+            m_data.index(k) = m_data.index(k-1);
+            m_data.value(k) = m_data.value(k-1);
+            k--;
+          }
+        }
+        else
+        {
+          // we are not inserting into the last inner vec
+          // update outer indices:
+          Index j = outer+2;
+          while (j<=m_outerSize && m_outerIndex[j]!=0)
+            m_outerIndex[j++]++;
+          --j;
+          // shift data of last vecs:
+          Index k = m_outerIndex[j]-1;
+          while (k>=Index(p))
+          {
+            m_data.index(k) = m_data.index(k-1);
+            m_data.value(k) = m_data.value(k-1);
+            k--;
+          }
+        }
+      }
+
+      while ( (p > startId) && (m_data.index(p-1) > inner) )
+      {
+        m_data.index(p) = m_data.index(p-1);
+        m_data.value(p) = m_data.value(p-1);
+        --p;
+      }
+
+      m_data.index(p) = inner;
+      return (m_data.value(p) = 0);
+    }
+
+    /** \internal
+      * A vector object that is equal to 0 everywhere but v at the position i */
+    class SingletonVector
+    {
+        Index m_index;
+        Index m_value;
+      public:
+        typedef Index value_type;
+        SingletonVector(Index i, Index v)
+          : m_index(i), m_value(v)
+        {}
+
+        Index operator[](Index i) const { return i==m_index ? m_value : 0; }
+    };
+
+    /** \internal
+      * \sa insert(Index,Index) */
+    EIGEN_DONT_INLINE Scalar& insertUncompressed(Index row, Index col)
+    {
+      eigen_assert(!isCompressed());
+
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      std::ptrdiff_t room = m_outerIndex[outer+1] - m_outerIndex[outer];
+      std::ptrdiff_t innerNNZ = m_innerNonZeros[outer];
+      if(innerNNZ>=room)
+      {
+        // this inner vector is full, we need to reallocate the whole buffer :(
+        reserve(SingletonVector(outer,std::max<std::ptrdiff_t>(2,innerNNZ)));
+      }
+
+      Index startId = m_outerIndex[outer];
+      Index p = startId + m_innerNonZeros[outer];
+      while ( (p > startId) && (m_data.index(p-1) > inner) )
+      {
+        m_data.index(p) = m_data.index(p-1);
+        m_data.value(p) = m_data.value(p-1);
+        --p;
+      }
+
+      m_innerNonZeros[outer]++;
+
+      m_data.index(p) = inner;
+      return (m_data.value(p) = 0);
+    }
+
+public:
+    /** \internal
+      * \sa insert(Index,Index) */
+    inline Scalar& insertBackUncompressed(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      eigen_assert(!isCompressed());
+      eigen_assert(m_innerNonZeros[outer]<=(m_outerIndex[outer+1] - m_outerIndex[outer]));
+
+      Index p = m_outerIndex[outer] + m_innerNonZeros[outer];
+      m_innerNonZeros[outer]++;
+      m_data.index(p) = inner;
+      return (m_data.value(p) = 0);
+    }
+
+private:
+  static void check_template_parameters()
+  {
+    EIGEN_STATIC_ASSERT(NumTraits<Index>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
+  }
+
+  struct default_prunning_func {
+    default_prunning_func(Scalar ref, RealScalar eps) : reference(ref), epsilon(eps) {}
+    inline bool operator() (const Index&, const Index&, const Scalar& value) const
+    {
+      return !internal::isMuchSmallerThan(value, reference, epsilon);
+    }
+    Scalar reference;
+    RealScalar epsilon;
+  };
+};
+
+template<typename Scalar, int _Options, typename _Index>
+class SparseMatrix<Scalar,_Options,_Index>::InnerIterator
+{
+  public:
+    InnerIterator(const SparseMatrix& mat, Index outer)
+      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer), m_id(mat.m_outerIndex[outer])
+    {
+      if(mat.isCompressed())
+        m_end = mat.m_outerIndex[outer+1];
+      else
+        m_end = m_id + mat.m_innerNonZeros[outer];
+    }
+
+    inline InnerIterator& operator++() { m_id++; return *this; }
+
+    inline const Scalar& value() const { return m_values[m_id]; }
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id]); }
+
+    inline Index index() const { return m_indices[m_id]; }
+    inline Index outer() const { return m_outer; }
+    inline Index row() const { return IsRowMajor ? m_outer : index(); }
+    inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+    inline operator bool() const { return (m_id < m_end); }
+
+  protected:
+    const Scalar* m_values;
+    const Index* m_indices;
+    const Index m_outer;
+    Index m_id;
+    Index m_end;
+};
+
+template<typename Scalar, int _Options, typename _Index>
+class SparseMatrix<Scalar,_Options,_Index>::ReverseInnerIterator
+{
+  public:
+    ReverseInnerIterator(const SparseMatrix& mat, Index outer)
+      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer), m_start(mat.m_outerIndex[outer])
+    {
+      if(mat.isCompressed())
+        m_id = mat.m_outerIndex[outer+1];
+      else
+        m_id = m_start + mat.m_innerNonZeros[outer];
+    }
+
+    inline ReverseInnerIterator& operator--() { --m_id; return *this; }
+
+    inline const Scalar& value() const { return m_values[m_id-1]; }
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id-1]); }
+
+    inline Index index() const { return m_indices[m_id-1]; }
+    inline Index outer() const { return m_outer; }
+    inline Index row() const { return IsRowMajor ? m_outer : index(); }
+    inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+    inline operator bool() const { return (m_id > m_start); }
+
+  protected:
+    const Scalar* m_values;
+    const Index* m_indices;
+    const Index m_outer;
+    Index m_id;
+    const Index m_start;
+};
+
+namespace internal {
+
+template<typename InputIterator, typename SparseMatrixType>
+void set_from_triplets(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat, int Options = 0)
+{
+  EIGEN_UNUSED_VARIABLE(Options);
+  enum { IsRowMajor = SparseMatrixType::IsRowMajor };
+  typedef typename SparseMatrixType::Scalar Scalar;
+  typedef typename SparseMatrixType::Index Index;
+  SparseMatrix<Scalar,IsRowMajor?ColMajor:RowMajor> trMat(mat.rows(),mat.cols());
+
+  // pass 1: count the nnz per inner-vector
+  VectorXi wi(trMat.outerSize());
+  wi.setZero();
+  for(InputIterator it(begin); it!=end; ++it)
+    wi(IsRowMajor ? it->col() : it->row())++;
+
+  // pass 2: insert all the elements into trMat
+  trMat.reserve(wi);
+  for(InputIterator it(begin); it!=end; ++it)
+    trMat.insertBackUncompressed(it->row(),it->col()) = it->value();
+
+  // pass 3:
+  trMat.sumupDuplicates();
+
+  // pass 4: transposed copy -> implicit sorting
+  mat = trMat;
+}
+
+}
+
+
+/** Fill the matrix \c *this with the list of \em triplets defined by the iterator range \a begin - \b.
+  *
+  * A \em triplet is a tuple (i,j,value) defining a non-zero element.
+  * The input list of triplets does not have to be sorted, and can contains duplicated elements.
+  * In any case, the result is a \b sorted and \b compressed sparse matrix where the duplicates have been summed up.
+  * This is a \em O(n) operation, with \em n the number of triplet elements.
+  * The initial contents of \c *this is destroyed.
+  * The matrix \c *this must be properly resized beforehand using the SparseMatrix(Index,Index) constructor,
+  * or the resize(Index,Index) method. The sizes are not extracted from the triplet list.
+  *
+  * The \a InputIterators value_type must provide the following interface:
+  * \code
+  * Scalar value() const; // the value
+  * Scalar row() const;   // the row index i
+  * Scalar col() const;   // the column index j
+  * \endcode
+  * See for instance the Eigen::Triplet template class.
+  *
+  * Here is a typical usage example:
+  * \code
+    typedef Triplet<double> T;
+    std::vector<T> tripletList;
+    triplets.reserve(estimation_of_entries);
+    for(...)
+    {
+      // ...
+      tripletList.push_back(T(i,j,v_ij));
+    }
+    SparseMatrixType m(rows,cols);
+    m.setFromTriplets(tripletList.begin(), tripletList.end());
+    // m is ready to go!
+  * \endcode
+  *
+  * \warning The list of triplets is read multiple times (at least twice). Therefore, it is not recommended to define
+  * an abstract iterator over a complex data-structure that would be expensive to evaluate. The triplets should rather
+  * be explicitely stored into a std::vector for instance.
+  */
+template<typename Scalar, int _Options, typename _Index>
+template<typename InputIterators>
+void SparseMatrix<Scalar,_Options,_Index>::setFromTriplets(const InputIterators& begin, const InputIterators& end)
+{
+  internal::set_from_triplets(begin, end, *this);
+}
+
+/** \internal */
+template<typename Scalar, int _Options, typename _Index>
+void SparseMatrix<Scalar,_Options,_Index>::sumupDuplicates()
+{
+  eigen_assert(!isCompressed());
+  // TODO, in practice we should be able to use m_innerNonZeros for that task
+  VectorXi wi(innerSize());
+  wi.fill(-1);
+  Index count = 0;
+  // for each inner-vector, wi[inner_index] will hold the position of first element into the index/value buffers
+  for(int j=0; j<outerSize(); ++j)
+  {
+    Index start   = count;
+    Index oldEnd  = m_outerIndex[j]+m_innerNonZeros[j];
+    for(Index k=m_outerIndex[j]; k<oldEnd; ++k)
+    {
+      Index i = m_data.index(k);
+      if(wi(i)>=start)
+      {
+        // we already meet this entry => accumulate it
+        m_data.value(wi(i)) += m_data.value(k);
+      }
+      else
+      {
+        m_data.value(count) = m_data.value(k);
+        m_data.index(count) = m_data.index(k);
+        wi(i) = count;
+        ++count;
+      }
+    }
+    m_outerIndex[j] = start;
+  }
+  m_outerIndex[m_outerSize] = count;
+
+  // turn the matrix into compressed form
+  delete[] m_innerNonZeros;
+  m_innerNonZeros = 0;
+  m_data.resize(m_outerIndex[m_outerSize]);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEMATRIX_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseMatrixBase.h b/extern/Eigen3/Eigen/src/SparseCore/SparseMatrixBase.h
new file mode 100644
index 00000000000..ed7da68928d
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseMatrixBase.h
@@ -0,0 +1,473 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEMATRIXBASE_H
+#define EIGEN_SPARSEMATRIXBASE_H
+
+namespace Eigen { 
+
+/** \ingroup SparseCore_Module
+  *
+  * \class SparseMatrixBase
+  *
+  * \brief Base class of any sparse matrices or sparse expressions
+  *
+  * \tparam Derived
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_SPARSEMATRIXBASE_PLUGIN.
+  */
+template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
+{
+  public:
+
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::Index Index;
+    typedef typename internal::add_const_on_value_type_if_arithmetic<
+                         typename internal::packet_traits<Scalar>::type
+                     >::type PacketReturnType;
+
+    typedef SparseMatrixBase StorageBaseType;
+    typedef EigenBase<Derived> Base;
+    
+    template<typename OtherDerived>
+    Derived& operator=(const EigenBase<OtherDerived> &other)
+    {
+      other.derived().evalTo(derived());
+      return derived();
+    }
+
+    enum {
+
+      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+        /**< The number of rows at compile-time. This is just a copy of the value provided
+          * by the \a Derived type. If a value is not known at compile-time,
+          * it is set to the \a Dynamic constant.
+          * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
+
+      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+        /**< The number of columns at compile-time. This is just a copy of the value provided
+          * by the \a Derived type. If a value is not known at compile-time,
+          * it is set to the \a Dynamic constant.
+          * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
+
+
+      SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime,
+                                                   internal::traits<Derived>::ColsAtCompileTime>::ret),
+        /**< This is equal to the number of coefficients, i.e. the number of
+          * rows times the number of columns, or to \a Dynamic if this is not
+          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
+
+      MaxRowsAtCompileTime = RowsAtCompileTime,
+      MaxColsAtCompileTime = ColsAtCompileTime,
+
+      MaxSizeAtCompileTime = (internal::size_at_compile_time<MaxRowsAtCompileTime,
+                                                      MaxColsAtCompileTime>::ret),
+
+      IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1,
+        /**< This is set to true if either the number of rows or the number of
+          * columns is known at compile-time to be equal to 1. Indeed, in that case,
+          * we are dealing with a column-vector (if there is only one column) or with
+          * a row-vector (if there is only one row). */
+
+      Flags = internal::traits<Derived>::Flags,
+        /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
+          * constructed from this one. See the \ref flags "list of flags".
+          */
+
+      CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
+        /**< This is a rough measure of how expensive it is to read one coefficient from
+          * this expression.
+          */
+
+      IsRowMajor = Flags&RowMajorBit ? 1 : 0,
+
+      #ifndef EIGEN_PARSED_BY_DOXYGEN
+      _HasDirectAccess = (int(Flags)&DirectAccessBit) ? 1 : 0 // workaround sunCC
+      #endif
+    };
+
+    /** \internal the return type of MatrixBase::adjoint() */
+    typedef typename internal::conditional<NumTraits<Scalar>::IsComplex,
+                        CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, Eigen::Transpose<const Derived> >,
+                        Transpose<const Derived>
+                     >::type AdjointReturnType;
+
+
+    typedef SparseMatrix<Scalar, Flags&RowMajorBit ? RowMajor : ColMajor> PlainObject;
+
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** This is the "real scalar" type; if the \a Scalar type is already real numbers
+      * (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
+      * \a Scalar is \a std::complex<T> then RealScalar is \a T.
+      *
+      * \sa class NumTraits
+      */
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    /** \internal the return type of coeff()
+      */
+    typedef typename internal::conditional<_HasDirectAccess, const Scalar&, Scalar>::type CoeffReturnType;
+
+    /** \internal Represents a matrix with all coefficients equal to one another*/
+    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Matrix<Scalar,Dynamic,Dynamic> > ConstantReturnType;
+
+    /** type of the equivalent square matrix */
+    typedef Matrix<Scalar,EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime),
+                          EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType;
+
+    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    inline Derived& derived() { return *static_cast<Derived*>(this); }
+    inline Derived& const_cast_derived() const
+    { return *static_cast<Derived*>(const_cast<SparseMatrixBase*>(this)); }
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::SparseMatrixBase
+#   include "../plugins/CommonCwiseUnaryOps.h"
+#   include "../plugins/CommonCwiseBinaryOps.h"
+#   include "../plugins/MatrixCwiseUnaryOps.h"
+#   include "../plugins/MatrixCwiseBinaryOps.h"
+#   ifdef EIGEN_SPARSEMATRIXBASE_PLUGIN
+#     include EIGEN_SPARSEMATRIXBASE_PLUGIN
+#   endif
+#   undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+
+
+    /** \returns the number of rows. \sa cols() */
+    inline Index rows() const { return derived().rows(); }
+    /** \returns the number of columns. \sa rows() */
+    inline Index cols() const { return derived().cols(); }
+    /** \returns the number of coefficients, which is \a rows()*cols().
+      * \sa rows(), cols(). */
+    inline Index size() const { return rows() * cols(); }
+    /** \returns the number of nonzero coefficients which is in practice the number
+      * of stored coefficients. */
+    inline Index nonZeros() const { return derived().nonZeros(); }
+    /** \returns true if either the number of rows or the number of columns is equal to 1.
+      * In other words, this function returns
+      * \code rows()==1 || cols()==1 \endcode
+      * \sa rows(), cols(), IsVectorAtCompileTime. */
+    inline bool isVector() const { return rows()==1 || cols()==1; }
+    /** \returns the size of the storage major dimension,
+      * i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
+    Index outerSize() const { return (int(Flags)&RowMajorBit) ? this->rows() : this->cols(); }
+    /** \returns the size of the inner dimension according to the storage order,
+      * i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
+    Index innerSize() const { return (int(Flags)&RowMajorBit) ? this->cols() : this->rows(); }
+
+    bool isRValue() const { return m_isRValue; }
+    Derived& markAsRValue() { m_isRValue = true; return derived(); }
+
+    SparseMatrixBase() : m_isRValue(false) { /* TODO check flags */ }
+
+    
+    template<typename OtherDerived>
+    Derived& operator=(const ReturnByValue<OtherDerived>& other)
+    {
+      other.evalTo(derived());
+      return derived();
+    }
+
+
+    template<typename OtherDerived>
+    inline Derived& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+      return assign(other.derived());
+    }
+
+    inline Derived& operator=(const Derived& other)
+    {
+//       if (other.isRValue())
+//         derived().swap(other.const_cast_derived());
+//       else
+      return assign(other.derived());
+    }
+
+  protected:
+
+    template<typename OtherDerived>
+    inline Derived& assign(const OtherDerived& other)
+    {
+      const bool transpose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+      const Index outerSize = (int(OtherDerived::Flags) & RowMajorBit) ? other.rows() : other.cols();
+      if ((!transpose) && other.isRValue())
+      {
+        // eval without temporary
+        derived().resize(other.rows(), other.cols());
+        derived().setZero();
+        derived().reserve((std::max)(this->rows(),this->cols())*2);
+        for (Index j=0; j<outerSize; ++j)
+        {
+          derived().startVec(j);
+          for (typename OtherDerived::InnerIterator it(other, j); it; ++it)
+          {
+            Scalar v = it.value();
+            derived().insertBackByOuterInner(j,it.index()) = v;
+          }
+        }
+        derived().finalize();
+      }
+      else
+      {
+        assignGeneric(other);
+      }
+      return derived();
+    }
+
+    template<typename OtherDerived>
+    inline void assignGeneric(const OtherDerived& other)
+    {
+      //const bool transpose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+      eigen_assert(( ((internal::traits<Derived>::SupportedAccessPatterns&OuterRandomAccessPattern)==OuterRandomAccessPattern) ||
+                  (!((Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit)))) &&
+                  "the transpose operation is supposed to be handled in SparseMatrix::operator=");
+
+      enum { Flip = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit) };
+
+      const Index outerSize = other.outerSize();
+      //typedef typename internal::conditional<transpose, LinkedVectorMatrix<Scalar,Flags&RowMajorBit>, Derived>::type TempType;
+      // thanks to shallow copies, we always eval to a tempary
+      Derived temp(other.rows(), other.cols());
+
+      temp.reserve((std::max)(this->rows(),this->cols())*2);
+      for (Index j=0; j<outerSize; ++j)
+      {
+        temp.startVec(j);
+        for (typename OtherDerived::InnerIterator it(other.derived(), j); it; ++it)
+        {
+          Scalar v = it.value();
+          temp.insertBackByOuterInner(Flip?it.index():j,Flip?j:it.index()) = v;
+        }
+      }
+      temp.finalize();
+
+      derived() = temp.markAsRValue();
+    }
+
+  public:
+
+    template<typename Lhs, typename Rhs>
+    inline Derived& operator=(const SparseSparseProduct<Lhs,Rhs>& product);
+
+    friend std::ostream & operator << (std::ostream & s, const SparseMatrixBase& m)
+    {
+      typedef typename Derived::Nested Nested;
+      typedef typename internal::remove_all<Nested>::type NestedCleaned;
+
+      if (Flags&RowMajorBit)
+      {
+        const Nested nm(m.derived());
+        for (Index row=0; row<nm.outerSize(); ++row)
+        {
+          Index col = 0;
+          for (typename NestedCleaned::InnerIterator it(nm.derived(), row); it; ++it)
+          {
+            for ( ; col<it.index(); ++col)
+              s << "0 ";
+            s << it.value() << " ";
+            ++col;
+          }
+          for ( ; col<m.cols(); ++col)
+            s << "0 ";
+          s << std::endl;
+        }
+      }
+      else
+      {
+        const Nested nm(m.derived());
+        if (m.cols() == 1) {
+          Index row = 0;
+          for (typename NestedCleaned::InnerIterator it(nm.derived(), 0); it; ++it)
+          {
+            for ( ; row<it.index(); ++row)
+              s << "0" << std::endl;
+            s << it.value() << std::endl;
+            ++row;
+          }
+          for ( ; row<m.rows(); ++row)
+            s << "0" << std::endl;
+        }
+        else
+        {
+          SparseMatrix<Scalar, RowMajorBit> trans = m;
+          s << static_cast<const SparseMatrixBase<SparseMatrix<Scalar, RowMajorBit> >&>(trans);
+        }
+      }
+      return s;
+    }
+
+    template<typename OtherDerived>
+    Derived& operator+=(const SparseMatrixBase<OtherDerived>& other);
+    template<typename OtherDerived>
+    Derived& operator-=(const SparseMatrixBase<OtherDerived>& other);
+
+    Derived& operator*=(const Scalar& other);
+    Derived& operator/=(const Scalar& other);
+
+    #define EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE \
+      CwiseBinaryOp< \
+        internal::scalar_product_op< \
+          typename internal::scalar_product_traits< \
+            typename internal::traits<Derived>::Scalar, \
+            typename internal::traits<OtherDerived>::Scalar \
+          >::ReturnType \
+        >, \
+        Derived, \
+        OtherDerived \
+      >
+
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE const EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE
+    cwiseProduct(const MatrixBase<OtherDerived> &other) const;
+
+    // sparse * sparse
+    template<typename OtherDerived>
+    const typename SparseSparseProductReturnType<Derived,OtherDerived>::Type
+    operator*(const SparseMatrixBase<OtherDerived> &other) const;
+
+    // sparse * diagonal
+    template<typename OtherDerived>
+    const SparseDiagonalProduct<Derived,OtherDerived>
+    operator*(const DiagonalBase<OtherDerived> &other) const;
+
+    // diagonal * sparse
+    template<typename OtherDerived> friend
+    const SparseDiagonalProduct<OtherDerived,Derived>
+    operator*(const DiagonalBase<OtherDerived> &lhs, const SparseMatrixBase& rhs)
+    { return SparseDiagonalProduct<OtherDerived,Derived>(lhs.derived(), rhs.derived()); }
+
+    /** dense * sparse (return a dense object unless it is an outer product) */
+    template<typename OtherDerived> friend
+    const typename DenseSparseProductReturnType<OtherDerived,Derived>::Type
+    operator*(const MatrixBase<OtherDerived>& lhs, const Derived& rhs)
+    { return typename DenseSparseProductReturnType<OtherDerived,Derived>::Type(lhs.derived(),rhs); }
+
+    /** sparse * dense (returns a dense object unless it is an outer product) */
+    template<typename OtherDerived>
+    const typename SparseDenseProductReturnType<Derived,OtherDerived>::Type
+    operator*(const MatrixBase<OtherDerived> &other) const;
+    
+     /** \returns an expression of P^-1 H P */
+    SparseSymmetricPermutationProduct<Derived,Upper|Lower> twistedBy(const PermutationMatrix<Dynamic,Dynamic,Index>& perm) const
+    {
+      return SparseSymmetricPermutationProduct<Derived,Upper|Lower>(derived(), perm);
+    }
+
+    template<typename OtherDerived>
+    Derived& operator*=(const SparseMatrixBase<OtherDerived>& other);
+
+    #ifdef EIGEN2_SUPPORT
+    // deprecated
+    template<typename OtherDerived>
+    typename internal::plain_matrix_type_column_major<OtherDerived>::type
+    solveTriangular(const MatrixBase<OtherDerived>& other) const;
+
+    // deprecated
+    template<typename OtherDerived>
+    void solveTriangularInPlace(MatrixBase<OtherDerived>& other) const;
+    #endif // EIGEN2_SUPPORT
+
+    template<int Mode>
+    inline const SparseTriangularView<Derived, Mode> triangularView() const;
+
+    template<unsigned int UpLo> inline const SparseSelfAdjointView<Derived, UpLo> selfadjointView() const;
+    template<unsigned int UpLo> inline SparseSelfAdjointView<Derived, UpLo> selfadjointView();
+
+    template<typename OtherDerived> Scalar dot(const MatrixBase<OtherDerived>& other) const;
+    template<typename OtherDerived> Scalar dot(const SparseMatrixBase<OtherDerived>& other) const;
+    RealScalar squaredNorm() const;
+    RealScalar norm()  const;
+
+    Transpose<Derived> transpose() { return derived(); }
+    const Transpose<const Derived> transpose() const { return derived(); }
+    const AdjointReturnType adjoint() const { return transpose(); }
+
+    // sub-vector
+    SparseInnerVectorSet<Derived,1> row(Index i);
+    const SparseInnerVectorSet<Derived,1> row(Index i) const;
+    SparseInnerVectorSet<Derived,1> col(Index j);
+    const SparseInnerVectorSet<Derived,1> col(Index j) const;
+    SparseInnerVectorSet<Derived,1> innerVector(Index outer);
+    const SparseInnerVectorSet<Derived,1> innerVector(Index outer) const;
+
+    // set of sub-vectors
+    SparseInnerVectorSet<Derived,Dynamic> subrows(Index start, Index size);
+    const SparseInnerVectorSet<Derived,Dynamic> subrows(Index start, Index size) const;
+    SparseInnerVectorSet<Derived,Dynamic> subcols(Index start, Index size);
+    const SparseInnerVectorSet<Derived,Dynamic> subcols(Index start, Index size) const;
+    
+    SparseInnerVectorSet<Derived,Dynamic> middleRows(Index start, Index size);
+    const SparseInnerVectorSet<Derived,Dynamic> middleRows(Index start, Index size) const;
+    SparseInnerVectorSet<Derived,Dynamic> middleCols(Index start, Index size);
+    const SparseInnerVectorSet<Derived,Dynamic> middleCols(Index start, Index size) const;
+    SparseInnerVectorSet<Derived,Dynamic> innerVectors(Index outerStart, Index outerSize);
+    const SparseInnerVectorSet<Derived,Dynamic> innerVectors(Index outerStart, Index outerSize) const;
+
+      /** \internal use operator= */
+      template<typename DenseDerived>
+      void evalTo(MatrixBase<DenseDerived>& dst) const
+      {
+        dst.setZero();
+        for (Index j=0; j<outerSize(); ++j)
+          for (typename Derived::InnerIterator i(derived(),j); i; ++i)
+            dst.coeffRef(i.row(),i.col()) = i.value();
+      }
+
+      Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> toDense() const
+      {
+        return derived();
+      }
+
+    template<typename OtherDerived>
+    bool isApprox(const SparseMatrixBase<OtherDerived>& other,
+                  RealScalar prec = NumTraits<Scalar>::dummy_precision()) const
+    { return toDense().isApprox(other.toDense(),prec); }
+
+    template<typename OtherDerived>
+    bool isApprox(const MatrixBase<OtherDerived>& other,
+                  RealScalar prec = NumTraits<Scalar>::dummy_precision()) const
+    { return toDense().isApprox(other,prec); }
+
+    /** \returns the matrix or vector obtained by evaluating this expression.
+      *
+      * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
+      * a const reference, in order to avoid a useless copy.
+      */
+    inline const typename internal::eval<Derived>::type eval() const
+    { return typename internal::eval<Derived>::type(derived()); }
+
+    Scalar sum() const;
+
+  protected:
+
+    bool m_isRValue;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEMATRIXBASE_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparsePermutation.h b/extern/Eigen3/Eigen/src/SparseCore/SparsePermutation.h
new file mode 100644
index 00000000000..b1f2a283fb2
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparsePermutation.h
@@ -0,0 +1,163 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_PERMUTATION_H
+#define EIGEN_SPARSE_PERMUTATION_H
+
+// This file implements sparse * permutation products
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
+struct traits<permut_sparsematrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
+{
+  typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
+  typedef typename MatrixTypeNestedCleaned::Scalar Scalar;
+  typedef typename MatrixTypeNestedCleaned::Index Index;
+  enum {
+    SrcStorageOrder = MatrixTypeNestedCleaned::Flags&RowMajorBit ? RowMajor : ColMajor,
+    MoveOuter = SrcStorageOrder==RowMajor ? Side==OnTheLeft : Side==OnTheRight
+  };
+
+  typedef typename internal::conditional<MoveOuter,
+        SparseMatrix<Scalar,SrcStorageOrder,Index>,
+        SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,Index> >::type ReturnType;
+};
+
+template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
+struct permut_sparsematrix_product_retval
+ : public ReturnByValue<permut_sparsematrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
+{
+    typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
+    typedef typename MatrixTypeNestedCleaned::Scalar Scalar;
+    typedef typename MatrixTypeNestedCleaned::Index Index;
+
+    enum {
+      SrcStorageOrder = MatrixTypeNestedCleaned::Flags&RowMajorBit ? RowMajor : ColMajor,
+      MoveOuter = SrcStorageOrder==RowMajor ? Side==OnTheLeft : Side==OnTheRight
+    };
+
+    permut_sparsematrix_product_retval(const PermutationType& perm, const MatrixType& matrix)
+      : m_permutation(perm), m_matrix(matrix)
+    {}
+
+    inline int rows() const { return m_matrix.rows(); }
+    inline int cols() const { return m_matrix.cols(); }
+
+    template<typename Dest> inline void evalTo(Dest& dst) const
+    {
+      if(MoveOuter)
+      {
+        SparseMatrix<Scalar,SrcStorageOrder,Index> tmp(m_matrix.rows(), m_matrix.cols());
+        VectorXi sizes(m_matrix.outerSize());
+        for(Index j=0; j<m_matrix.outerSize(); ++j)
+        {
+          Index jp = m_permutation.indices().coeff(j);
+          sizes[((Side==OnTheLeft) ^ Transposed) ? jp : j] = m_matrix.innerVector(((Side==OnTheRight) ^ Transposed) ? jp : j).size();
+        }
+        tmp.reserve(sizes);
+        for(Index j=0; j<m_matrix.outerSize(); ++j)
+        {
+          Index jp = m_permutation.indices().coeff(j);
+          Index jsrc = ((Side==OnTheRight) ^ Transposed) ? jp : j;
+          Index jdst = ((Side==OnTheLeft) ^ Transposed) ? jp : j;
+          for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,jsrc); it; ++it)
+            tmp.insertByOuterInner(jdst,it.index()) = it.value();
+        }
+        dst = tmp;
+      }
+      else
+      {
+        SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,Index> tmp(m_matrix.rows(), m_matrix.cols());
+        VectorXi sizes(tmp.outerSize());
+        sizes.setZero();
+        PermutationMatrix<Dynamic,Dynamic,Index> perm;
+        if((Side==OnTheLeft) ^ Transposed)
+          perm = m_permutation;
+        else
+          perm = m_permutation.transpose();
+
+        for(Index j=0; j<m_matrix.outerSize(); ++j)
+          for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,j); it; ++it)
+            sizes[perm.indices().coeff(it.index())]++;
+        tmp.reserve(sizes);
+        for(Index j=0; j<m_matrix.outerSize(); ++j)
+          for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,j); it; ++it)
+            tmp.insertByOuterInner(perm.indices().coeff(it.index()),j) = it.value();
+        dst = tmp;
+      }
+    }
+
+  protected:
+    const PermutationType& m_permutation;
+    typename MatrixType::Nested m_matrix;
+};
+
+}
+
+
+
+/** \returns the matrix with the permutation applied to the columns
+  */
+template<typename SparseDerived, typename PermDerived>
+inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, false>
+operator*(const SparseMatrixBase<SparseDerived>& matrix, const PermutationBase<PermDerived>& perm)
+{
+  return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, false>(perm, matrix.derived());
+}
+
+/** \returns the matrix with the permutation applied to the rows
+  */
+template<typename SparseDerived, typename PermDerived>
+inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, false>
+operator*( const PermutationBase<PermDerived>& perm, const SparseMatrixBase<SparseDerived>& matrix)
+{
+  return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, false>(perm, matrix.derived());
+}
+
+
+
+/** \returns the matrix with the inverse permutation applied to the columns.
+  */
+template<typename SparseDerived, typename PermDerived>
+inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, true>
+operator*(const SparseMatrixBase<SparseDerived>& matrix, const Transpose<PermutationBase<PermDerived> >& tperm)
+{
+  return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, true>(tperm.nestedPermutation(), matrix.derived());
+}
+
+/** \returns the matrix with the inverse permutation applied to the rows.
+  */
+template<typename SparseDerived, typename PermDerived>
+inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, true>
+operator*(const Transpose<PermutationBase<PermDerived> >& tperm, const SparseMatrixBase<SparseDerived>& matrix)
+{
+  return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, true>(tperm.nestedPermutation(), matrix.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseProduct.h b/extern/Eigen3/Eigen/src/SparseCore/SparseProduct.h
new file mode 100644
index 00000000000..813dbf62427
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseProduct.h
@@ -0,0 +1,201 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEPRODUCT_H
+#define EIGEN_SPARSEPRODUCT_H
+
+namespace Eigen { 
+
+template<typename Lhs, typename Rhs>
+struct SparseSparseProductReturnType
+{
+  typedef typename internal::traits<Lhs>::Scalar Scalar;
+  enum {
+    LhsRowMajor = internal::traits<Lhs>::Flags & RowMajorBit,
+    RhsRowMajor = internal::traits<Rhs>::Flags & RowMajorBit,
+    TransposeRhs = (!LhsRowMajor) && RhsRowMajor,
+    TransposeLhs = LhsRowMajor && (!RhsRowMajor)
+  };
+
+  typedef typename internal::conditional<TransposeLhs,
+    SparseMatrix<Scalar,0>,
+    typename internal::nested<Lhs,Rhs::RowsAtCompileTime>::type>::type LhsNested;
+
+  typedef typename internal::conditional<TransposeRhs,
+    SparseMatrix<Scalar,0>,
+    typename internal::nested<Rhs,Lhs::RowsAtCompileTime>::type>::type RhsNested;
+
+  typedef SparseSparseProduct<LhsNested, RhsNested> Type;
+};
+
+namespace internal {
+template<typename LhsNested, typename RhsNested>
+struct traits<SparseSparseProduct<LhsNested, RhsNested> >
+{
+  typedef MatrixXpr XprKind;
+  // clean the nested types:
+  typedef typename remove_all<LhsNested>::type _LhsNested;
+  typedef typename remove_all<RhsNested>::type _RhsNested;
+  typedef typename _LhsNested::Scalar Scalar;
+  typedef typename promote_index_type<typename traits<_LhsNested>::Index,
+                                         typename traits<_RhsNested>::Index>::type Index;
+
+  enum {
+    LhsCoeffReadCost = _LhsNested::CoeffReadCost,
+    RhsCoeffReadCost = _RhsNested::CoeffReadCost,
+    LhsFlags = _LhsNested::Flags,
+    RhsFlags = _RhsNested::Flags,
+
+    RowsAtCompileTime    = _LhsNested::RowsAtCompileTime,
+    ColsAtCompileTime    = _RhsNested::ColsAtCompileTime,
+    MaxRowsAtCompileTime = _LhsNested::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = _RhsNested::MaxColsAtCompileTime,
+
+    InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(_LhsNested::ColsAtCompileTime, _RhsNested::RowsAtCompileTime),
+
+    EvalToRowMajor = (RhsFlags & LhsFlags & RowMajorBit),
+
+    RemovedBits = ~(EvalToRowMajor ? 0 : RowMajorBit),
+
+    Flags = (int(LhsFlags | RhsFlags) & HereditaryBits & RemovedBits)
+          | EvalBeforeAssigningBit
+          | EvalBeforeNestingBit,
+
+    CoeffReadCost = Dynamic
+  };
+
+  typedef Sparse StorageKind;
+};
+
+} // end namespace internal
+
+template<typename LhsNested, typename RhsNested>
+class SparseSparseProduct : internal::no_assignment_operator,
+  public SparseMatrixBase<SparseSparseProduct<LhsNested, RhsNested> >
+{
+  public:
+
+    typedef SparseMatrixBase<SparseSparseProduct> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(SparseSparseProduct)
+
+  private:
+
+    typedef typename internal::traits<SparseSparseProduct>::_LhsNested _LhsNested;
+    typedef typename internal::traits<SparseSparseProduct>::_RhsNested _RhsNested;
+
+  public:
+
+    template<typename Lhs, typename Rhs>
+    EIGEN_STRONG_INLINE SparseSparseProduct(const Lhs& lhs, const Rhs& rhs)
+      : m_lhs(lhs), m_rhs(rhs), m_tolerance(0), m_conservative(true)
+    {
+      init();
+    }
+
+    template<typename Lhs, typename Rhs>
+    EIGEN_STRONG_INLINE SparseSparseProduct(const Lhs& lhs, const Rhs& rhs, RealScalar tolerance)
+      : m_lhs(lhs), m_rhs(rhs), m_tolerance(tolerance), m_conservative(false)
+    {
+      init();
+    }
+
+    SparseSparseProduct pruned(Scalar reference = 0, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision()) const
+    {
+      return SparseSparseProduct(m_lhs,m_rhs,internal::abs(reference)*epsilon);
+    }
+
+    template<typename Dest>
+    void evalTo(Dest& result) const
+    {
+      if(m_conservative)
+        internal::conservative_sparse_sparse_product_selector<_LhsNested, _RhsNested, Dest>::run(lhs(),rhs(),result);
+      else
+        internal::sparse_sparse_product_with_pruning_selector<_LhsNested, _RhsNested, Dest>::run(lhs(),rhs(),result,m_tolerance);
+    }
+
+    EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); }
+
+    EIGEN_STRONG_INLINE const _LhsNested& lhs() const { return m_lhs; }
+    EIGEN_STRONG_INLINE const _RhsNested& rhs() const { return m_rhs; }
+
+  protected:
+    void init()
+    {
+      eigen_assert(m_lhs.cols() == m_rhs.rows());
+
+      enum {
+        ProductIsValid = _LhsNested::ColsAtCompileTime==Dynamic
+                      || _RhsNested::RowsAtCompileTime==Dynamic
+                      || int(_LhsNested::ColsAtCompileTime)==int(_RhsNested::RowsAtCompileTime),
+        AreVectors = _LhsNested::IsVectorAtCompileTime && _RhsNested::IsVectorAtCompileTime,
+        SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(_LhsNested,_RhsNested)
+      };
+      // note to the lost user:
+      //    * for a dot product use: v1.dot(v2)
+      //    * for a coeff-wise product use: v1.cwise()*v2
+      EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
+        INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
+      EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
+        INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
+      EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
+    }
+
+    LhsNested m_lhs;
+    RhsNested m_rhs;
+    RealScalar m_tolerance;
+    bool m_conservative;
+};
+
+// sparse = sparse * sparse
+template<typename Derived>
+template<typename Lhs, typename Rhs>
+inline Derived& SparseMatrixBase<Derived>::operator=(const SparseSparseProduct<Lhs,Rhs>& product)
+{
+  product.evalTo(derived());
+  return derived();
+}
+
+/** \returns an expression of the product of two sparse matrices.
+  * By default a conservative product preserving the symbolic non zeros is performed.
+  * The automatic pruning of the small values can be achieved by calling the pruned() function
+  * in which case a totally different product algorithm is employed:
+  * \code
+  * C = (A*B).pruned();             // supress numerical zeros (exact)
+  * C = (A*B).pruned(ref);
+  * C = (A*B).pruned(ref,epsilon);
+  * \endcode
+  * where \c ref is a meaningful non zero reference value.
+  * */
+template<typename Derived>
+template<typename OtherDerived>
+inline const typename SparseSparseProductReturnType<Derived,OtherDerived>::Type
+SparseMatrixBase<Derived>::operator*(const SparseMatrixBase<OtherDerived> &other) const
+{
+  return typename SparseSparseProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEPRODUCT_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseRedux.h b/extern/Eigen3/Eigen/src/SparseCore/SparseRedux.h
new file mode 100644
index 00000000000..73fb9a318fd
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseRedux.h
@@ -0,0 +1,60 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEREDUX_H
+#define EIGEN_SPARSEREDUX_H
+
+namespace Eigen { 
+
+template<typename Derived>
+typename internal::traits<Derived>::Scalar
+SparseMatrixBase<Derived>::sum() const
+{
+  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
+  Scalar res(0);
+  for (Index j=0; j<outerSize(); ++j)
+    for (typename Derived::InnerIterator iter(derived(),j); iter; ++iter)
+      res += iter.value();
+  return res;
+}
+
+template<typename _Scalar, int _Options, typename _Index>
+typename internal::traits<SparseMatrix<_Scalar,_Options,_Index> >::Scalar
+SparseMatrix<_Scalar,_Options,_Index>::sum() const
+{
+  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
+  return Matrix<Scalar,1,Dynamic>::Map(&m_data.value(0), m_data.size()).sum();
+}
+
+template<typename _Scalar, int _Options, typename _Index>
+typename internal::traits<SparseVector<_Scalar,_Options, _Index> >::Scalar
+SparseVector<_Scalar,_Options,_Index>::sum() const
+{
+  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
+  return Matrix<Scalar,1,Dynamic>::Map(&m_data.value(0), m_data.size()).sum();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEREDUX_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h b/extern/Eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h
new file mode 100644
index 00000000000..fc23b24d652
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h
@@ -0,0 +1,495 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_SELFADJOINTVIEW_H
+#define EIGEN_SPARSE_SELFADJOINTVIEW_H
+
+namespace Eigen { 
+
+/** \ingroup SparseCore_Module
+  * \class SparseSelfAdjointView
+  *
+  * \brief Pseudo expression to manipulate a triangular sparse matrix as a selfadjoint matrix.
+  *
+  * \param MatrixType the type of the dense matrix storing the coefficients
+  * \param UpLo can be either \c #Lower or \c #Upper
+  *
+  * This class is an expression of a sefladjoint matrix from a triangular part of a matrix
+  * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
+  * and most of the time this is the only way that it is used.
+  *
+  * \sa SparseMatrixBase::selfadjointView()
+  */
+template<typename Lhs, typename Rhs, int UpLo>
+class SparseSelfAdjointTimeDenseProduct;
+
+template<typename Lhs, typename Rhs, int UpLo>
+class DenseTimeSparseSelfAdjointProduct;
+
+namespace internal {
+  
+template<typename MatrixType, unsigned int UpLo>
+struct traits<SparseSelfAdjointView<MatrixType,UpLo> > : traits<MatrixType> {
+};
+
+template<int SrcUpLo,int DstUpLo,typename MatrixType,int DestOrder>
+void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm = 0);
+
+template<int UpLo,typename MatrixType,int DestOrder>
+void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm = 0);
+
+}
+
+template<typename MatrixType, unsigned int UpLo> class SparseSelfAdjointView
+  : public EigenBase<SparseSelfAdjointView<MatrixType,UpLo> >
+{
+  public:
+
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::Index Index;
+    typedef Matrix<Index,Dynamic,1> VectorI;
+    typedef typename MatrixType::Nested MatrixTypeNested;
+    typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
+
+    inline SparseSelfAdjointView(const MatrixType& matrix) : m_matrix(matrix)
+    {
+      eigen_assert(rows()==cols() && "SelfAdjointView is only for squared matrices");
+    }
+
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+
+    /** \internal \returns a reference to the nested matrix */
+    const _MatrixTypeNested& matrix() const { return m_matrix; }
+    _MatrixTypeNested& matrix() { return m_matrix.const_cast_derived(); }
+
+    /** Efficient sparse self-adjoint matrix times dense vector/matrix product */
+    template<typename OtherDerived>
+    SparseSelfAdjointTimeDenseProduct<MatrixType,OtherDerived,UpLo>
+    operator*(const MatrixBase<OtherDerived>& rhs) const
+    {
+      return SparseSelfAdjointTimeDenseProduct<MatrixType,OtherDerived,UpLo>(m_matrix, rhs.derived());
+    }
+
+    /** Efficient dense vector/matrix times sparse self-adjoint matrix product */
+    template<typename OtherDerived> friend
+    DenseTimeSparseSelfAdjointProduct<OtherDerived,MatrixType,UpLo>
+    operator*(const MatrixBase<OtherDerived>& lhs, const SparseSelfAdjointView& rhs)
+    {
+      return DenseTimeSparseSelfAdjointProduct<OtherDerived,_MatrixTypeNested,UpLo>(lhs.derived(), rhs.m_matrix);
+    }
+
+    /** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
+      * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
+      *
+      * \returns a reference to \c *this
+      *
+      * To perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
+      * call this function with u.adjoint().
+      */
+    template<typename DerivedU>
+    SparseSelfAdjointView& rankUpdate(const SparseMatrixBase<DerivedU>& u, Scalar alpha = Scalar(1));
+    
+    /** \internal triggered by sparse_matrix = SparseSelfadjointView; */
+    template<typename DestScalar,int StorageOrder> void evalTo(SparseMatrix<DestScalar,StorageOrder,Index>& _dest) const
+    {
+      internal::permute_symm_to_fullsymm<UpLo>(m_matrix, _dest);
+    }
+    
+    template<typename DestScalar> void evalTo(DynamicSparseMatrix<DestScalar,ColMajor,Index>& _dest) const
+    {
+      // TODO directly evaluate into _dest;
+      SparseMatrix<DestScalar,ColMajor,Index> tmp(_dest.rows(),_dest.cols());
+      internal::permute_symm_to_fullsymm<UpLo>(m_matrix, tmp);
+      _dest = tmp;
+    }
+    
+    /** \returns an expression of P^-1 H P */
+    SparseSymmetricPermutationProduct<_MatrixTypeNested,UpLo> twistedBy(const PermutationMatrix<Dynamic,Dynamic,Index>& perm) const
+    {
+      return SparseSymmetricPermutationProduct<_MatrixTypeNested,UpLo>(m_matrix, perm);
+    }
+    
+    template<typename SrcMatrixType,int SrcUpLo>
+    SparseSelfAdjointView& operator=(const SparseSymmetricPermutationProduct<SrcMatrixType,SrcUpLo>& permutedMatrix)
+    {
+      permutedMatrix.evalTo(*this);
+      return *this;
+    }
+
+
+    SparseSelfAdjointView& operator=(const SparseSelfAdjointView& src)
+    {
+      PermutationMatrix<Dynamic> pnull;
+      return *this = src.twistedBy(pnull);
+    }
+
+    template<typename SrcMatrixType,unsigned int SrcUpLo>
+    SparseSelfAdjointView& operator=(const SparseSelfAdjointView<SrcMatrixType,SrcUpLo>& src)
+    {
+      PermutationMatrix<Dynamic> pnull;
+      return *this = src.twistedBy(pnull);
+    }
+    
+
+    // const SparseLLT<PlainObject, UpLo> llt() const;
+    // const SparseLDLT<PlainObject, UpLo> ldlt() const;
+
+  protected:
+
+    typename MatrixType::Nested m_matrix;
+    mutable VectorI m_countPerRow;
+    mutable VectorI m_countPerCol;
+};
+
+/***************************************************************************
+* Implementation of SparseMatrixBase methods
+***************************************************************************/
+
+template<typename Derived>
+template<unsigned int UpLo>
+const SparseSelfAdjointView<Derived, UpLo> SparseMatrixBase<Derived>::selfadjointView() const
+{
+  return derived();
+}
+
+template<typename Derived>
+template<unsigned int UpLo>
+SparseSelfAdjointView<Derived, UpLo> SparseMatrixBase<Derived>::selfadjointView()
+{
+  return derived();
+}
+
+/***************************************************************************
+* Implementation of SparseSelfAdjointView methods
+***************************************************************************/
+
+template<typename MatrixType, unsigned int UpLo>
+template<typename DerivedU>
+SparseSelfAdjointView<MatrixType,UpLo>&
+SparseSelfAdjointView<MatrixType,UpLo>::rankUpdate(const SparseMatrixBase<DerivedU>& u, Scalar alpha)
+{
+  SparseMatrix<Scalar,MatrixType::Flags&RowMajorBit?RowMajor:ColMajor> tmp = u * u.adjoint();
+  if(alpha==Scalar(0))
+    m_matrix.const_cast_derived() = tmp.template triangularView<UpLo>();
+  else
+    m_matrix.const_cast_derived() += alpha * tmp.template triangularView<UpLo>();
+
+  return *this;
+}
+
+/***************************************************************************
+* Implementation of sparse self-adjoint time dense matrix
+***************************************************************************/
+
+namespace internal {
+template<typename Lhs, typename Rhs, int UpLo>
+struct traits<SparseSelfAdjointTimeDenseProduct<Lhs,Rhs,UpLo> >
+ : traits<ProductBase<SparseSelfAdjointTimeDenseProduct<Lhs,Rhs,UpLo>, Lhs, Rhs> >
+{
+  typedef Dense StorageKind;
+};
+}
+
+template<typename Lhs, typename Rhs, int UpLo>
+class SparseSelfAdjointTimeDenseProduct
+  : public ProductBase<SparseSelfAdjointTimeDenseProduct<Lhs,Rhs,UpLo>, Lhs, Rhs>
+{
+  public:
+    EIGEN_PRODUCT_PUBLIC_INTERFACE(SparseSelfAdjointTimeDenseProduct)
+
+    SparseSelfAdjointTimeDenseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
+    {}
+
+    template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
+    {
+      // TODO use alpha
+      eigen_assert(alpha==Scalar(1) && "alpha != 1 is not implemented yet, sorry");
+      typedef typename internal::remove_all<Lhs>::type _Lhs;
+      typedef typename internal::remove_all<Rhs>::type _Rhs;
+      typedef typename _Lhs::InnerIterator LhsInnerIterator;
+      enum {
+        LhsIsRowMajor = (_Lhs::Flags&RowMajorBit)==RowMajorBit,
+        ProcessFirstHalf =
+                 ((UpLo&(Upper|Lower))==(Upper|Lower))
+              || ( (UpLo&Upper) && !LhsIsRowMajor)
+              || ( (UpLo&Lower) && LhsIsRowMajor),
+        ProcessSecondHalf = !ProcessFirstHalf
+      };
+      for (Index j=0; j<m_lhs.outerSize(); ++j)
+      {
+        LhsInnerIterator i(m_lhs,j);
+        if (ProcessSecondHalf)
+        {
+          while (i && i.index()<j) ++i;
+          if(i && i.index()==j)
+          {
+            dest.row(j) += i.value() * m_rhs.row(j);
+            ++i;
+          }
+        }
+        for(; (ProcessFirstHalf ? i && i.index() < j : i) ; ++i)
+        {
+          Index a = LhsIsRowMajor ? j : i.index();
+          Index b = LhsIsRowMajor ? i.index() : j;
+          typename Lhs::Scalar v = i.value();
+          dest.row(a) += (v) * m_rhs.row(b);
+          dest.row(b) += internal::conj(v) * m_rhs.row(a);
+        }
+        if (ProcessFirstHalf && i && (i.index()==j))
+          dest.row(j) += i.value() * m_rhs.row(j);
+      }
+    }
+
+  private:
+    SparseSelfAdjointTimeDenseProduct& operator=(const SparseSelfAdjointTimeDenseProduct&);
+};
+
+namespace internal {
+template<typename Lhs, typename Rhs, int UpLo>
+struct traits<DenseTimeSparseSelfAdjointProduct<Lhs,Rhs,UpLo> >
+ : traits<ProductBase<DenseTimeSparseSelfAdjointProduct<Lhs,Rhs,UpLo>, Lhs, Rhs> >
+{};
+}
+
+template<typename Lhs, typename Rhs, int UpLo>
+class DenseTimeSparseSelfAdjointProduct
+  : public ProductBase<DenseTimeSparseSelfAdjointProduct<Lhs,Rhs,UpLo>, Lhs, Rhs>
+{
+  public:
+    EIGEN_PRODUCT_PUBLIC_INTERFACE(DenseTimeSparseSelfAdjointProduct)
+
+    DenseTimeSparseSelfAdjointProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
+    {}
+
+    template<typename Dest> void scaleAndAddTo(Dest& /*dest*/, Scalar /*alpha*/) const
+    {
+      // TODO
+    }
+
+  private:
+    DenseTimeSparseSelfAdjointProduct& operator=(const DenseTimeSparseSelfAdjointProduct&);
+};
+
+/***************************************************************************
+* Implementation of symmetric copies and permutations
+***************************************************************************/
+namespace internal {
+  
+template<typename MatrixType, int UpLo>
+struct traits<SparseSymmetricPermutationProduct<MatrixType,UpLo> > : traits<MatrixType> {
+};
+
+template<int UpLo,typename MatrixType,int DestOrder>
+void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm)
+{
+  typedef typename MatrixType::Index Index;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef SparseMatrix<Scalar,DestOrder,Index> Dest;
+  typedef Matrix<Index,Dynamic,1> VectorI;
+  
+  Dest& dest(_dest.derived());
+  enum {
+    StorageOrderMatch = int(Dest::IsRowMajor) == int(MatrixType::IsRowMajor)
+  };
+  
+  Index size = mat.rows();
+  VectorI count;
+  count.resize(size);
+  count.setZero();
+  dest.resize(size,size);
+  for(Index j = 0; j<size; ++j)
+  {
+    Index jp = perm ? perm[j] : j;
+    for(typename MatrixType::InnerIterator it(mat,j); it; ++it)
+    {
+      Index i = it.index();
+      Index r = it.row();
+      Index c = it.col();
+      Index ip = perm ? perm[i] : i;
+      if(UpLo==(Upper|Lower))
+        count[StorageOrderMatch ? jp : ip]++;
+      else if(r==c)
+        count[ip]++;
+      else if(( UpLo==Lower && r>c) || ( UpLo==Upper && r<c))
+      {
+        count[ip]++;
+        count[jp]++;
+      }
+    }
+  }
+  Index nnz = count.sum();
+  
+  // reserve space
+  dest.resizeNonZeros(nnz);
+  dest.outerIndexPtr()[0] = 0;
+  for(Index j=0; j<size; ++j)
+    dest.outerIndexPtr()[j+1] = dest.outerIndexPtr()[j] + count[j];
+  for(Index j=0; j<size; ++j)
+    count[j] = dest.outerIndexPtr()[j];
+  
+  // copy data
+  for(Index j = 0; j<size; ++j)
+  {
+    for(typename MatrixType::InnerIterator it(mat,j); it; ++it)
+    {
+      Index i = it.index();
+      Index r = it.row();
+      Index c = it.col();
+      
+      Index jp = perm ? perm[j] : j;
+      Index ip = perm ? perm[i] : i;
+      
+      if(UpLo==(Upper|Lower))
+      {
+        Index k = count[StorageOrderMatch ? jp : ip]++;
+        dest.innerIndexPtr()[k] = StorageOrderMatch ? ip : jp;
+        dest.valuePtr()[k] = it.value();
+      }
+      else if(r==c)
+      {
+        Index k = count[ip]++;
+        dest.innerIndexPtr()[k] = ip;
+        dest.valuePtr()[k] = it.value();
+      }
+      else if(( (UpLo&Lower)==Lower && r>c) || ( (UpLo&Upper)==Upper && r<c))
+      {
+        if(!StorageOrderMatch)
+          std::swap(ip,jp);
+        Index k = count[jp]++;
+        dest.innerIndexPtr()[k] = ip;
+        dest.valuePtr()[k] = it.value();
+        k = count[ip]++;
+        dest.innerIndexPtr()[k] = jp;
+        dest.valuePtr()[k] = internal::conj(it.value());
+      }
+    }
+  }
+}
+
+template<int _SrcUpLo,int _DstUpLo,typename MatrixType,int DstOrder>
+void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DstOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm)
+{
+  typedef typename MatrixType::Index Index;
+  typedef typename MatrixType::Scalar Scalar;
+  SparseMatrix<Scalar,DstOrder,Index>& dest(_dest.derived());
+  typedef Matrix<Index,Dynamic,1> VectorI;
+  enum {
+    SrcOrder = MatrixType::IsRowMajor ? RowMajor : ColMajor,
+    StorageOrderMatch = int(SrcOrder) == int(DstOrder),
+    DstUpLo = DstOrder==RowMajor ? (_DstUpLo==Upper ? Lower : Upper) : _DstUpLo,
+    SrcUpLo = SrcOrder==RowMajor ? (_SrcUpLo==Upper ? Lower : Upper) : _SrcUpLo
+  };
+  
+  Index size = mat.rows();
+  VectorI count(size);
+  count.setZero();
+  dest.resize(size,size);
+  for(Index j = 0; j<size; ++j)
+  {
+    Index jp = perm ? perm[j] : j;
+    for(typename MatrixType::InnerIterator it(mat,j); it; ++it)
+    {
+      Index i = it.index();
+      if((int(SrcUpLo)==int(Lower) && i<j) || (int(SrcUpLo)==int(Upper) && i>j))
+        continue;
+                  
+      Index ip = perm ? perm[i] : i;
+      count[int(DstUpLo)==int(Lower) ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
+    }
+  }
+  dest.outerIndexPtr()[0] = 0;
+  for(Index j=0; j<size; ++j)
+    dest.outerIndexPtr()[j+1] = dest.outerIndexPtr()[j] + count[j];
+  dest.resizeNonZeros(dest.outerIndexPtr()[size]);
+  for(Index j=0; j<size; ++j)
+    count[j] = dest.outerIndexPtr()[j];
+  
+  for(Index j = 0; j<size; ++j)
+  {
+    
+    for(typename MatrixType::InnerIterator it(mat,j); it; ++it)
+    {
+      Index i = it.index();
+      if((int(SrcUpLo)==int(Lower) && i<j) || (int(SrcUpLo)==int(Upper) && i>j))
+        continue;
+                  
+      Index jp = perm ? perm[j] : j;
+      Index ip = perm? perm[i] : i;
+      
+      Index k = count[int(DstUpLo)==int(Lower) ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
+      dest.innerIndexPtr()[k] = int(DstUpLo)==int(Lower) ? (std::max)(ip,jp) : (std::min)(ip,jp);
+      
+      if(!StorageOrderMatch) std::swap(ip,jp);
+      if( ((int(DstUpLo)==int(Lower) && ip<jp) || (int(DstUpLo)==int(Upper) && ip>jp)))
+        dest.valuePtr()[k] = conj(it.value());
+      else
+        dest.valuePtr()[k] = it.value();
+    }
+  }
+}
+
+}
+
+template<typename MatrixType,int UpLo>
+class SparseSymmetricPermutationProduct
+  : public EigenBase<SparseSymmetricPermutationProduct<MatrixType,UpLo> >
+{
+  public:
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::Index Index;
+  protected:
+    typedef PermutationMatrix<Dynamic,Dynamic,Index> Perm;
+  public:
+    typedef Matrix<Index,Dynamic,1> VectorI;
+    typedef typename MatrixType::Nested MatrixTypeNested;
+    typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
+    
+    SparseSymmetricPermutationProduct(const MatrixType& mat, const Perm& perm)
+      : m_matrix(mat), m_perm(perm)
+    {}
+    
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+    
+    template<typename DestScalar, int Options, typename DstIndex>
+    void evalTo(SparseMatrix<DestScalar,Options,DstIndex>& _dest) const
+    {
+      internal::permute_symm_to_fullsymm<UpLo>(m_matrix,_dest,m_perm.indices().data());
+    }
+    
+    template<typename DestType,unsigned int DestUpLo> void evalTo(SparseSelfAdjointView<DestType,DestUpLo>& dest) const
+    {
+      internal::permute_symm_to_symm<UpLo,DestUpLo>(m_matrix,dest.matrix(),m_perm.indices().data());
+    }
+    
+  protected:
+    MatrixTypeNested m_matrix;
+    const Perm& m_perm;
+
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h b/extern/Eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
new file mode 100644
index 00000000000..abd4fda829c
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
@@ -0,0 +1,164 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
+#define EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
+
+namespace Eigen { 
+
+namespace internal {
+
+
+// perform a pseudo in-place sparse * sparse product assuming all matrices are col major
+template<typename Lhs, typename Rhs, typename ResultType>
+static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res, typename ResultType::RealScalar tolerance)
+{
+  // return sparse_sparse_product_with_pruning_impl2(lhs,rhs,res);
+
+  typedef typename remove_all<Lhs>::type::Scalar Scalar;
+  typedef typename remove_all<Lhs>::type::Index Index;
+
+  // make sure to call innerSize/outerSize since we fake the storage order.
+  Index rows = lhs.innerSize();
+  Index cols = rhs.outerSize();
+  //int size = lhs.outerSize();
+  eigen_assert(lhs.outerSize() == rhs.innerSize());
+
+  // allocate a temporary buffer
+  AmbiVector<Scalar,Index> tempVector(rows);
+
+  // estimate the number of non zero entries
+  // given a rhs column containing Y non zeros, we assume that the respective Y columns
+  // of the lhs differs in average of one non zeros, thus the number of non zeros for
+  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
+  // per column of the lhs.
+  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
+  Index estimated_nnz_prod = lhs.nonZeros() + rhs.nonZeros();
+
+  // mimics a resizeByInnerOuter:
+  if(ResultType::IsRowMajor)
+    res.resize(cols, rows);
+  else
+    res.resize(rows, cols);
+
+  res.reserve(estimated_nnz_prod);
+  double ratioColRes = double(estimated_nnz_prod)/double(lhs.rows()*rhs.cols());
+  for (Index j=0; j<cols; ++j)
+  {
+    // FIXME:
+    //double ratioColRes = (double(rhs.innerVector(j).nonZeros()) + double(lhs.nonZeros())/double(lhs.cols()))/double(lhs.rows());
+    // let's do a more accurate determination of the nnz ratio for the current column j of res
+    tempVector.init(ratioColRes);
+    tempVector.setZero();
+    for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
+    {
+      // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
+      tempVector.restart();
+      Scalar x = rhsIt.value();
+      for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
+      {
+        tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
+      }
+    }
+    res.startVec(j);
+    for (typename AmbiVector<Scalar,Index>::Iterator it(tempVector,tolerance); it; ++it)
+      res.insertBackByOuterInner(j,it.index()) = it.value();
+  }
+  res.finalize();
+}
+
+template<typename Lhs, typename Rhs, typename ResultType,
+  int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
+  int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
+  int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
+struct sparse_sparse_product_with_pruning_selector;
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
+{
+  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
+  typedef typename ResultType::RealScalar RealScalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, RealScalar tolerance)
+  {
+    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res, tolerance);
+    res.swap(_res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
+{
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, RealScalar tolerance)
+  {
+    // we need a col-major matrix to hold the result
+    typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
+    SparseTemporaryType _res(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res, tolerance);
+    res = _res;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
+{
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, RealScalar tolerance)
+  {
+    // let's transpose the product to get a column x column product
+    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res, tolerance);
+    res.swap(_res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
+{
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, RealScalar tolerance)
+  {
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+    ColMajorMatrix colLhs(lhs);
+    ColMajorMatrix colRhs(rhs);
+    internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrix,ColMajorMatrix,ResultType>(colLhs, colRhs, res, tolerance);
+
+    // let's transpose the product to get a column x column product
+//     typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
+//     SparseTemporaryType _res(res.cols(), res.rows());
+//     sparse_sparse_product_with_pruning_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
+//     res = _res.transpose();
+  }
+};
+
+// NOTE the 2 others cases (col row *) must never occur since they are caught
+// by ProductReturnType which transforms it to (col col *) by evaluating rhs.
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseTranspose.h b/extern/Eigen3/Eigen/src/SparseCore/SparseTranspose.h
new file mode 100644
index 00000000000..07d9e0bbd08
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseTranspose.h
@@ -0,0 +1,76 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSETRANSPOSE_H
+#define EIGEN_SPARSETRANSPOSE_H
+
+namespace Eigen { 
+
+template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>
+  : public SparseMatrixBase<Transpose<MatrixType> >
+{
+    typedef typename internal::remove_all<typename MatrixType::Nested>::type _MatrixTypeNested;
+  public:
+
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Transpose<MatrixType>)
+
+    class InnerIterator;
+    class ReverseInnerIterator;
+
+    inline Index nonZeros() const { return derived().nestedExpression().nonZeros(); }
+};
+
+// NOTE: VC10 trigger an ICE if don't put typename TransposeImpl<MatrixType,Sparse>:: in front of Index,
+// a typedef typename TransposeImpl<MatrixType,Sparse>::Index Index;
+// does not fix the issue.
+// An alternative is to define the nested class in the parent class itself.
+template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>::InnerIterator
+  : public _MatrixTypeNested::InnerIterator
+{
+    typedef typename _MatrixTypeNested::InnerIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const TransposeImpl& trans, typename TransposeImpl<MatrixType,Sparse>::Index outer)
+      : Base(trans.derived().nestedExpression(), outer)
+    {}
+    inline typename TransposeImpl<MatrixType,Sparse>::Index row() const { return Base::col(); }
+    inline typename TransposeImpl<MatrixType,Sparse>::Index col() const { return Base::row(); }
+};
+
+template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>::ReverseInnerIterator
+  : public _MatrixTypeNested::ReverseInnerIterator
+{
+    typedef typename _MatrixTypeNested::ReverseInnerIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE ReverseInnerIterator(const TransposeImpl& xpr, typename TransposeImpl<MatrixType,Sparse>::Index outer)
+      : Base(xpr.derived().nestedExpression(), outer)
+    {}
+    inline typename TransposeImpl<MatrixType,Sparse>::Index row() const { return Base::col(); }
+    inline typename TransposeImpl<MatrixType,Sparse>::Index col() const { return Base::row(); }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSETRANSPOSE_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseTriangularView.h b/extern/Eigen3/Eigen/src/SparseCore/SparseTriangularView.h
new file mode 100644
index 00000000000..59aab575684
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseTriangularView.h
@@ -0,0 +1,179 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_TRIANGULARVIEW_H
+#define EIGEN_SPARSE_TRIANGULARVIEW_H
+
+namespace Eigen { 
+
+namespace internal {
+  
+template<typename MatrixType, int Mode>
+struct traits<SparseTriangularView<MatrixType,Mode> >
+: public traits<MatrixType>
+{};
+
+} // namespace internal
+
+template<typename MatrixType, int Mode> class SparseTriangularView
+  : public SparseMatrixBase<SparseTriangularView<MatrixType,Mode> >
+{
+    enum { SkipFirst = ((Mode&Lower) && !(MatrixType::Flags&RowMajorBit))
+                    || ((Mode&Upper) &&  (MatrixType::Flags&RowMajorBit)),
+           SkipLast = !SkipFirst,
+           HasUnitDiag = (Mode&UnitDiag) ? 1 : 0
+    };
+
+  public:
+    
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseTriangularView)
+
+    class InnerIterator;
+    class ReverseInnerIterator;
+
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+
+    typedef typename MatrixType::Nested MatrixTypeNested;
+    typedef typename internal::remove_reference<MatrixTypeNested>::type MatrixTypeNestedNonRef;
+    typedef typename internal::remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned;
+
+    inline SparseTriangularView(const MatrixType& matrix) : m_matrix(matrix) {}
+
+    /** \internal */
+    inline const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; }
+
+    template<typename OtherDerived>
+    typename internal::plain_matrix_type_column_major<OtherDerived>::type
+    solve(const MatrixBase<OtherDerived>& other) const;
+
+    template<typename OtherDerived> void solveInPlace(MatrixBase<OtherDerived>& other) const;
+    template<typename OtherDerived> void solveInPlace(SparseMatrixBase<OtherDerived>& other) const;
+
+  protected:
+    MatrixTypeNested m_matrix;
+};
+
+template<typename MatrixType, int Mode>
+class SparseTriangularView<MatrixType,Mode>::InnerIterator : public MatrixTypeNestedCleaned::InnerIterator
+{
+    typedef typename MatrixTypeNestedCleaned::InnerIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const SparseTriangularView& view, Index outer)
+      : Base(view.nestedExpression(), outer), m_returnOne(false)
+    {
+      if(SkipFirst)
+      {
+        while((*this) && (HasUnitDiag ? this->index()<=outer : this->index()<outer))
+          Base::operator++();
+        if(HasUnitDiag)
+          m_returnOne = true;
+      }
+      else if(HasUnitDiag && ((!Base::operator bool()) || Base::index()>=Base::outer()))
+      {
+        if((!SkipFirst) && Base::operator bool())
+          Base::operator++();
+        m_returnOne = true;
+      }
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    {
+      if(HasUnitDiag && m_returnOne)
+        m_returnOne = false;
+      else
+      {
+        Base::operator++();
+        if(HasUnitDiag && (!SkipFirst) && ((!Base::operator bool()) || Base::index()>=Base::outer()))
+        {
+          if((!SkipFirst) && Base::operator bool())
+            Base::operator++();
+          m_returnOne = true;
+        }
+      }
+      return *this;
+    }
+
+    inline Index row() const { return Base::row(); }
+    inline Index col() const { return Base::col(); }
+    inline Index index() const
+    {
+      if(HasUnitDiag && m_returnOne)  return Base::outer();
+      else                            return Base::index();
+    }
+    inline Scalar value() const
+    {
+      if(HasUnitDiag && m_returnOne)  return Scalar(1);
+      else                            return Base::value();
+    }
+
+    EIGEN_STRONG_INLINE operator bool() const
+    {
+      if(HasUnitDiag && m_returnOne)
+        return true;
+      return (SkipFirst ? Base::operator bool() : (Base::operator bool() && this->index() <= this->outer()));
+    }
+  protected:
+    bool m_returnOne;
+};
+
+template<typename MatrixType, int Mode>
+class SparseTriangularView<MatrixType,Mode>::ReverseInnerIterator : public MatrixTypeNestedCleaned::ReverseInnerIterator
+{
+    typedef typename MatrixTypeNestedCleaned::ReverseInnerIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE ReverseInnerIterator(const SparseTriangularView& view, Index outer)
+      : Base(view.nestedExpression(), outer)
+    {
+      eigen_assert((!HasUnitDiag) && "ReverseInnerIterator does not support yet triangular views with a unit diagonal");
+      if(SkipLast)
+        while((*this) && this->index()>outer)
+          --(*this);
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator--()
+    { Base::operator--(); return *this; }
+
+    inline Index row() const { return Base::row(); }
+    inline Index col() const { return Base::col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const
+    {
+      return SkipLast ? Base::operator bool() : (Base::operator bool() && this->index() >= this->outer());
+    }
+};
+
+template<typename Derived>
+template<int Mode>
+inline const SparseTriangularView<Derived, Mode>
+SparseMatrixBase<Derived>::triangularView() const
+{
+  return derived();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_TRIANGULARVIEW_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseUtil.h b/extern/Eigen3/Eigen/src/SparseCore/SparseUtil.h
new file mode 100644
index 00000000000..050b65253a8
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseUtil.h
@@ -0,0 +1,188 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEUTIL_H
+#define EIGEN_SPARSEUTIL_H
+
+namespace Eigen { 
+
+#ifdef NDEBUG
+#define EIGEN_DBG_SPARSE(X)
+#else
+#define EIGEN_DBG_SPARSE(X) X
+#endif
+
+#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, Op) \
+template<typename OtherDerived> \
+EIGEN_STRONG_INLINE Derived& operator Op(const Eigen::SparseMatrixBase<OtherDerived>& other) \
+{ \
+  return Base::operator Op(other.derived()); \
+} \
+EIGEN_STRONG_INLINE Derived& operator Op(const Derived& other) \
+{ \
+  return Base::operator Op(other); \
+}
+
+#define EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, Op) \
+template<typename Other> \
+EIGEN_STRONG_INLINE Derived& operator Op(const Other& scalar) \
+{ \
+  return Base::operator Op(scalar); \
+}
+
+#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATORS(Derived) \
+EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =) \
+EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, +=) \
+EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, -=) \
+EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, *=) \
+EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, /=)
+
+#define _EIGEN_SPARSE_PUBLIC_INTERFACE(Derived, BaseClass) \
+  typedef BaseClass Base; \
+  typedef typename Eigen::internal::traits<Derived >::Scalar Scalar; \
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; \
+  typedef typename Eigen::internal::nested<Derived >::type Nested; \
+  typedef typename Eigen::internal::traits<Derived >::StorageKind StorageKind; \
+  typedef typename Eigen::internal::traits<Derived >::Index Index; \
+  enum { RowsAtCompileTime = Eigen::internal::traits<Derived >::RowsAtCompileTime, \
+        ColsAtCompileTime = Eigen::internal::traits<Derived >::ColsAtCompileTime, \
+        Flags = Eigen::internal::traits<Derived >::Flags, \
+        CoeffReadCost = Eigen::internal::traits<Derived >::CoeffReadCost, \
+        SizeAtCompileTime = Base::SizeAtCompileTime, \
+        IsVectorAtCompileTime = Base::IsVectorAtCompileTime }; \
+  using Base::derived; \
+  using Base::const_cast_derived;
+
+#define EIGEN_SPARSE_PUBLIC_INTERFACE(Derived) \
+  _EIGEN_SPARSE_PUBLIC_INTERFACE(Derived, Eigen::SparseMatrixBase<Derived >)
+
+const int CoherentAccessPattern     = 0x1;
+const int InnerRandomAccessPattern  = 0x2 | CoherentAccessPattern;
+const int OuterRandomAccessPattern  = 0x4 | CoherentAccessPattern;
+const int RandomAccessPattern       = 0x8 | OuterRandomAccessPattern | InnerRandomAccessPattern;
+
+template<typename Derived> class SparseMatrixBase;
+template<typename _Scalar, int _Flags = 0, typename _Index = int>  class SparseMatrix;
+template<typename _Scalar, int _Flags = 0, typename _Index = int>  class DynamicSparseMatrix;
+template<typename _Scalar, int _Flags = 0, typename _Index = int>  class SparseVector;
+template<typename _Scalar, int _Flags = 0, typename _Index = int>  class MappedSparseMatrix;
+
+template<typename MatrixType, int Size>           class SparseInnerVectorSet;
+template<typename MatrixType, int Mode>           class SparseTriangularView;
+template<typename MatrixType, unsigned int UpLo>  class SparseSelfAdjointView;
+template<typename Lhs, typename Rhs>              class SparseDiagonalProduct;
+template<typename MatrixType> class SparseView;
+
+template<typename Lhs, typename Rhs>        class SparseSparseProduct;
+template<typename Lhs, typename Rhs>        class SparseTimeDenseProduct;
+template<typename Lhs, typename Rhs>        class DenseTimeSparseProduct;
+template<typename Lhs, typename Rhs, bool Transpose> class SparseDenseOuterProduct;
+
+template<typename Lhs, typename Rhs> struct SparseSparseProductReturnType;
+template<typename Lhs, typename Rhs, int InnerSize = internal::traits<Lhs>::ColsAtCompileTime> struct DenseSparseProductReturnType;
+template<typename Lhs, typename Rhs, int InnerSize = internal::traits<Lhs>::ColsAtCompileTime> struct SparseDenseProductReturnType;
+template<typename MatrixType,int UpLo> class SparseSymmetricPermutationProduct;
+
+namespace internal {
+
+template<typename T,int Rows,int Cols> struct sparse_eval;
+
+template<typename T> struct eval<T,Sparse>
+  : public sparse_eval<T, traits<T>::RowsAtCompileTime,traits<T>::ColsAtCompileTime>
+{};
+
+template<typename T,int Cols> struct sparse_eval<T,1,Cols> {
+    typedef typename traits<T>::Scalar _Scalar;
+    enum { _Flags = traits<T>::Flags| RowMajorBit };
+  public:
+    typedef SparseVector<_Scalar, _Flags> type;
+};
+
+template<typename T,int Rows> struct sparse_eval<T,Rows,1> {
+    typedef typename traits<T>::Scalar _Scalar;
+    enum { _Flags = traits<T>::Flags & (~RowMajorBit) };
+  public:
+    typedef SparseVector<_Scalar, _Flags> type;
+};
+
+template<typename T,int Rows,int Cols> struct sparse_eval {
+    typedef typename traits<T>::Scalar _Scalar;
+    enum { _Flags = traits<T>::Flags };
+  public:
+    typedef SparseMatrix<_Scalar, _Flags> type;
+};
+
+template<typename T> struct sparse_eval<T,1,1> {
+    typedef typename traits<T>::Scalar _Scalar;
+  public:
+    typedef Matrix<_Scalar, 1, 1> type;
+};
+
+template<typename T> struct plain_matrix_type<T,Sparse>
+{
+  typedef typename traits<T>::Scalar _Scalar;
+    enum {
+          _Flags = traits<T>::Flags
+    };
+
+  public:
+    typedef SparseMatrix<_Scalar, _Flags> type;
+};
+
+} // end namespace internal
+
+/** \ingroup SparseCore_Module
+  *
+  * \class Triplet
+  *
+  * \brief A small structure to hold a non zero as a triplet (i,j,value).
+  *
+  * \sa SparseMatrix::setFromTriplets()
+  */
+template<typename Scalar, typename Index=unsigned int>
+class Triplet
+{
+public:
+  Triplet() : m_row(0), m_col(0), m_value(0) {}
+
+  Triplet(const Index& i, const Index& j, const Scalar& v = Scalar(0))
+    : m_row(i), m_col(j), m_value(v)
+  {}
+
+  /** \returns the row index of the element */
+  const Index& row() const { return m_row; }
+
+  /** \returns the column index of the element */
+  const Index& col() const { return m_col; }
+
+  /** \returns the value of the element */
+  const Scalar& value() const { return m_value; }
+protected:
+  Index m_row, m_col;
+  Scalar m_value;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEUTIL_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseVector.h b/extern/Eigen3/Eigen/src/SparseCore/SparseVector.h
new file mode 100644
index 00000000000..e81347705b2
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseVector.h
@@ -0,0 +1,413 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEVECTOR_H
+#define EIGEN_SPARSEVECTOR_H
+
+namespace Eigen { 
+
+/** \ingroup SparseCore_Module
+  * \class SparseVector
+  *
+  * \brief a sparse vector class
+  *
+  * \tparam _Scalar the scalar type, i.e. the type of the coefficients
+  *
+  * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_SPARSEVECTOR_PLUGIN.
+  */
+
+namespace internal {
+template<typename _Scalar, int _Options, typename _Index>
+struct traits<SparseVector<_Scalar, _Options, _Index> >
+{
+  typedef _Scalar Scalar;
+  typedef _Index Index;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    IsColVector = (_Options & RowMajorBit) ? 0 : 1,
+
+    RowsAtCompileTime = IsColVector ? Dynamic : 1,
+    ColsAtCompileTime = IsColVector ? 1 : Dynamic,
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    Flags = _Options | NestByRefBit | LvalueBit | (IsColVector ? 0 : RowMajorBit),
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    SupportedAccessPatterns = InnerRandomAccessPattern
+  };
+};
+}
+
+template<typename _Scalar, int _Options, typename _Index>
+class SparseVector
+  : public SparseMatrixBase<SparseVector<_Scalar, _Options, _Index> >
+{
+  public:
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseVector)
+    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, +=)
+    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, -=)
+
+  protected:
+  public:
+
+    typedef SparseMatrixBase<SparseVector> SparseBase;
+    enum { IsColVector = internal::traits<SparseVector>::IsColVector };
+    
+    enum {
+      Options = _Options
+    };
+
+    internal::CompressedStorage<Scalar,Index> m_data;
+    Index m_size;
+
+    internal::CompressedStorage<Scalar,Index>& _data() { return m_data; }
+    internal::CompressedStorage<Scalar,Index>& _data() const { return m_data; }
+
+  public:
+
+    EIGEN_STRONG_INLINE Index rows() const { return IsColVector ? m_size : 1; }
+    EIGEN_STRONG_INLINE Index cols() const { return IsColVector ? 1 : m_size; }
+    EIGEN_STRONG_INLINE Index innerSize() const { return m_size; }
+    EIGEN_STRONG_INLINE Index outerSize() const { return 1; }
+
+    EIGEN_STRONG_INLINE const Scalar* valuePtr() const { return &m_data.value(0); }
+    EIGEN_STRONG_INLINE Scalar* valuePtr() { return &m_data.value(0); }
+
+    EIGEN_STRONG_INLINE const Index* innerIndexPtr() const { return &m_data.index(0); }
+    EIGEN_STRONG_INLINE Index* innerIndexPtr() { return &m_data.index(0); }
+
+    inline Scalar coeff(Index row, Index col) const
+    {
+      eigen_assert((IsColVector ? col : row)==0);
+      return coeff(IsColVector ? row : col);
+    }
+    inline Scalar coeff(Index i) const { return m_data.at(i); }
+
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      eigen_assert((IsColVector ? col : row)==0);
+      return coeff(IsColVector ? row : col);
+    }
+
+    /** \returns a reference to the coefficient value at given index \a i
+      * This operation involes a log(rho*size) binary search. If the coefficient does not
+      * exist yet, then a sorted insertion into a sequential buffer is performed.
+      *
+      * This insertion might be very costly if the number of nonzeros above \a i is large.
+      */
+    inline Scalar& coeffRef(Index i)
+    {
+      return m_data.atWithInsertion(i);
+    }
+
+  public:
+
+    class InnerIterator;
+    class ReverseInnerIterator;
+
+    inline void setZero() { m_data.clear(); }
+
+    /** \returns the number of non zero coefficients */
+    inline Index nonZeros() const  { return static_cast<Index>(m_data.size()); }
+
+    inline void startVec(Index outer)
+    {
+      EIGEN_UNUSED_VARIABLE(outer);
+      eigen_assert(outer==0);
+    }
+
+    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
+    {
+      EIGEN_UNUSED_VARIABLE(outer);
+      eigen_assert(outer==0);
+      return insertBack(inner);
+    }
+    inline Scalar& insertBack(Index i)
+    {
+      m_data.append(0, i);
+      return m_data.value(m_data.size()-1);
+    }
+
+    inline Scalar& insert(Index row, Index col)
+    {
+      Index inner = IsColVector ? row : col;
+      Index outer = IsColVector ? col : row;
+      eigen_assert(outer==0);
+      return insert(inner);
+    }
+    Scalar& insert(Index i)
+    {
+      Index startId = 0;
+      Index p = Index(m_data.size()) - 1;
+      // TODO smart realloc
+      m_data.resize(p+2,1);
+
+      while ( (p >= startId) && (m_data.index(p) > i) )
+      {
+        m_data.index(p+1) = m_data.index(p);
+        m_data.value(p+1) = m_data.value(p);
+        --p;
+      }
+      m_data.index(p+1) = i;
+      m_data.value(p+1) = 0;
+      return m_data.value(p+1);
+    }
+
+    /**
+      */
+    inline void reserve(Index reserveSize) { m_data.reserve(reserveSize); }
+
+
+    inline void finalize() {}
+
+    void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision())
+    {
+      m_data.prune(reference,epsilon);
+    }
+
+    void resize(Index rows, Index cols)
+    {
+      eigen_assert(rows==1 || cols==1);
+      resize(IsColVector ? rows : cols);
+    }
+
+    void resize(Index newSize)
+    {
+      m_size = newSize;
+      m_data.clear();
+    }
+
+    void resizeNonZeros(Index size) { m_data.resize(size); }
+
+    inline SparseVector() : m_size(0) { resize(0); }
+
+    inline SparseVector(Index size) : m_size(0) { resize(size); }
+
+    inline SparseVector(Index rows, Index cols) : m_size(0) { resize(rows,cols); }
+
+    template<typename OtherDerived>
+    inline SparseVector(const SparseMatrixBase<OtherDerived>& other)
+      : m_size(0)
+    {
+      *this = other.derived();
+    }
+
+    inline SparseVector(const SparseVector& other)
+      : m_size(0)
+    {
+      *this = other.derived();
+    }
+
+    inline void swap(SparseVector& other)
+    {
+      std::swap(m_size, other.m_size);
+      m_data.swap(other.m_data);
+    }
+
+    inline SparseVector& operator=(const SparseVector& other)
+    {
+      if (other.isRValue())
+      {
+        swap(other.const_cast_derived());
+      }
+      else
+      {
+        resize(other.size());
+        m_data = other.m_data;
+      }
+      return *this;
+    }
+
+    template<typename OtherDerived>
+    inline SparseVector& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+      if (int(RowsAtCompileTime)!=int(OtherDerived::RowsAtCompileTime))
+        return assign(other.transpose());
+      else
+        return assign(other);
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename Lhs, typename Rhs>
+    inline SparseVector& operator=(const SparseSparseProduct<Lhs,Rhs>& product)
+    {
+      return Base::operator=(product);
+    }
+    #endif
+
+    friend std::ostream & operator << (std::ostream & s, const SparseVector& m)
+    {
+      for (Index i=0; i<m.nonZeros(); ++i)
+        s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
+      s << std::endl;
+      return s;
+    }
+
+    /** Destructor */
+    inline ~SparseVector() {}
+
+    /** Overloaded for performance */
+    Scalar sum() const;
+
+  public:
+
+    /** \deprecated use setZero() and reserve() */
+    EIGEN_DEPRECATED void startFill(Index reserve)
+    {
+      setZero();
+      m_data.reserve(reserve);
+    }
+
+    /** \deprecated use insertBack(Index,Index) */
+    EIGEN_DEPRECATED Scalar& fill(Index r, Index c)
+    {
+      eigen_assert(r==0 || c==0);
+      return fill(IsColVector ? r : c);
+    }
+
+    /** \deprecated use insertBack(Index) */
+    EIGEN_DEPRECATED Scalar& fill(Index i)
+    {
+      m_data.append(0, i);
+      return m_data.value(m_data.size()-1);
+    }
+
+    /** \deprecated use insert(Index,Index) */
+    EIGEN_DEPRECATED Scalar& fillrand(Index r, Index c)
+    {
+      eigen_assert(r==0 || c==0);
+      return fillrand(IsColVector ? r : c);
+    }
+
+    /** \deprecated use insert(Index) */
+    EIGEN_DEPRECATED Scalar& fillrand(Index i)
+    {
+      return insert(i);
+    }
+
+    /** \deprecated use finalize() */
+    EIGEN_DEPRECATED void endFill() {}
+    
+#   ifdef EIGEN_SPARSEVECTOR_PLUGIN
+#     include EIGEN_SPARSEVECTOR_PLUGIN
+#   endif
+
+protected:
+    template<typename OtherDerived>
+    EIGEN_DONT_INLINE SparseVector& assign(const SparseMatrixBase<OtherDerived>& _other)
+    {
+      const OtherDerived& other(_other.derived());
+      const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
+      if(needToTranspose)
+      {
+        Index size = other.size();
+        Index nnz = other.nonZeros();
+        resize(size);
+        reserve(nnz);
+        for(Index i=0; i<size; ++i)
+        {
+          typename OtherDerived::InnerIterator it(other, i);
+          if(it)
+              insert(i) = it.value();
+        }
+        return *this;
+      }
+      else
+      {
+        // there is no special optimization
+        return Base::operator=(other);
+      }
+    }
+};
+
+template<typename Scalar, int _Options, typename _Index>
+class SparseVector<Scalar,_Options,_Index>::InnerIterator
+{
+  public:
+    InnerIterator(const SparseVector& vec, Index outer=0)
+      : m_data(vec.m_data), m_id(0), m_end(static_cast<Index>(m_data.size()))
+    {
+      EIGEN_UNUSED_VARIABLE(outer);
+      eigen_assert(outer==0);
+    }
+
+    InnerIterator(const internal::CompressedStorage<Scalar,Index>& data)
+      : m_data(data), m_id(0), m_end(static_cast<Index>(m_data.size()))
+    {}
+
+    inline InnerIterator& operator++() { m_id++; return *this; }
+
+    inline Scalar value() const { return m_data.value(m_id); }
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_data.value(m_id)); }
+
+    inline Index index() const { return m_data.index(m_id); }
+    inline Index row() const { return IsColVector ? index() : 0; }
+    inline Index col() const { return IsColVector ? 0 : index(); }
+
+    inline operator bool() const { return (m_id < m_end); }
+
+  protected:
+    const internal::CompressedStorage<Scalar,Index>& m_data;
+    Index m_id;
+    const Index m_end;
+};
+
+template<typename Scalar, int _Options, typename _Index>
+class SparseVector<Scalar,_Options,_Index>::ReverseInnerIterator
+{
+  public:
+    ReverseInnerIterator(const SparseVector& vec, Index outer=0)
+      : m_data(vec.m_data), m_id(static_cast<Index>(m_data.size())), m_start(0)
+    {
+      EIGEN_UNUSED_VARIABLE(outer);
+      eigen_assert(outer==0);
+    }
+
+    ReverseInnerIterator(const internal::CompressedStorage<Scalar,Index>& data)
+      : m_data(data), m_id(static_cast<Index>(m_data.size())), m_start(0)
+    {}
+
+    inline ReverseInnerIterator& operator--() { m_id--; return *this; }
+
+    inline Scalar value() const { return m_data.value(m_id-1); }
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_data.value(m_id-1)); }
+
+    inline Index index() const { return m_data.index(m_id-1); }
+    inline Index row() const { return IsColVector ? index() : 0; }
+    inline Index col() const { return IsColVector ? 0 : index(); }
+
+    inline operator bool() const { return (m_id > m_start); }
+
+  protected:
+    const internal::CompressedStorage<Scalar,Index>& m_data;
+    Index m_id;
+    const Index m_start;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEVECTOR_H
diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseView.h b/extern/Eigen3/Eigen/src/SparseCore/SparseView.h
new file mode 100644
index 00000000000..43a3adb240e
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/SparseView.h
@@ -0,0 +1,113 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Daniel Lowengrub <lowdanie@gmail.com>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSEVIEW_H
+#define EIGEN_SPARSEVIEW_H
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename MatrixType>
+struct traits<SparseView<MatrixType> > : traits<MatrixType>
+{
+  typedef int Index;
+  typedef Sparse StorageKind;
+  enum {
+    Flags = int(traits<MatrixType>::Flags) & (RowMajorBit)
+  };
+};
+
+} // end namespace internal
+
+template<typename MatrixType>
+class SparseView : public SparseMatrixBase<SparseView<MatrixType> >
+{
+  typedef typename MatrixType::Nested MatrixTypeNested;
+  typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
+public:
+  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseView)
+
+  SparseView(const MatrixType& mat, const Scalar& m_reference = Scalar(0),
+             typename NumTraits<Scalar>::Real m_epsilon = NumTraits<Scalar>::dummy_precision()) : 
+    m_matrix(mat), m_reference(m_reference), m_epsilon(m_epsilon) {}
+
+  class InnerIterator;
+
+  inline Index rows() const { return m_matrix.rows(); }
+  inline Index cols() const { return m_matrix.cols(); }
+
+  inline Index innerSize() const { return m_matrix.innerSize(); }
+  inline Index outerSize() const { return m_matrix.outerSize(); }
+
+protected:
+  MatrixTypeNested m_matrix;
+  Scalar m_reference;
+  typename NumTraits<Scalar>::Real m_epsilon;
+};
+
+template<typename MatrixType>
+class SparseView<MatrixType>::InnerIterator : public _MatrixTypeNested::InnerIterator
+{
+public:
+  typedef typename _MatrixTypeNested::InnerIterator IterBase;
+  InnerIterator(const SparseView& view, Index outer) :
+  IterBase(view.m_matrix, outer), m_view(view)
+  {
+    incrementToNonZero();
+  }
+
+  EIGEN_STRONG_INLINE InnerIterator& operator++()
+  {
+    IterBase::operator++();
+    incrementToNonZero();
+    return *this;
+  }
+
+  using IterBase::value;
+
+protected:
+  const SparseView& m_view;
+
+private:
+  void incrementToNonZero()
+  {
+    while((bool(*this)) && internal::isMuchSmallerThan(value(), m_view.m_reference, m_view.m_epsilon))
+    {
+      IterBase::operator++();
+    }
+  }
+};
+
+template<typename Derived>
+const SparseView<Derived> MatrixBase<Derived>::sparseView(const Scalar& m_reference,
+                                                          typename NumTraits<Scalar>::Real m_epsilon) const
+{
+  return SparseView<Derived>(derived(), m_reference, m_epsilon);
+}
+
+} // end namespace Eigen
+
+#endif
diff --git a/extern/Eigen3/Eigen/src/SparseCore/TriangularSolver.h b/extern/Eigen3/Eigen/src/SparseCore/TriangularSolver.h
new file mode 100644
index 00000000000..9a45e8f417f
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SparseCore/TriangularSolver.h
@@ -0,0 +1,349 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSETRIANGULARSOLVER_H
+#define EIGEN_SPARSETRIANGULARSOLVER_H
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, int Mode,
+  int UpLo = (Mode & Lower)
+           ? Lower
+           : (Mode & Upper)
+           ? Upper
+           : -1,
+  int StorageOrder = int(traits<Lhs>::Flags) & RowMajorBit>
+struct sparse_solve_triangular_selector;
+
+// forward substitution, row-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,RowMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    for(int col=0 ; col<other.cols() ; ++col)
+    {
+      for(int i=0; i<lhs.rows(); ++i)
+      {
+        Scalar tmp = other.coeff(i,col);
+        Scalar lastVal(0);
+        int lastIndex = 0;
+        for(typename Lhs::InnerIterator it(lhs, i); it; ++it)
+        {
+          lastVal = it.value();
+          lastIndex = it.index();
+          if(lastIndex==i)
+            break;
+          tmp -= lastVal * other.coeff(lastIndex,col);
+        }
+        if (Mode & UnitDiag)
+          other.coeffRef(i,col) = tmp;
+        else
+        {
+          eigen_assert(lastIndex==i);
+          other.coeffRef(i,col) = tmp/lastVal;
+        }
+      }
+    }
+  }
+};
+
+// backward substitution, row-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,RowMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    for(int col=0 ; col<other.cols() ; ++col)
+    {
+      for(int i=lhs.rows()-1 ; i>=0 ; --i)
+      {
+        Scalar tmp = other.coeff(i,col);
+        Scalar l_ii = 0;
+        typename Lhs::InnerIterator it(lhs, i);
+        while(it && it.index()<i)
+          ++it;
+        if(!(Mode & UnitDiag))
+        {
+          eigen_assert(it && it.index()==i);
+          l_ii = it.value();
+          ++it;
+        }
+        else if (it && it.index() == i)
+          ++it;
+        for(; it; ++it)
+        {
+          tmp -= it.value() * other.coeff(it.index(),col);
+        }
+
+        if (Mode & UnitDiag)
+          other.coeffRef(i,col) = tmp;
+        else
+          other.coeffRef(i,col) = tmp/l_ii;
+      }
+    }
+  }
+};
+
+// forward substitution, col-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,ColMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    for(int col=0 ; col<other.cols() ; ++col)
+    {
+      for(int i=0; i<lhs.cols(); ++i)
+      {
+        Scalar& tmp = other.coeffRef(i,col);
+        if (tmp!=Scalar(0)) // optimization when other is actually sparse
+        {
+          typename Lhs::InnerIterator it(lhs, i);
+          while(it && it.index()<i)
+            ++it;
+          if(!(Mode & UnitDiag))
+          {
+            eigen_assert(it && it.index()==i);
+            tmp /= it.value();
+          }
+          if (it && it.index()==i)
+            ++it;
+          for(; it; ++it)
+            other.coeffRef(it.index(), col) -= tmp * it.value();
+        }
+      }
+    }
+  }
+};
+
+// backward substitution, col-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,ColMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    for(int col=0 ; col<other.cols() ; ++col)
+    {
+      for(int i=lhs.cols()-1; i>=0; --i)
+      {
+        Scalar& tmp = other.coeffRef(i,col);
+        if (tmp!=Scalar(0)) // optimization when other is actually sparse
+        {
+          if(!(Mode & UnitDiag))
+          {
+            // TODO replace this by a binary search. make sure the binary search is safe for partially sorted elements
+            typename Lhs::ReverseInnerIterator it(lhs, i);
+            while(it && it.index()!=i)
+              --it;
+            eigen_assert(it && it.index()==i);
+            other.coeffRef(i,col) /= it.value();
+          }
+          typename Lhs::InnerIterator it(lhs, i);
+          for(; it && it.index()<i; ++it)
+            other.coeffRef(it.index(), col) -= tmp * it.value();
+        }
+      }
+    }
+  }
+};
+
+} // end namespace internal
+
+template<typename ExpressionType,int Mode>
+template<typename OtherDerived>
+void SparseTriangularView<ExpressionType,Mode>::solveInPlace(MatrixBase<OtherDerived>& other) const
+{
+  eigen_assert(m_matrix.cols() == m_matrix.rows() && m_matrix.cols() == other.rows());
+  eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
+
+  enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
+
+  typedef typename internal::conditional<copy,
+    typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
+  OtherCopy otherCopy(other.derived());
+
+  internal::sparse_solve_triangular_selector<ExpressionType, typename internal::remove_reference<OtherCopy>::type, Mode>::run(m_matrix, otherCopy);
+
+  if (copy)
+    other = otherCopy;
+}
+
+template<typename ExpressionType,int Mode>
+template<typename OtherDerived>
+typename internal::plain_matrix_type_column_major<OtherDerived>::type
+SparseTriangularView<ExpressionType,Mode>::solve(const MatrixBase<OtherDerived>& other) const
+{
+  typename internal::plain_matrix_type_column_major<OtherDerived>::type res(other);
+  solveInPlace(res);
+  return res;
+}
+
+// pure sparse path
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, int Mode,
+  int UpLo = (Mode & Lower)
+           ? Lower
+           : (Mode & Upper)
+           ? Upper
+           : -1,
+  int StorageOrder = int(Lhs::Flags) & (RowMajorBit)>
+struct sparse_solve_triangular_sparse_selector;
+
+// forward substitution, col-major
+template<typename Lhs, typename Rhs, int Mode, int UpLo>
+struct sparse_solve_triangular_sparse_selector<Lhs,Rhs,Mode,UpLo,ColMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  typedef typename promote_index_type<typename traits<Lhs>::Index,
+                                         typename traits<Rhs>::Index>::type Index;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    const bool IsLower = (UpLo==Lower);
+    AmbiVector<Scalar,Index> tempVector(other.rows()*2);
+    tempVector.setBounds(0,other.rows());
+
+    Rhs res(other.rows(), other.cols());
+    res.reserve(other.nonZeros());
+
+    for(int col=0 ; col<other.cols() ; ++col)
+    {
+      // FIXME estimate number of non zeros
+      tempVector.init(.99/*float(other.col(col).nonZeros())/float(other.rows())*/);
+      tempVector.setZero();
+      tempVector.restart();
+      for (typename Rhs::InnerIterator rhsIt(other, col); rhsIt; ++rhsIt)
+      {
+        tempVector.coeffRef(rhsIt.index()) = rhsIt.value();
+      }
+
+      for(int i=IsLower?0:lhs.cols()-1;
+          IsLower?i<lhs.cols():i>=0;
+          i+=IsLower?1:-1)
+      {
+        tempVector.restart();
+        Scalar& ci = tempVector.coeffRef(i);
+        if (ci!=Scalar(0))
+        {
+          // find
+          typename Lhs::InnerIterator it(lhs, i);
+          if(!(Mode & UnitDiag))
+          {
+            if (IsLower)
+            {
+              eigen_assert(it.index()==i);
+              ci /= it.value();
+            }
+            else
+              ci /= lhs.coeff(i,i);
+          }
+          tempVector.restart();
+          if (IsLower)
+          {
+            if (it.index()==i)
+              ++it;
+            for(; it; ++it)
+              tempVector.coeffRef(it.index()) -= ci * it.value();
+          }
+          else
+          {
+            for(; it && it.index()<i; ++it)
+              tempVector.coeffRef(it.index()) -= ci * it.value();
+          }
+        }
+      }
+
+
+      int count = 0;
+      // FIXME compute a reference value to filter zeros
+      for (typename AmbiVector<Scalar,Index>::Iterator it(tempVector/*,1e-12*/); it; ++it)
+      {
+        ++ count;
+//         std::cerr << "fill " << it.index() << ", " << col << "\n";
+//         std::cout << it.value() << "  ";
+        // FIXME use insertBack
+        res.insert(it.index(), col) = it.value();
+      }
+//       std::cout << "tempVector.nonZeros() == " << int(count) << " / " << (other.rows()) << "\n";
+    }
+    res.finalize();
+    other = res.markAsRValue();
+  }
+};
+
+} // end namespace internal
+
+template<typename ExpressionType,int Mode>
+template<typename OtherDerived>
+void SparseTriangularView<ExpressionType,Mode>::solveInPlace(SparseMatrixBase<OtherDerived>& other) const
+{
+  eigen_assert(m_matrix.cols() == m_matrix.rows() && m_matrix.cols() == other.rows());
+  eigen_assert( (!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
+
+//   enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
+
+//   typedef typename internal::conditional<copy,
+//     typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy;
+//   OtherCopy otherCopy(other.derived());
+
+  internal::sparse_solve_triangular_sparse_selector<ExpressionType, OtherDerived, Mode>::run(m_matrix, other.derived());
+
+//   if (copy)
+//     other = otherCopy;
+}
+
+#ifdef EIGEN2_SUPPORT
+
+// deprecated stuff:
+
+/** \deprecated */
+template<typename Derived>
+template<typename OtherDerived>
+void SparseMatrixBase<Derived>::solveTriangularInPlace(MatrixBase<OtherDerived>& other) const
+{
+  this->template triangular<Flags&(Upper|Lower)>().solveInPlace(other);
+}
+
+/** \deprecated */
+template<typename Derived>
+template<typename OtherDerived>
+typename internal::plain_matrix_type_column_major<OtherDerived>::type
+SparseMatrixBase<Derived>::solveTriangular(const MatrixBase<OtherDerived>& other) const
+{
+  typename internal::plain_matrix_type_column_major<OtherDerived>::type res(other);
+  derived().solveTriangularInPlace(res);
+  return res;
+}
+#endif // EIGEN2_SUPPORT
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSETRIANGULARSOLVER_H
diff --git a/extern/Eigen3/Eigen/src/StlSupport/CMakeLists.txt b/extern/Eigen3/Eigen/src/StlSupport/CMakeLists.txt
new file mode 100644
index 00000000000..0f094f637ac
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/StlSupport/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_StlSupport_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_StlSupport_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/StlSupport COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/SuperLUSupport/CMakeLists.txt b/extern/Eigen3/Eigen/src/SuperLUSupport/CMakeLists.txt
new file mode 100644
index 00000000000..b28ebe5839c
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SuperLUSupport/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_SuperLUSupport_SRCS "*.h")
+
+INSTALL(FILES 
+  ${Eigen_SuperLUSupport_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/SuperLUSupport COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h b/extern/Eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h
new file mode 100644
index 00000000000..6c3eb685849
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h
@@ -0,0 +1,1040 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SUPERLUSUPPORT_H
+#define EIGEN_SUPERLUSUPPORT_H
+
+namespace Eigen { 
+
+#define DECL_GSSVX(PREFIX,FLOATTYPE,KEYTYPE)		\
+    extern "C" {                                                                                          \
+      typedef struct { FLOATTYPE for_lu; FLOATTYPE total_needed; int expansions; } PREFIX##mem_usage_t;   \
+      extern void PREFIX##gssvx(superlu_options_t *, SuperMatrix *, int *, int *, int *,                  \
+                                char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *,           \
+                                void *, int, SuperMatrix *, SuperMatrix *,                                \
+                                FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, FLOATTYPE *,                       \
+                                PREFIX##mem_usage_t *, SuperLUStat_t *, int *);                           \
+    }                                                                                                     \
+    inline float SuperLU_gssvx(superlu_options_t *options, SuperMatrix *A,                                \
+         int *perm_c, int *perm_r, int *etree, char *equed,                                               \
+         FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L,                                                      \
+         SuperMatrix *U, void *work, int lwork,                                                           \
+         SuperMatrix *B, SuperMatrix *X,                                                                  \
+         FLOATTYPE *recip_pivot_growth,                                                                   \
+         FLOATTYPE *rcond, FLOATTYPE *ferr, FLOATTYPE *berr,                                              \
+         SuperLUStat_t *stats, int *info, KEYTYPE) {                                                      \
+    PREFIX##mem_usage_t mem_usage;                                                                        \
+    PREFIX##gssvx(options, A, perm_c, perm_r, etree, equed, R, C, L,                                      \
+         U, work, lwork, B, X, recip_pivot_growth, rcond,                                                 \
+         ferr, berr, &mem_usage, stats, info);                                                            \
+    return mem_usage.for_lu; /* bytes used by the factor storage */                                       \
+  }
+
+DECL_GSSVX(s,float,float)
+DECL_GSSVX(c,float,std::complex<float>)
+DECL_GSSVX(d,double,double)
+DECL_GSSVX(z,double,std::complex<double>)
+
+#ifdef MILU_ALPHA
+#define EIGEN_SUPERLU_HAS_ILU
+#endif
+
+#ifdef EIGEN_SUPERLU_HAS_ILU
+
+// similarly for the incomplete factorization using gsisx
+#define DECL_GSISX(PREFIX,FLOATTYPE,KEYTYPE)                                                    \
+    extern "C" {                                                                                \
+      extern void PREFIX##gsisx(superlu_options_t *, SuperMatrix *, int *, int *, int *,        \
+                         char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *,        \
+                         void *, int, SuperMatrix *, SuperMatrix *, FLOATTYPE *, FLOATTYPE *,   \
+                         PREFIX##mem_usage_t *, SuperLUStat_t *, int *);                        \
+    }                                                                                           \
+    inline float SuperLU_gsisx(superlu_options_t *options, SuperMatrix *A,                      \
+         int *perm_c, int *perm_r, int *etree, char *equed,                                     \
+         FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L,                                            \
+         SuperMatrix *U, void *work, int lwork,                                                 \
+         SuperMatrix *B, SuperMatrix *X,                                                        \
+         FLOATTYPE *recip_pivot_growth,                                                         \
+         FLOATTYPE *rcond,                                                                      \
+         SuperLUStat_t *stats, int *info, KEYTYPE) {                                            \
+    PREFIX##mem_usage_t mem_usage;                                                              \
+    PREFIX##gsisx(options, A, perm_c, perm_r, etree, equed, R, C, L,                            \
+         U, work, lwork, B, X, recip_pivot_growth, rcond,                                       \
+         &mem_usage, stats, info);                                                              \
+    return mem_usage.for_lu; /* bytes used by the factor storage */                             \
+  }
+
+DECL_GSISX(s,float,float)
+DECL_GSISX(c,float,std::complex<float>)
+DECL_GSISX(d,double,double)
+DECL_GSISX(z,double,std::complex<double>)
+
+#endif
+
+template<typename MatrixType>
+struct SluMatrixMapHelper;
+
+/** \internal
+  *
+  * A wrapper class for SuperLU matrices. It supports only compressed sparse matrices
+  * and dense matrices. Supernodal and other fancy format are not supported by this wrapper.
+  *
+  * This wrapper class mainly aims to avoids the need of dynamic allocation of the storage structure.
+  */
+struct SluMatrix : SuperMatrix
+{
+  SluMatrix()
+  {
+    Store = &storage;
+  }
+
+  SluMatrix(const SluMatrix& other)
+    : SuperMatrix(other)
+  {
+    Store = &storage;
+    storage = other.storage;
+  }
+
+  SluMatrix& operator=(const SluMatrix& other)
+  {
+    SuperMatrix::operator=(static_cast<const SuperMatrix&>(other));
+    Store = &storage;
+    storage = other.storage;
+    return *this;
+  }
+
+  struct
+  {
+    union {int nnz;int lda;};
+    void *values;
+    int *innerInd;
+    int *outerInd;
+  } storage;
+
+  void setStorageType(Stype_t t)
+  {
+    Stype = t;
+    if (t==SLU_NC || t==SLU_NR || t==SLU_DN)
+      Store = &storage;
+    else
+    {
+      eigen_assert(false && "storage type not supported");
+      Store = 0;
+    }
+  }
+
+  template<typename Scalar>
+  void setScalarType()
+  {
+    if (internal::is_same<Scalar,float>::value)
+      Dtype = SLU_S;
+    else if (internal::is_same<Scalar,double>::value)
+      Dtype = SLU_D;
+    else if (internal::is_same<Scalar,std::complex<float> >::value)
+      Dtype = SLU_C;
+    else if (internal::is_same<Scalar,std::complex<double> >::value)
+      Dtype = SLU_Z;
+    else
+    {
+      eigen_assert(false && "Scalar type not supported by SuperLU");
+    }
+  }
+
+  template<typename MatrixType>
+  static SluMatrix Map(MatrixBase<MatrixType>& _mat)
+  {
+    MatrixType& mat(_mat.derived());
+    eigen_assert( ((MatrixType::Flags&RowMajorBit)!=RowMajorBit) && "row-major dense matrices are not supported by SuperLU");
+    SluMatrix res;
+    res.setStorageType(SLU_DN);
+    res.setScalarType<typename MatrixType::Scalar>();
+    res.Mtype     = SLU_GE;
+
+    res.nrow      = mat.rows();
+    res.ncol      = mat.cols();
+
+    res.storage.lda       = MatrixType::IsVectorAtCompileTime ? mat.size() : mat.outerStride();
+    res.storage.values    = mat.data();
+    return res;
+  }
+
+  template<typename MatrixType>
+  static SluMatrix Map(SparseMatrixBase<MatrixType>& mat)
+  {
+    SluMatrix res;
+    if ((MatrixType::Flags&RowMajorBit)==RowMajorBit)
+    {
+      res.setStorageType(SLU_NR);
+      res.nrow      = mat.cols();
+      res.ncol      = mat.rows();
+    }
+    else
+    {
+      res.setStorageType(SLU_NC);
+      res.nrow      = mat.rows();
+      res.ncol      = mat.cols();
+    }
+
+    res.Mtype       = SLU_GE;
+
+    res.storage.nnz       = mat.nonZeros();
+    res.storage.values    = mat.derived().valuePtr();
+    res.storage.innerInd  = mat.derived().innerIndexPtr();
+    res.storage.outerInd  = mat.derived().outerIndexPtr();
+
+    res.setScalarType<typename MatrixType::Scalar>();
+
+    // FIXME the following is not very accurate
+    if (MatrixType::Flags & Upper)
+      res.Mtype = SLU_TRU;
+    if (MatrixType::Flags & Lower)
+      res.Mtype = SLU_TRL;
+
+    eigen_assert(((MatrixType::Flags & SelfAdjoint)==0) && "SelfAdjoint matrix shape not supported by SuperLU");
+
+    return res;
+  }
+};
+
+template<typename Scalar, int Rows, int Cols, int Options, int MRows, int MCols>
+struct SluMatrixMapHelper<Matrix<Scalar,Rows,Cols,Options,MRows,MCols> >
+{
+  typedef Matrix<Scalar,Rows,Cols,Options,MRows,MCols> MatrixType;
+  static void run(MatrixType& mat, SluMatrix& res)
+  {
+    eigen_assert( ((Options&RowMajor)!=RowMajor) && "row-major dense matrices is not supported by SuperLU");
+    res.setStorageType(SLU_DN);
+    res.setScalarType<Scalar>();
+    res.Mtype     = SLU_GE;
+
+    res.nrow      = mat.rows();
+    res.ncol      = mat.cols();
+
+    res.storage.lda       = mat.outerStride();
+    res.storage.values    = mat.data();
+  }
+};
+
+template<typename Derived>
+struct SluMatrixMapHelper<SparseMatrixBase<Derived> >
+{
+  typedef Derived MatrixType;
+  static void run(MatrixType& mat, SluMatrix& res)
+  {
+    if ((MatrixType::Flags&RowMajorBit)==RowMajorBit)
+    {
+      res.setStorageType(SLU_NR);
+      res.nrow      = mat.cols();
+      res.ncol      = mat.rows();
+    }
+    else
+    {
+      res.setStorageType(SLU_NC);
+      res.nrow      = mat.rows();
+      res.ncol      = mat.cols();
+    }
+
+    res.Mtype       = SLU_GE;
+
+    res.storage.nnz       = mat.nonZeros();
+    res.storage.values    = mat.valuePtr();
+    res.storage.innerInd  = mat.innerIndexPtr();
+    res.storage.outerInd  = mat.outerIndexPtr();
+
+    res.setScalarType<typename MatrixType::Scalar>();
+
+    // FIXME the following is not very accurate
+    if (MatrixType::Flags & Upper)
+      res.Mtype = SLU_TRU;
+    if (MatrixType::Flags & Lower)
+      res.Mtype = SLU_TRL;
+
+    eigen_assert(((MatrixType::Flags & SelfAdjoint)==0) && "SelfAdjoint matrix shape not supported by SuperLU");
+  }
+};
+
+namespace internal {
+
+template<typename MatrixType>
+SluMatrix asSluMatrix(MatrixType& mat)
+{
+  return SluMatrix::Map(mat);
+}
+
+/** View a Super LU matrix as an Eigen expression */
+template<typename Scalar, int Flags, typename Index>
+MappedSparseMatrix<Scalar,Flags,Index> map_superlu(SluMatrix& sluMat)
+{
+  eigen_assert((Flags&RowMajor)==RowMajor && sluMat.Stype == SLU_NR
+         || (Flags&ColMajor)==ColMajor && sluMat.Stype == SLU_NC);
+
+  Index outerSize = (Flags&RowMajor)==RowMajor ? sluMat.ncol : sluMat.nrow;
+
+  return MappedSparseMatrix<Scalar,Flags,Index>(
+    sluMat.nrow, sluMat.ncol, sluMat.storage.outerInd[outerSize],
+    sluMat.storage.outerInd, sluMat.storage.innerInd, reinterpret_cast<Scalar*>(sluMat.storage.values) );
+}
+
+} // end namespace internal
+
+/** \ingroup SuperLUSupport_Module
+  * \class SuperLUBase
+  * \brief The base class for the direct and incomplete LU factorization of SuperLU
+  */
+template<typename _MatrixType, typename Derived>
+class SuperLUBase : internal::noncopyable
+{
+  public:
+    typedef _MatrixType MatrixType;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::Index Index;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;    
+    typedef SparseMatrix<Scalar> LUMatrixType;
+
+  public:
+
+    SuperLUBase() {}
+
+    ~SuperLUBase()
+    {
+      clearFactors();
+    }
+    
+    Derived& derived() { return *static_cast<Derived*>(this); }
+    const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+    
+    /** \returns a reference to the Super LU option object to configure the  Super LU algorithms. */
+    inline superlu_options_t& options() { return m_sluOptions; }
+    
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was succesful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    void compute(const MatrixType& matrix)
+    {
+      derived().analyzePattern(matrix);
+      derived().factorize(matrix);
+    }
+    
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const internal::solve_retval<SuperLUBase, Rhs> solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "SuperLU is not initialized.");
+      eigen_assert(rows()==b.rows()
+                && "SuperLU::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::solve_retval<SuperLUBase, Rhs>(*this, b.derived());
+    }
+    
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+//     template<typename Rhs>
+//     inline const internal::sparse_solve_retval<SuperLU, Rhs> solve(const SparseMatrixBase<Rhs>& b) const
+//     {
+//       eigen_assert(m_isInitialized && "SuperLU is not initialized.");
+//       eigen_assert(rows()==b.rows()
+//                 && "SuperLU::solve(): invalid number of rows of the right hand side matrix b");
+//       return internal::sparse_solve_retval<SuperLU, Rhs>(*this, b.derived());
+//     }
+    
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      * 
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& /*matrix*/)
+    {
+      m_isInitialized = true;
+      m_info = Success;
+      m_analysisIsOk = true;
+      m_factorizationIsOk = false;
+    }
+    
+    template<typename Stream>
+    void dumpMemory(Stream& s)
+    {}
+    
+  protected:
+    
+    void initFactorization(const MatrixType& a)
+    {
+      set_default_options(&this->m_sluOptions);
+      
+      const int size = a.rows();
+      m_matrix = a;
+
+      m_sluA = internal::asSluMatrix(m_matrix);
+      clearFactors();
+
+      m_p.resize(size);
+      m_q.resize(size);
+      m_sluRscale.resize(size);
+      m_sluCscale.resize(size);
+      m_sluEtree.resize(size);
+
+      // set empty B and X
+      m_sluB.setStorageType(SLU_DN);
+      m_sluB.setScalarType<Scalar>();
+      m_sluB.Mtype          = SLU_GE;
+      m_sluB.storage.values = 0;
+      m_sluB.nrow           = 0;
+      m_sluB.ncol           = 0;
+      m_sluB.storage.lda    = size;
+      m_sluX                = m_sluB;
+      
+      m_extractedDataAreDirty = true;
+    }
+    
+    void init()
+    {
+      m_info = InvalidInput;
+      m_isInitialized = false;
+      m_sluL.Store = 0;
+      m_sluU.Store = 0;
+    }
+    
+    void extractData() const;
+
+    void clearFactors()
+    {
+      if(m_sluL.Store)
+        Destroy_SuperNode_Matrix(&m_sluL);
+      if(m_sluU.Store)
+        Destroy_CompCol_Matrix(&m_sluU);
+
+      m_sluL.Store = 0;
+      m_sluU.Store = 0;
+
+      memset(&m_sluL,0,sizeof m_sluL);
+      memset(&m_sluU,0,sizeof m_sluU);
+    }
+
+    // cached data to reduce reallocation, etc.
+    mutable LUMatrixType m_l;
+    mutable LUMatrixType m_u;
+    mutable IntColVectorType m_p;
+    mutable IntRowVectorType m_q;
+
+    mutable LUMatrixType m_matrix;  // copy of the factorized matrix
+    mutable SluMatrix m_sluA;
+    mutable SuperMatrix m_sluL, m_sluU;
+    mutable SluMatrix m_sluB, m_sluX;
+    mutable SuperLUStat_t m_sluStat;
+    mutable superlu_options_t m_sluOptions;
+    mutable std::vector<int> m_sluEtree;
+    mutable Matrix<RealScalar,Dynamic,1> m_sluRscale, m_sluCscale;
+    mutable Matrix<RealScalar,Dynamic,1> m_sluFerr, m_sluBerr;
+    mutable char m_sluEqued;
+
+    mutable ComputationInfo m_info;
+    bool m_isInitialized;
+    int m_factorizationIsOk;
+    int m_analysisIsOk;
+    mutable bool m_extractedDataAreDirty;
+    
+  private:
+    SuperLUBase(SuperLUBase& ) { }
+};
+
+
+/** \ingroup SuperLUSupport_Module
+  * \class SuperLU
+  * \brief A sparse direct LU factorization and solver based on the SuperLU library
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
+  * using the SuperLU library. The sparse matrix A must be squared and invertible. The vectors or matrices
+  * X and B can be either dense or sparse.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \sa \ref TutorialSparseDirectSolvers
+  */
+template<typename _MatrixType>
+class SuperLU : public SuperLUBase<_MatrixType,SuperLU<_MatrixType> >
+{
+  public:
+    typedef SuperLUBase<_MatrixType,SuperLU> Base;
+    typedef _MatrixType MatrixType;
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::RealScalar RealScalar;
+    typedef typename Base::Index Index;
+    typedef typename Base::IntRowVectorType IntRowVectorType;
+    typedef typename Base::IntColVectorType IntColVectorType;    
+    typedef typename Base::LUMatrixType LUMatrixType;
+    typedef TriangularView<LUMatrixType, Lower|UnitDiag>  LMatrixType;
+    typedef TriangularView<LUMatrixType,  Upper>           UMatrixType;
+
+  public:
+
+    SuperLU() : Base() { init(); }
+
+    SuperLU(const MatrixType& matrix) : Base()
+    {
+      Base::init();
+      compute(matrix);
+    }
+
+    ~SuperLU()
+    {
+    }
+    
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      * 
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    {
+      m_info = InvalidInput;
+      m_isInitialized = false;
+      Base::analyzePattern(matrix);
+    }
+    
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& matrix);
+    
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal */
+    template<typename Rhs,typename Dest>
+    void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
+    #endif // EIGEN_PARSED_BY_DOXYGEN
+    
+    inline const LMatrixType& matrixL() const
+    {
+      if (m_extractedDataAreDirty) this->extractData();
+      return m_l;
+    }
+
+    inline const UMatrixType& matrixU() const
+    {
+      if (m_extractedDataAreDirty) this->extractData();
+      return m_u;
+    }
+
+    inline const IntColVectorType& permutationP() const
+    {
+      if (m_extractedDataAreDirty) this->extractData();
+      return m_p;
+    }
+
+    inline const IntRowVectorType& permutationQ() const
+    {
+      if (m_extractedDataAreDirty) this->extractData();
+      return m_q;
+    }
+    
+    Scalar determinant() const;
+    
+  protected:
+    
+    using Base::m_matrix;
+    using Base::m_sluOptions;
+    using Base::m_sluA;
+    using Base::m_sluB;
+    using Base::m_sluX;
+    using Base::m_p;
+    using Base::m_q;
+    using Base::m_sluEtree;
+    using Base::m_sluEqued;
+    using Base::m_sluRscale;
+    using Base::m_sluCscale;
+    using Base::m_sluL;
+    using Base::m_sluU;
+    using Base::m_sluStat;
+    using Base::m_sluFerr;
+    using Base::m_sluBerr;
+    using Base::m_l;
+    using Base::m_u;
+    
+    using Base::m_analysisIsOk;
+    using Base::m_factorizationIsOk;
+    using Base::m_extractedDataAreDirty;
+    using Base::m_isInitialized;
+    using Base::m_info;
+    
+    void init()
+    {
+      Base::init();
+      
+      set_default_options(&this->m_sluOptions);
+      m_sluOptions.PrintStat        = NO;
+      m_sluOptions.ConditionNumber  = NO;
+      m_sluOptions.Trans            = NOTRANS;
+      m_sluOptions.ColPerm          = COLAMD;
+    }
+    
+    
+  private:
+    SuperLU(SuperLU& ) { }
+};
+
+template<typename MatrixType>
+void SuperLU<MatrixType>::factorize(const MatrixType& a)
+{
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  if(!m_analysisIsOk)
+  {
+    m_info = InvalidInput;
+    return;
+  }
+  
+  this->initFactorization(a);
+  
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+  RealScalar ferr, berr;
+
+  StatInit(&m_sluStat);
+  SuperLU_gssvx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0],
+                &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0],
+                &m_sluL, &m_sluU,
+                NULL, 0,
+                &m_sluB, &m_sluX,
+                &recip_pivot_growth, &rcond,
+                &ferr, &berr,
+                &m_sluStat, &info, Scalar());
+  StatFree(&m_sluStat);
+
+  m_extractedDataAreDirty = true;
+
+  // FIXME how to better check for errors ???
+  m_info = info == 0 ? Success : NumericalIssue;
+  m_factorizationIsOk = true;
+}
+
+template<typename MatrixType>
+template<typename Rhs,typename Dest>
+void SuperLU<MatrixType>::_solve(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const
+{
+  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
+
+  const int size = m_matrix.rows();
+  const int rhsCols = b.cols();
+  eigen_assert(size==b.rows());
+
+  m_sluOptions.Trans = NOTRANS;
+  m_sluOptions.Fact = FACTORED;
+  m_sluOptions.IterRefine = NOREFINE;
+  
+
+  m_sluFerr.resize(rhsCols);
+  m_sluBerr.resize(rhsCols);
+  m_sluB = SluMatrix::Map(b.const_cast_derived());
+  m_sluX = SluMatrix::Map(x.derived());
+  
+  typename Rhs::PlainObject b_cpy;
+  if(m_sluEqued!='N')
+  {
+    b_cpy = b;
+    m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());  
+  }
+
+  StatInit(&m_sluStat);
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+  SuperLU_gssvx(&m_sluOptions, &m_sluA,
+                m_q.data(), m_p.data(),
+                &m_sluEtree[0], &m_sluEqued,
+                &m_sluRscale[0], &m_sluCscale[0],
+                &m_sluL, &m_sluU,
+                NULL, 0,
+                &m_sluB, &m_sluX,
+                &recip_pivot_growth, &rcond,
+                &m_sluFerr[0], &m_sluBerr[0],
+                &m_sluStat, &info, Scalar());
+  StatFree(&m_sluStat);
+  m_info = info==0 ? Success : NumericalIssue;
+}
+
+// the code of this extractData() function has been adapted from the SuperLU's Matlab support code,
+//
+//  Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+//
+//  THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+//  EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+//
+template<typename MatrixType, typename Derived>
+void SuperLUBase<MatrixType,Derived>::extractData() const
+{
+  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for extracting factors, you must first call either compute() or analyzePattern()/factorize()");
+  if (m_extractedDataAreDirty)
+  {
+    int         upper;
+    int         fsupc, istart, nsupr;
+    int         lastl = 0, lastu = 0;
+    SCformat    *Lstore = static_cast<SCformat*>(m_sluL.Store);
+    NCformat    *Ustore = static_cast<NCformat*>(m_sluU.Store);
+    Scalar      *SNptr;
+
+    const int size = m_matrix.rows();
+    m_l.resize(size,size);
+    m_l.resizeNonZeros(Lstore->nnz);
+    m_u.resize(size,size);
+    m_u.resizeNonZeros(Ustore->nnz);
+
+    int* Lcol = m_l.outerIndexPtr();
+    int* Lrow = m_l.innerIndexPtr();
+    Scalar* Lval = m_l.valuePtr();
+
+    int* Ucol = m_u.outerIndexPtr();
+    int* Urow = m_u.innerIndexPtr();
+    Scalar* Uval = m_u.valuePtr();
+
+    Ucol[0] = 0;
+    Ucol[0] = 0;
+
+    /* for each supernode */
+    for (int k = 0; k <= Lstore->nsuper; ++k)
+    {
+      fsupc   = L_FST_SUPC(k);
+      istart  = L_SUB_START(fsupc);
+      nsupr   = L_SUB_START(fsupc+1) - istart;
+      upper   = 1;
+
+      /* for each column in the supernode */
+      for (int j = fsupc; j < L_FST_SUPC(k+1); ++j)
+      {
+        SNptr = &((Scalar*)Lstore->nzval)[L_NZ_START(j)];
+
+        /* Extract U */
+        for (int i = U_NZ_START(j); i < U_NZ_START(j+1); ++i)
+        {
+          Uval[lastu] = ((Scalar*)Ustore->nzval)[i];
+          /* Matlab doesn't like explicit zero. */
+          if (Uval[lastu] != 0.0)
+            Urow[lastu++] = U_SUB(i);
+        }
+        for (int i = 0; i < upper; ++i)
+        {
+          /* upper triangle in the supernode */
+          Uval[lastu] = SNptr[i];
+          /* Matlab doesn't like explicit zero. */
+          if (Uval[lastu] != 0.0)
+            Urow[lastu++] = L_SUB(istart+i);
+        }
+        Ucol[j+1] = lastu;
+
+        /* Extract L */
+        Lval[lastl] = 1.0; /* unit diagonal */
+        Lrow[lastl++] = L_SUB(istart + upper - 1);
+        for (int i = upper; i < nsupr; ++i)
+        {
+          Lval[lastl] = SNptr[i];
+          /* Matlab doesn't like explicit zero. */
+          if (Lval[lastl] != 0.0)
+            Lrow[lastl++] = L_SUB(istart+i);
+        }
+        Lcol[j+1] = lastl;
+
+        ++upper;
+      } /* for j ... */
+
+    } /* for k ... */
+
+    // squeeze the matrices :
+    m_l.resizeNonZeros(lastl);
+    m_u.resizeNonZeros(lastu);
+
+    m_extractedDataAreDirty = false;
+  }
+}
+
+template<typename MatrixType>
+typename SuperLU<MatrixType>::Scalar SuperLU<MatrixType>::determinant() const
+{
+  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for computing the determinant, you must first call either compute() or analyzePattern()/factorize()");
+  
+  if (m_extractedDataAreDirty)
+    this->extractData();
+
+  Scalar det = Scalar(1);
+  for (int j=0; j<m_u.cols(); ++j)
+  {
+    if (m_u.outerIndexPtr()[j+1]-m_u.outerIndexPtr()[j] > 0)
+    {
+      int lastId = m_u.outerIndexPtr()[j+1]-1;
+      eigen_assert(m_u.innerIndexPtr()[lastId]<=j);
+      if (m_u.innerIndexPtr()[lastId]==j)
+        det *= m_u.valuePtr()[lastId];
+    }
+  }
+  if(m_sluEqued!='N')
+    return det/m_sluRscale.prod()/m_sluCscale.prod();
+  else
+    return det;
+}
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+#define EIGEN_SUPERLU_HAS_ILU
+#endif
+
+#ifdef EIGEN_SUPERLU_HAS_ILU
+
+/** \ingroup SuperLUSupport_Module
+  * \class SuperILU
+  * \brief A sparse direct \b incomplete LU factorization and solver based on the SuperLU library
+  *
+  * This class allows to solve for an approximate solution of A.X = B sparse linear problems via an incomplete LU factorization
+  * using the SuperLU library. This class is aimed to be used as a preconditioner of the iterative linear solvers.
+  *
+  * \warning This class requires SuperLU 4 or later.
+  *
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \sa \ref TutorialSparseDirectSolvers, class ConjugateGradient, class BiCGSTAB
+  */
+
+template<typename _MatrixType>
+class SuperILU : public SuperLUBase<_MatrixType,SuperILU<_MatrixType> >
+{
+  public:
+    typedef SuperLUBase<_MatrixType,SuperILU> Base;
+    typedef _MatrixType MatrixType;
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::RealScalar RealScalar;
+    typedef typename Base::Index Index;
+
+  public:
+
+    SuperILU() : Base() { init(); }
+
+    SuperILU(const MatrixType& matrix) : Base()
+    {
+      init();
+      compute(matrix);
+    }
+
+    ~SuperILU()
+    {
+    }
+    
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      * 
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    {
+      Base::analyzePattern(matrix);
+    }
+    
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& matrix);
+    
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal */
+    template<typename Rhs,typename Dest>
+    void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
+    #endif // EIGEN_PARSED_BY_DOXYGEN
+    
+  protected:
+    
+    using Base::m_matrix;
+    using Base::m_sluOptions;
+    using Base::m_sluA;
+    using Base::m_sluB;
+    using Base::m_sluX;
+    using Base::m_p;
+    using Base::m_q;
+    using Base::m_sluEtree;
+    using Base::m_sluEqued;
+    using Base::m_sluRscale;
+    using Base::m_sluCscale;
+    using Base::m_sluL;
+    using Base::m_sluU;
+    using Base::m_sluStat;
+    using Base::m_sluFerr;
+    using Base::m_sluBerr;
+    using Base::m_l;
+    using Base::m_u;
+    
+    using Base::m_analysisIsOk;
+    using Base::m_factorizationIsOk;
+    using Base::m_extractedDataAreDirty;
+    using Base::m_isInitialized;
+    using Base::m_info;
+
+    void init()
+    {
+      Base::init();
+      
+      ilu_set_default_options(&m_sluOptions);
+      m_sluOptions.PrintStat        = NO;
+      m_sluOptions.ConditionNumber  = NO;
+      m_sluOptions.Trans            = NOTRANS;
+      m_sluOptions.ColPerm          = MMD_AT_PLUS_A;
+      
+      // no attempt to preserve column sum
+      m_sluOptions.ILU_MILU = SILU;
+      // only basic ILU(k) support -- no direct control over memory consumption
+      // better to use ILU_DropRule = DROP_BASIC | DROP_AREA
+      // and set ILU_FillFactor to max memory growth
+      m_sluOptions.ILU_DropRule = DROP_BASIC;
+      m_sluOptions.ILU_DropTol = NumTraits<Scalar>::dummy_precision()*10;
+    }
+    
+  private:
+    SuperILU(SuperILU& ) { }
+};
+
+template<typename MatrixType>
+void SuperILU<MatrixType>::factorize(const MatrixType& a)
+{
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  if(!m_analysisIsOk)
+  {
+    m_info = InvalidInput;
+    return;
+  }
+  
+  this->initFactorization(a);
+
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+
+  StatInit(&m_sluStat);
+  SuperLU_gsisx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0],
+                &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0],
+                &m_sluL, &m_sluU,
+                NULL, 0,
+                &m_sluB, &m_sluX,
+                &recip_pivot_growth, &rcond,
+                &m_sluStat, &info, Scalar());
+  StatFree(&m_sluStat);
+
+  // FIXME how to better check for errors ???
+  m_info = info == 0 ? Success : NumericalIssue;
+  m_factorizationIsOk = true;
+}
+
+template<typename MatrixType>
+template<typename Rhs,typename Dest>
+void SuperILU<MatrixType>::_solve(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const
+{
+  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
+
+  const int size = m_matrix.rows();
+  const int rhsCols = b.cols();
+  eigen_assert(size==b.rows());
+
+  m_sluOptions.Trans = NOTRANS;
+  m_sluOptions.Fact = FACTORED;
+  m_sluOptions.IterRefine = NOREFINE;
+
+  m_sluFerr.resize(rhsCols);
+  m_sluBerr.resize(rhsCols);
+  m_sluB = SluMatrix::Map(b.const_cast_derived());
+  m_sluX = SluMatrix::Map(x.derived());
+
+  typename Rhs::PlainObject b_cpy;
+  if(m_sluEqued!='N')
+  {
+    b_cpy = b;
+    m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());  
+  }
+  
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+
+  StatInit(&m_sluStat);
+  SuperLU_gsisx(&m_sluOptions, &m_sluA,
+                m_q.data(), m_p.data(),
+                &m_sluEtree[0], &m_sluEqued,
+                &m_sluRscale[0], &m_sluCscale[0],
+                &m_sluL, &m_sluU,
+                NULL, 0,
+                &m_sluB, &m_sluX,
+                &recip_pivot_growth, &rcond,
+                &m_sluStat, &info, Scalar());
+  StatFree(&m_sluStat);
+
+  m_info = info==0 ? Success : NumericalIssue;
+}
+#endif
+
+namespace internal {
+  
+template<typename _MatrixType, typename Derived, typename Rhs>
+struct solve_retval<SuperLUBase<_MatrixType,Derived>, Rhs>
+  : solve_retval_base<SuperLUBase<_MatrixType,Derived>, Rhs>
+{
+  typedef SuperLUBase<_MatrixType,Derived> Dec;
+  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec().derived()._solve(rhs(),dst);
+  }
+};
+
+template<typename _MatrixType, typename Derived, typename Rhs>
+struct sparse_solve_retval<SuperLUBase<_MatrixType,Derived>, Rhs>
+  : sparse_solve_retval_base<SuperLUBase<_MatrixType,Derived>, Rhs>
+{
+  typedef SuperLUBase<_MatrixType,Derived> Dec;
+  EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec().derived()._solve(rhs(),dst);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SUPERLUSUPPORT_H
diff --git a/extern/Eigen3/Eigen/src/UmfPackSupport/CMakeLists.txt b/extern/Eigen3/Eigen/src/UmfPackSupport/CMakeLists.txt
new file mode 100644
index 00000000000..a57de00209d
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/UmfPackSupport/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_UmfPackSupport_SRCS "*.h")
+
+INSTALL(FILES 
+  ${Eigen_UmfPackSupport_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/UmfPackSupport COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h b/extern/Eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h
new file mode 100644
index 00000000000..f98a4c8c0e3
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h
@@ -0,0 +1,446 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_UMFPACKSUPPORT_H
+#define EIGEN_UMFPACKSUPPORT_H
+
+namespace Eigen { 
+
+/* TODO extract L, extract U, compute det, etc... */
+
+// generic double/complex<double> wrapper functions:
+
+inline void umfpack_free_numeric(void **Numeric, double)
+{ umfpack_di_free_numeric(Numeric); *Numeric = 0; }
+
+inline void umfpack_free_numeric(void **Numeric, std::complex<double>)
+{ umfpack_zi_free_numeric(Numeric); *Numeric = 0; }
+
+inline void umfpack_free_symbolic(void **Symbolic, double)
+{ umfpack_di_free_symbolic(Symbolic); *Symbolic = 0; }
+
+inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>)
+{ umfpack_zi_free_symbolic(Symbolic); *Symbolic = 0; }
+
+inline int umfpack_symbolic(int n_row,int n_col,
+                            const int Ap[], const int Ai[], const double Ax[], void **Symbolic,
+                            const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
+{
+  return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info);
+}
+
+inline int umfpack_symbolic(int n_row,int n_col,
+                            const int Ap[], const int Ai[], const std::complex<double> Ax[], void **Symbolic,
+                            const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
+{
+  return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&internal::real_ref(Ax[0]),0,Symbolic,Control,Info);
+}
+
+inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[],
+                            void *Symbolic, void **Numeric,
+                            const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
+{
+  return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info);
+}
+
+inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex<double> Ax[],
+                            void *Symbolic, void **Numeric,
+                            const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
+{
+  return umfpack_zi_numeric(Ap,Ai,&internal::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info);
+}
+
+inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[],
+                          double X[], const double B[], void *Numeric,
+                          const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
+{
+  return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info);
+}
+
+inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[],
+                          std::complex<double> X[], const std::complex<double> B[], void *Numeric,
+                          const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
+{
+  return umfpack_zi_solve(sys,Ap,Ai,&internal::real_ref(Ax[0]),0,&internal::real_ref(X[0]),0,&internal::real_ref(B[0]),0,Numeric,Control,Info);
+}
+
+inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double)
+{
+  return umfpack_di_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
+}
+
+inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, std::complex<double>)
+{
+  return umfpack_zi_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
+}
+
+inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[],
+                               int P[], int Q[], double Dx[], int *do_recip, double Rs[], void *Numeric)
+{
+  return umfpack_di_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric);
+}
+
+inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex<double> Lx[], int Up[], int Ui[], std::complex<double> Ux[],
+                               int P[], int Q[], std::complex<double> Dx[], int *do_recip, double Rs[], void *Numeric)
+{
+  double& lx0_real = internal::real_ref(Lx[0]);
+  double& ux0_real = internal::real_ref(Ux[0]);
+  double& dx0_real = internal::real_ref(Dx[0]);
+  return umfpack_zi_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q,
+                                Dx?&dx0_real:0,0,do_recip,Rs,Numeric);
+}
+
+inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO])
+{
+  return umfpack_di_get_determinant(Mx,Ex,NumericHandle,User_Info);
+}
+
+inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO])
+{
+  double& mx_real = internal::real_ref(*Mx);
+  return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info);
+}
+
+/** \ingroup UmfPackSupport_Module
+  * \brief A sparse LU factorization and solver based on UmfPack
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a LU factorization
+  * using the UmfPack library. The sparse matrix A must be squared and full rank.
+  * The vectors or matrices X and B can be either dense or sparse.
+  *
+  * \WARNING The input matrix A should be in a \b compressed and \b column-major form.
+  * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
+  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \sa \ref TutorialSparseDirectSolvers
+  */
+template<typename _MatrixType>
+class UmfPackLU : internal::noncopyable
+{
+  public:
+    typedef _MatrixType MatrixType;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::Index Index;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
+    typedef SparseMatrix<Scalar> LUMatrixType;
+    typedef SparseMatrix<Scalar,ColMajor,int> UmfpackMatrixType;
+
+  public:
+
+    UmfPackLU() { init(); }
+
+    UmfPackLU(const MatrixType& matrix)
+    {
+      init();
+      compute(matrix);
+    }
+
+    ~UmfPackLU()
+    {
+      if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
+      if(m_numeric)  umfpack_free_numeric(&m_numeric,Scalar());
+    }
+
+    inline Index rows() const { return m_copyMatrix.rows(); }
+    inline Index cols() const { return m_copyMatrix.cols(); }
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was succesful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+
+    inline const LUMatrixType& matrixL() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_l;
+    }
+
+    inline const LUMatrixType& matrixU() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_u;
+    }
+
+    inline const IntColVectorType& permutationP() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_p;
+    }
+
+    inline const IntRowVectorType& permutationQ() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_q;
+    }
+
+    /** Computes the sparse Cholesky decomposition of \a matrix 
+     *  Note that the matrix should be column-major, and in compressed format for best performance.
+     *  \sa SparseMatrix::makeCompressed().
+     */
+    void compute(const MatrixType& matrix)
+    {
+      analyzePattern(matrix);
+      factorize(matrix);
+    }
+
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const internal::solve_retval<UmfPackLU, Rhs> solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "UmfPackLU is not initialized.");
+      eigen_assert(rows()==b.rows()
+                && "UmfPackLU::solve(): invalid number of rows of the right hand side matrix b");
+      return internal::solve_retval<UmfPackLU, Rhs>(*this, b.derived());
+    }
+
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+//     template<typename Rhs>
+//     inline const internal::sparse_solve_retval<UmfPAckLU, Rhs> solve(const SparseMatrixBase<Rhs>& b) const
+//     {
+//       eigen_assert(m_isInitialized && "UmfPAckLU is not initialized.");
+//       eigen_assert(rows()==b.rows()
+//                 && "UmfPAckLU::solve(): invalid number of rows of the right hand side matrix b");
+//       return internal::sparse_solve_retval<UmfPAckLU, Rhs>(*this, b.derived());
+//     }
+
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      *
+      * \sa factorize(), compute()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    {
+      if(m_symbolic)
+        umfpack_free_symbolic(&m_symbolic,Scalar());
+      if(m_numeric)
+        umfpack_free_numeric(&m_numeric,Scalar());
+      
+      grapInput(matrix);
+
+      int errorCode = 0;
+      errorCode = umfpack_symbolic(matrix.rows(), matrix.cols(), m_outerIndexPtr, m_innerIndexPtr, m_valuePtr,
+                                   &m_symbolic, 0, 0);
+
+      m_isInitialized = true;
+      m_info = errorCode ? InvalidInput : Success;
+      m_analysisIsOk = true;
+      m_factorizationIsOk = false;
+    }
+
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.
+      *
+      * \sa analyzePattern(), compute()
+      */
+    void factorize(const MatrixType& matrix)
+    {
+      eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
+      if(m_numeric)
+        umfpack_free_numeric(&m_numeric,Scalar());
+
+      grapInput(matrix);
+
+      int errorCode;
+      errorCode = umfpack_numeric(m_outerIndexPtr, m_innerIndexPtr, m_valuePtr,
+                                  m_symbolic, &m_numeric, 0, 0);
+
+      m_info = errorCode ? NumericalIssue : Success;
+      m_factorizationIsOk = true;
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal */
+    template<typename BDerived,typename XDerived>
+    bool _solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
+    #endif
+
+    Scalar determinant() const;
+
+    void extractData() const;
+
+  protected:
+
+
+    void init()
+    {
+      m_info = InvalidInput;
+      m_isInitialized = false;
+      m_numeric = 0;
+      m_symbolic = 0;
+      m_outerIndexPtr = 0;
+      m_innerIndexPtr = 0;
+      m_valuePtr      = 0;
+    }
+    
+    void grapInput(const MatrixType& mat)
+    {
+      m_copyMatrix.resize(mat.rows(), mat.cols());
+      if( ((MatrixType::Flags&RowMajorBit)==RowMajorBit) || sizeof(typename MatrixType::Index)!=sizeof(int) || !mat.isCompressed() )
+      {
+        // non supported input -> copy
+        m_copyMatrix = mat;
+        m_outerIndexPtr = m_copyMatrix.outerIndexPtr();
+        m_innerIndexPtr = m_copyMatrix.innerIndexPtr();
+        m_valuePtr      = m_copyMatrix.valuePtr();
+      }
+      else
+      {
+        m_outerIndexPtr = mat.outerIndexPtr();
+        m_innerIndexPtr = mat.innerIndexPtr();
+        m_valuePtr      = mat.valuePtr();
+      }
+    }
+
+    // cached data to reduce reallocation, etc.
+    mutable LUMatrixType m_l;
+    mutable LUMatrixType m_u;
+    mutable IntColVectorType m_p;
+    mutable IntRowVectorType m_q;
+
+    UmfpackMatrixType m_copyMatrix;
+    const Scalar* m_valuePtr;
+    const int* m_outerIndexPtr;
+    const int* m_innerIndexPtr;
+    void* m_numeric;
+    void* m_symbolic;
+
+    mutable ComputationInfo m_info;
+    bool m_isInitialized;
+    int m_factorizationIsOk;
+    int m_analysisIsOk;
+    mutable bool m_extractedDataAreDirty;
+    
+  private:
+    UmfPackLU(UmfPackLU& ) { }
+};
+
+
+template<typename MatrixType>
+void UmfPackLU<MatrixType>::extractData() const
+{
+  if (m_extractedDataAreDirty)
+  {
+    // get size of the data
+    int lnz, unz, rows, cols, nz_udiag;
+    umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
+
+    // allocate data
+    m_l.resize(rows,(std::min)(rows,cols));
+    m_l.resizeNonZeros(lnz);
+
+    m_u.resize((std::min)(rows,cols),cols);
+    m_u.resizeNonZeros(unz);
+
+    m_p.resize(rows);
+    m_q.resize(cols);
+
+    // extract
+    umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(),
+                        m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(),
+                        m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
+
+    m_extractedDataAreDirty = false;
+  }
+}
+
+template<typename MatrixType>
+typename UmfPackLU<MatrixType>::Scalar UmfPackLU<MatrixType>::determinant() const
+{
+  Scalar det;
+  umfpack_get_determinant(&det, 0, m_numeric, 0);
+  return det;
+}
+
+template<typename MatrixType>
+template<typename BDerived,typename XDerived>
+bool UmfPackLU<MatrixType>::_solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const
+{
+  const int rhsCols = b.cols();
+  eigen_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major rhs yet");
+  eigen_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major result yet");
+
+  int errorCode;
+  for (int j=0; j<rhsCols; ++j)
+  {
+    errorCode = umfpack_solve(UMFPACK_A,
+        m_outerIndexPtr, m_innerIndexPtr, m_valuePtr,
+        &x.col(j).coeffRef(0), &b.const_cast_derived().col(j).coeffRef(0), m_numeric, 0, 0);
+    if (errorCode!=0)
+      return false;
+  }
+
+  return true;
+}
+
+
+namespace internal {
+
+template<typename _MatrixType, typename Rhs>
+struct solve_retval<UmfPackLU<_MatrixType>, Rhs>
+  : solve_retval_base<UmfPackLU<_MatrixType>, Rhs>
+{
+  typedef UmfPackLU<_MatrixType> Dec;
+  EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve(rhs(),dst);
+  }
+};
+
+template<typename _MatrixType, typename Rhs>
+struct sparse_solve_retval<UmfPackLU<_MatrixType>, Rhs>
+  : sparse_solve_retval_base<UmfPackLU<_MatrixType>, Rhs>
+{
+  typedef UmfPackLU<_MatrixType> Dec;
+  EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    dec()._solve(rhs(),dst);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_UMFPACKSUPPORT_H
diff --git a/extern/Eigen3/Eigen/src/misc/CMakeLists.txt b/extern/Eigen3/Eigen/src/misc/CMakeLists.txt
new file mode 100644
index 00000000000..a58ffb7457e
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/misc/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_misc_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_misc_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/misc COMPONENT Devel
+  )
diff --git a/extern/Eigen3/Eigen/src/misc/SparseSolve.h b/extern/Eigen3/Eigen/src/misc/SparseSolve.h
new file mode 100644
index 00000000000..aca34b2d1c4
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/misc/SparseSolve.h
@@ -0,0 +1,126 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SPARSE_SOLVE_H
+#define EIGEN_SPARSE_SOLVE_H
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename _DecompositionType, typename Rhs> struct sparse_solve_retval_base;
+template<typename _DecompositionType, typename Rhs> struct sparse_solve_retval;
+  
+template<typename DecompositionType, typename Rhs>
+struct traits<sparse_solve_retval_base<DecompositionType, Rhs> >
+{
+  typedef typename DecompositionType::MatrixType MatrixType;
+  typedef SparseMatrix<typename Rhs::Scalar, Rhs::Options, typename Rhs::Index> ReturnType;
+};
+
+template<typename _DecompositionType, typename Rhs> struct sparse_solve_retval_base
+ : public ReturnByValue<sparse_solve_retval_base<_DecompositionType, Rhs> >
+{
+  typedef typename remove_all<typename Rhs::Nested>::type RhsNestedCleaned;
+  typedef _DecompositionType DecompositionType;
+  typedef ReturnByValue<sparse_solve_retval_base> Base;
+  typedef typename Base::Index Index;
+
+  sparse_solve_retval_base(const DecompositionType& dec, const Rhs& rhs)
+    : m_dec(dec), m_rhs(rhs)
+  {}
+
+  inline Index rows() const { return m_dec.cols(); }
+  inline Index cols() const { return m_rhs.cols(); }
+  inline const DecompositionType& dec() const { return m_dec; }
+  inline const RhsNestedCleaned& rhs() const { return m_rhs; }
+
+  template<typename Dest> inline void evalTo(Dest& dst) const
+  {
+    static_cast<const sparse_solve_retval<DecompositionType,Rhs>*>(this)->evalTo(dst);
+  }
+
+  protected:
+    const DecompositionType& m_dec;
+    typename Rhs::Nested m_rhs;
+};
+
+#define EIGEN_MAKE_SPARSE_SOLVE_HELPERS(DecompositionType,Rhs) \
+  typedef typename DecompositionType::MatrixType MatrixType; \
+  typedef typename MatrixType::Scalar Scalar; \
+  typedef typename MatrixType::RealScalar RealScalar; \
+  typedef typename MatrixType::Index Index; \
+  typedef Eigen::internal::sparse_solve_retval_base<DecompositionType,Rhs> Base; \
+  using Base::dec; \
+  using Base::rhs; \
+  using Base::rows; \
+  using Base::cols; \
+  sparse_solve_retval(const DecompositionType& dec, const Rhs& rhs) \
+    : Base(dec, rhs) {}
+
+
+
+template<typename DecompositionType, typename Rhs, typename Guess> struct solve_retval_with_guess;
+
+template<typename DecompositionType, typename Rhs, typename Guess>
+struct traits<solve_retval_with_guess<DecompositionType, Rhs, Guess> >
+{
+  typedef typename DecompositionType::MatrixType MatrixType;
+  typedef Matrix<typename Rhs::Scalar,
+                 MatrixType::ColsAtCompileTime,
+                 Rhs::ColsAtCompileTime,
+                 Rhs::PlainObject::Options,
+                 MatrixType::MaxColsAtCompileTime,
+                 Rhs::MaxColsAtCompileTime> ReturnType;
+};
+
+template<typename DecompositionType, typename Rhs, typename Guess> struct solve_retval_with_guess
+ : public ReturnByValue<solve_retval_with_guess<DecompositionType, Rhs, Guess> >
+{
+  typedef typename DecompositionType::Index Index;
+
+  solve_retval_with_guess(const DecompositionType& dec, const Rhs& rhs, const Guess& guess)
+    : m_dec(dec), m_rhs(rhs), m_guess(guess)
+  {}
+
+  inline Index rows() const { return m_dec.cols(); }
+  inline Index cols() const { return m_rhs.cols(); }
+
+  template<typename Dest> inline void evalTo(Dest& dst) const
+  {
+    dst = m_guess;
+    m_dec._solveWithGuess(m_rhs,dst);
+  }
+
+  protected:
+    const DecompositionType& m_dec;
+    const typename Rhs::Nested m_rhs;
+    const typename Guess::Nested m_guess;
+};
+
+} // namepsace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_SOLVE_H
diff --git a/extern/Eigen3/Eigen/src/misc/blas.h b/extern/Eigen3/Eigen/src/misc/blas.h
new file mode 100644
index 00000000000..6fce99ed5c4
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/misc/blas.h
@@ -0,0 +1,658 @@
+#ifndef BLAS_H
+#define BLAS_H
+
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+#define BLASFUNC(FUNC) FUNC##_
+
+#ifdef __WIN64__
+typedef long long BLASLONG;
+typedef unsigned long long BLASULONG;
+#else
+typedef long BLASLONG;
+typedef unsigned long BLASULONG;
+#endif
+
+int    BLASFUNC(xerbla)(const char *, int *info, int);
+
+float  BLASFUNC(sdot)  (int *, float  *, int *, float  *, int *);
+float  BLASFUNC(sdsdot)(int *, float  *,        float  *, int *, float  *, int *);
+
+double BLASFUNC(dsdot) (int *, float  *, int *, float  *, int *);
+double BLASFUNC(ddot)  (int *, double *, int *, double *, int *);
+double BLASFUNC(qdot)  (int *, double *, int *, double *, int *);
+
+int  BLASFUNC(cdotuw)  (int *, float  *, int *, float  *, int *, float*);
+int  BLASFUNC(cdotcw)  (int *, float  *, int *, float  *, int *, float*);
+int  BLASFUNC(zdotuw)  (int *, double  *, int *, double  *, int *, double*);
+int  BLASFUNC(zdotcw)  (int *, double  *, int *, double  *, int *, double*);
+
+int    BLASFUNC(saxpy) (int *, float  *, float  *, int *, float  *, int *);
+int    BLASFUNC(daxpy) (int *, double *, double *, int *, double *, int *);
+int    BLASFUNC(qaxpy) (int *, double *, double *, int *, double *, int *);
+int    BLASFUNC(caxpy) (int *, float  *, float  *, int *, float  *, int *);
+int    BLASFUNC(zaxpy) (int *, double *, double *, int *, double *, int *);
+int    BLASFUNC(xaxpy) (int *, double *, double *, int *, double *, int *);
+int    BLASFUNC(caxpyc)(int *, float  *, float  *, int *, float  *, int *);
+int    BLASFUNC(zaxpyc)(int *, double *, double *, int *, double *, int *);
+int    BLASFUNC(xaxpyc)(int *, double *, double *, int *, double *, int *);
+
+int    BLASFUNC(scopy) (int *, float  *, int *, float  *, int *);
+int    BLASFUNC(dcopy) (int *, double *, int *, double *, int *);
+int    BLASFUNC(qcopy) (int *, double *, int *, double *, int *);
+int    BLASFUNC(ccopy) (int *, float  *, int *, float  *, int *);
+int    BLASFUNC(zcopy) (int *, double *, int *, double *, int *);
+int    BLASFUNC(xcopy) (int *, double *, int *, double *, int *);
+
+int    BLASFUNC(sswap) (int *, float  *, int *, float  *, int *);
+int    BLASFUNC(dswap) (int *, double *, int *, double *, int *);
+int    BLASFUNC(qswap) (int *, double *, int *, double *, int *);
+int    BLASFUNC(cswap) (int *, float  *, int *, float  *, int *);
+int    BLASFUNC(zswap) (int *, double *, int *, double *, int *);
+int    BLASFUNC(xswap) (int *, double *, int *, double *, int *);
+
+float  BLASFUNC(sasum) (int *, float  *, int *);
+float  BLASFUNC(scasum)(int *, float  *, int *);
+double BLASFUNC(dasum) (int *, double *, int *);
+double BLASFUNC(qasum) (int *, double *, int *);
+double BLASFUNC(dzasum)(int *, double *, int *);
+double BLASFUNC(qxasum)(int *, double *, int *);
+
+int    BLASFUNC(isamax)(int *, float  *, int *);
+int    BLASFUNC(idamax)(int *, double *, int *);
+int    BLASFUNC(iqamax)(int *, double *, int *);
+int    BLASFUNC(icamax)(int *, float  *, int *);
+int    BLASFUNC(izamax)(int *, double *, int *);
+int    BLASFUNC(ixamax)(int *, double *, int *);
+
+int    BLASFUNC(ismax) (int *, float  *, int *);
+int    BLASFUNC(idmax) (int *, double *, int *);
+int    BLASFUNC(iqmax) (int *, double *, int *);
+int    BLASFUNC(icmax) (int *, float  *, int *);
+int    BLASFUNC(izmax) (int *, double *, int *);
+int    BLASFUNC(ixmax) (int *, double *, int *);
+
+int    BLASFUNC(isamin)(int *, float  *, int *);
+int    BLASFUNC(idamin)(int *, double *, int *);
+int    BLASFUNC(iqamin)(int *, double *, int *);
+int    BLASFUNC(icamin)(int *, float  *, int *);
+int    BLASFUNC(izamin)(int *, double *, int *);
+int    BLASFUNC(ixamin)(int *, double *, int *);
+
+int    BLASFUNC(ismin)(int *, float  *, int *);
+int    BLASFUNC(idmin)(int *, double *, int *);
+int    BLASFUNC(iqmin)(int *, double *, int *);
+int    BLASFUNC(icmin)(int *, float  *, int *);
+int    BLASFUNC(izmin)(int *, double *, int *);
+int    BLASFUNC(ixmin)(int *, double *, int *);
+
+float  BLASFUNC(samax) (int *, float  *, int *);
+double BLASFUNC(damax) (int *, double *, int *);
+double BLASFUNC(qamax) (int *, double *, int *);
+float  BLASFUNC(scamax)(int *, float  *, int *);
+double BLASFUNC(dzamax)(int *, double *, int *);
+double BLASFUNC(qxamax)(int *, double *, int *);
+
+float  BLASFUNC(samin) (int *, float  *, int *);
+double BLASFUNC(damin) (int *, double *, int *);
+double BLASFUNC(qamin) (int *, double *, int *);
+float  BLASFUNC(scamin)(int *, float  *, int *);
+double BLASFUNC(dzamin)(int *, double *, int *);
+double BLASFUNC(qxamin)(int *, double *, int *);
+
+float  BLASFUNC(smax)  (int *, float  *, int *);
+double BLASFUNC(dmax)  (int *, double *, int *);
+double BLASFUNC(qmax)  (int *, double *, int *);
+float  BLASFUNC(scmax) (int *, float  *, int *);
+double BLASFUNC(dzmax) (int *, double *, int *);
+double BLASFUNC(qxmax) (int *, double *, int *);
+
+float  BLASFUNC(smin)  (int *, float  *, int *);
+double BLASFUNC(dmin)  (int *, double *, int *);
+double BLASFUNC(qmin)  (int *, double *, int *);
+float  BLASFUNC(scmin) (int *, float  *, int *);
+double BLASFUNC(dzmin) (int *, double *, int *);
+double BLASFUNC(qxmin) (int *, double *, int *);
+
+int    BLASFUNC(sscal) (int *,  float  *, float  *, int *);
+int    BLASFUNC(dscal) (int *,  double *, double *, int *);
+int    BLASFUNC(qscal) (int *,  double *, double *, int *);
+int    BLASFUNC(cscal) (int *,  float  *, float  *, int *);
+int    BLASFUNC(zscal) (int *,  double *, double *, int *);
+int    BLASFUNC(xscal) (int *,  double *, double *, int *);
+int    BLASFUNC(csscal)(int *,  float  *, float  *, int *);
+int    BLASFUNC(zdscal)(int *,  double *, double *, int *);
+int    BLASFUNC(xqscal)(int *,  double *, double *, int *);
+
+float  BLASFUNC(snrm2) (int *, float  *, int *);
+float  BLASFUNC(scnrm2)(int *, float  *, int *);
+
+double BLASFUNC(dnrm2) (int *, double *, int *);
+double BLASFUNC(qnrm2) (int *, double *, int *);
+double BLASFUNC(dznrm2)(int *, double *, int *);
+double BLASFUNC(qxnrm2)(int *, double *, int *);
+
+int    BLASFUNC(srot)  (int *, float  *, int *, float  *, int *, float  *, float  *);
+int    BLASFUNC(drot)  (int *, double *, int *, double *, int *, double *, double *);
+int    BLASFUNC(qrot)  (int *, double *, int *, double *, int *, double *, double *);
+int    BLASFUNC(csrot) (int *, float  *, int *, float  *, int *, float  *, float  *);
+int    BLASFUNC(zdrot) (int *, double *, int *, double *, int *, double *, double *);
+int    BLASFUNC(xqrot) (int *, double *, int *, double *, int *, double *, double *);
+
+int    BLASFUNC(srotg) (float  *, float  *, float  *, float  *);
+int    BLASFUNC(drotg) (double *, double *, double *, double *);
+int    BLASFUNC(qrotg) (double *, double *, double *, double *);
+int    BLASFUNC(crotg) (float  *, float  *, float  *, float  *);
+int    BLASFUNC(zrotg) (double *, double *, double *, double *);
+int    BLASFUNC(xrotg) (double *, double *, double *, double *);
+
+int    BLASFUNC(srotmg)(float  *, float  *, float  *, float  *, float  *);
+int    BLASFUNC(drotmg)(double *, double *, double *, double *, double *);
+
+int    BLASFUNC(srotm) (int *, float  *, int *, float  *, int *, float  *);
+int    BLASFUNC(drotm) (int *, double *, int *, double *, int *, double *);
+int    BLASFUNC(qrotm) (int *, double *, int *, double *, int *, double *);
+
+/* Level 2 routines */
+
+int BLASFUNC(sger)(int *,    int *, float *,  float *, int *,
+		   float *,  int *, float *,  int *);
+int BLASFUNC(dger)(int *,    int *, double *, double *, int *,
+		   double *, int *, double *, int *);
+int BLASFUNC(qger)(int *,    int *, double *, double *, int *,
+		   double *, int *, double *, int *);
+int BLASFUNC(cgeru)(int *,    int *, float *,  float *, int *,
+		    float *,  int *, float *,  int *);
+int BLASFUNC(cgerc)(int *,    int *, float *,  float *, int *,
+		    float *,  int *, float *,  int *);
+int BLASFUNC(zgeru)(int *,    int *, double *, double *, int *,
+		    double *, int *, double *, int *);
+int BLASFUNC(zgerc)(int *,    int *, double *, double *, int *,
+		    double *, int *, double *, int *);
+int BLASFUNC(xgeru)(int *,    int *, double *, double *, int *,
+		    double *, int *, double *, int *);
+int BLASFUNC(xgerc)(int *,    int *, double *, double *, int *,
+		    double *, int *, double *, int *);
+
+int BLASFUNC(sgemv)(char *, int *, int *, float  *, float  *, int *,
+		    float  *, int *, float  *, float  *, int *);
+int BLASFUNC(dgemv)(char *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+int BLASFUNC(qgemv)(char *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+int BLASFUNC(cgemv)(char *, int *, int *, float  *, float  *, int *,
+		    float  *, int *, float  *, float  *, int *);
+int BLASFUNC(zgemv)(char *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+int BLASFUNC(xgemv)(char *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+
+int BLASFUNC(strsv) (char *, char *, char *, int *, float  *, int *,
+		     float  *, int *);
+int BLASFUNC(dtrsv) (char *, char *, char *, int *, double *, int *,
+		     double *, int *);
+int BLASFUNC(qtrsv) (char *, char *, char *, int *, double *, int *,
+		     double *, int *);
+int BLASFUNC(ctrsv) (char *, char *, char *, int *, float  *, int *,
+		     float  *, int *);
+int BLASFUNC(ztrsv) (char *, char *, char *, int *, double *, int *,
+		     double *, int *);
+int BLASFUNC(xtrsv) (char *, char *, char *, int *, double *, int *,
+		     double *, int *);
+
+int BLASFUNC(stpsv) (char *, char *, char *, int *, float  *, float  *, int *);
+int BLASFUNC(dtpsv) (char *, char *, char *, int *, double *, double *, int *);
+int BLASFUNC(qtpsv) (char *, char *, char *, int *, double *, double *, int *);
+int BLASFUNC(ctpsv) (char *, char *, char *, int *, float  *, float  *, int *);
+int BLASFUNC(ztpsv) (char *, char *, char *, int *, double *, double *, int *);
+int BLASFUNC(xtpsv) (char *, char *, char *, int *, double *, double *, int *);
+
+int BLASFUNC(strmv) (char *, char *, char *, int *, float  *, int *,
+		     float  *, int *);
+int BLASFUNC(dtrmv) (char *, char *, char *, int *, double *, int *,
+		     double *, int *);
+int BLASFUNC(qtrmv) (char *, char *, char *, int *, double *, int *,
+		     double *, int *);
+int BLASFUNC(ctrmv) (char *, char *, char *, int *, float  *, int *,
+		     float  *, int *);
+int BLASFUNC(ztrmv) (char *, char *, char *, int *, double *, int *,
+		     double *, int *);
+int BLASFUNC(xtrmv) (char *, char *, char *, int *, double *, int *,
+		     double *, int *);
+
+int BLASFUNC(stpmv) (char *, char *, char *, int *, float  *, float  *, int *);
+int BLASFUNC(dtpmv) (char *, char *, char *, int *, double *, double *, int *);
+int BLASFUNC(qtpmv) (char *, char *, char *, int *, double *, double *, int *);
+int BLASFUNC(ctpmv) (char *, char *, char *, int *, float  *, float  *, int *);
+int BLASFUNC(ztpmv) (char *, char *, char *, int *, double *, double *, int *);
+int BLASFUNC(xtpmv) (char *, char *, char *, int *, double *, double *, int *);
+
+int BLASFUNC(stbmv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
+int BLASFUNC(dtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
+int BLASFUNC(qtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
+int BLASFUNC(ctbmv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
+int BLASFUNC(ztbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
+int BLASFUNC(xtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
+
+int BLASFUNC(stbsv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
+int BLASFUNC(dtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
+int BLASFUNC(qtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
+int BLASFUNC(ctbsv) (char *, char *, char *, int *, int *, float  *, int *, float  *, int *);
+int BLASFUNC(ztbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
+int BLASFUNC(xtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *);
+
+int BLASFUNC(ssymv) (char *, int *, float  *, float *, int *,
+		     float  *, int *, float *, float *, int *);
+int BLASFUNC(dsymv) (char *, int *, double  *, double *, int *,
+		     double  *, int *, double *, double *, int *);
+int BLASFUNC(qsymv) (char *, int *, double  *, double *, int *,
+		     double  *, int *, double *, double *, int *);
+int BLASFUNC(csymv) (char *, int *, float  *, float *, int *,
+		     float  *, int *, float *, float *, int *);
+int BLASFUNC(zsymv) (char *, int *, double  *, double *, int *,
+		     double  *, int *, double *, double *, int *);
+int BLASFUNC(xsymv) (char *, int *, double  *, double *, int *,
+		     double  *, int *, double *, double *, int *);
+
+int BLASFUNC(sspmv) (char *, int *, float  *, float *,
+		     float  *, int *, float *, float *, int *);
+int BLASFUNC(dspmv) (char *, int *, double  *, double *,
+		     double  *, int *, double *, double *, int *);
+int BLASFUNC(qspmv) (char *, int *, double  *, double *,
+		     double  *, int *, double *, double *, int *);
+int BLASFUNC(cspmv) (char *, int *, float  *, float *,
+		     float  *, int *, float *, float *, int *);
+int BLASFUNC(zspmv) (char *, int *, double  *, double *,
+		     double  *, int *, double *, double *, int *);
+int BLASFUNC(xspmv) (char *, int *, double  *, double *,
+		     double  *, int *, double *, double *, int *);
+
+int BLASFUNC(ssyr) (char *, int *, float   *, float  *, int *,
+		    float  *, int *);
+int BLASFUNC(dsyr) (char *, int *, double  *, double *, int *,
+		    double *, int *);
+int BLASFUNC(qsyr) (char *, int *, double  *, double *, int *,
+		    double *, int *);
+int BLASFUNC(csyr) (char *, int *, float   *, float  *, int *,
+		    float  *, int *);
+int BLASFUNC(zsyr) (char *, int *, double  *, double *, int *,
+		    double *, int *);
+int BLASFUNC(xsyr) (char *, int *, double  *, double *, int *,
+		    double *, int *);
+
+int BLASFUNC(ssyr2) (char *, int *, float   *,
+		     float  *, int *, float  *, int *, float  *, int *);
+int BLASFUNC(dsyr2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *, int *);
+int BLASFUNC(qsyr2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *, int *);
+int BLASFUNC(csyr2) (char *, int *, float   *,
+		     float  *, int *, float  *, int *, float  *, int *);
+int BLASFUNC(zsyr2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *, int *);
+int BLASFUNC(xsyr2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *, int *);
+
+int BLASFUNC(sspr) (char *, int *, float   *, float  *, int *,
+		    float  *);
+int BLASFUNC(dspr) (char *, int *, double  *, double *, int *,
+		    double *);
+int BLASFUNC(qspr) (char *, int *, double  *, double *, int *,
+		    double *);
+int BLASFUNC(cspr) (char *, int *, float   *, float  *, int *,
+		    float  *);
+int BLASFUNC(zspr) (char *, int *, double  *, double *, int *,
+		    double *);
+int BLASFUNC(xspr) (char *, int *, double  *, double *, int *,
+		    double *);
+
+int BLASFUNC(sspr2) (char *, int *, float   *,
+		     float  *, int *, float  *, int *, float  *);
+int BLASFUNC(dspr2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *);
+int BLASFUNC(qspr2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *);
+int BLASFUNC(cspr2) (char *, int *, float   *,
+		     float  *, int *, float  *, int *, float  *);
+int BLASFUNC(zspr2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *);
+int BLASFUNC(xspr2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *);
+
+int BLASFUNC(cher) (char *, int *, float   *, float  *, int *,
+		    float  *, int *);
+int BLASFUNC(zher) (char *, int *, double  *, double *, int *,
+		    double *, int *);
+int BLASFUNC(xher) (char *, int *, double  *, double *, int *,
+		    double *, int *);
+
+int BLASFUNC(chpr) (char *, int *, float   *, float  *, int *, float  *);
+int BLASFUNC(zhpr) (char *, int *, double  *, double *, int *, double *);
+int BLASFUNC(xhpr) (char *, int *, double  *, double *, int *, double *);
+
+int BLASFUNC(cher2) (char *, int *, float   *,
+		     float  *, int *, float  *, int *, float  *, int *);
+int BLASFUNC(zher2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *, int *);
+int BLASFUNC(xher2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *, int *);
+
+int BLASFUNC(chpr2) (char *, int *, float   *,
+		     float  *, int *, float  *, int *, float  *);
+int BLASFUNC(zhpr2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *);
+int BLASFUNC(xhpr2) (char *, int *, double  *,
+		     double *, int *, double *, int *, double *);
+
+int BLASFUNC(chemv) (char *, int *, float  *, float *, int *,
+		     float  *, int *, float *, float *, int *);
+int BLASFUNC(zhemv) (char *, int *, double  *, double *, int *,
+		     double  *, int *, double *, double *, int *);
+int BLASFUNC(xhemv) (char *, int *, double  *, double *, int *,
+		     double  *, int *, double *, double *, int *);
+
+int BLASFUNC(chpmv) (char *, int *, float  *, float *,
+		     float  *, int *, float *, float *, int *);
+int BLASFUNC(zhpmv) (char *, int *, double  *, double *,
+		     double  *, int *, double *, double *, int *);
+int BLASFUNC(xhpmv) (char *, int *, double  *, double *,
+		     double  *, int *, double *, double *, int *);
+
+int BLASFUNC(snorm)(char *, int *, int *, float  *, int *);
+int BLASFUNC(dnorm)(char *, int *, int *, double *, int *);
+int BLASFUNC(cnorm)(char *, int *, int *, float  *, int *);
+int BLASFUNC(znorm)(char *, int *, int *, double *, int *);
+
+int BLASFUNC(sgbmv)(char *, int *, int *, int *, int *, float  *, float  *, int *,
+		    float  *, int *, float  *, float  *, int *);
+int BLASFUNC(dgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+int BLASFUNC(qgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+int BLASFUNC(cgbmv)(char *, int *, int *, int *, int *, float  *, float  *, int *,
+		    float  *, int *, float  *, float  *, int *);
+int BLASFUNC(zgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+int BLASFUNC(xgbmv)(char *, int *, int *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+
+int BLASFUNC(ssbmv)(char *, int *, int *, float  *, float  *, int *,
+		    float  *, int *, float  *, float  *, int *);
+int BLASFUNC(dsbmv)(char *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+int BLASFUNC(qsbmv)(char *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+int BLASFUNC(csbmv)(char *, int *, int *, float  *, float  *, int *,
+		    float  *, int *, float  *, float  *, int *);
+int BLASFUNC(zsbmv)(char *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+int BLASFUNC(xsbmv)(char *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+
+int BLASFUNC(chbmv)(char *, int *, int *, float  *, float  *, int *,
+		    float  *, int *, float  *, float  *, int *);
+int BLASFUNC(zhbmv)(char *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+int BLASFUNC(xhbmv)(char *, int *, int *, double *, double *, int *,
+		    double *, int *, double *, double *, int *);
+
+/* Level 3 routines */
+
+int BLASFUNC(sgemm)(char *, char *, int *, int *, int *, float *,
+	   float  *, int *, float  *, int *, float  *, float  *, int *);
+int BLASFUNC(dgemm)(char *, char *, int *, int *, int *, double *,
+	   double *, int *, double *, int *, double *, double *, int *);
+int BLASFUNC(qgemm)(char *, char *, int *, int *, int *, double *,
+	   double *, int *, double *, int *, double *, double *, int *);
+int BLASFUNC(cgemm)(char *, char *, int *, int *, int *, float *,
+	   float  *, int *, float  *, int *, float  *, float  *, int *);
+int BLASFUNC(zgemm)(char *, char *, int *, int *, int *, double *,
+	   double *, int *, double *, int *, double *, double *, int *);
+int BLASFUNC(xgemm)(char *, char *, int *, int *, int *, double *,
+	   double *, int *, double *, int *, double *, double *, int *);
+
+int BLASFUNC(cgemm3m)(char *, char *, int *, int *, int *, float *,
+	   float  *, int *, float  *, int *, float  *, float  *, int *);
+int BLASFUNC(zgemm3m)(char *, char *, int *, int *, int *, double *,
+	   double *, int *, double *, int *, double *, double *, int *);
+int BLASFUNC(xgemm3m)(char *, char *, int *, int *, int *, double *,
+	   double *, int *, double *, int *, double *, double *, int *);
+
+int BLASFUNC(sge2mm)(char *, char *, char *, int *, int *,
+		     float *, float  *, int *, float  *, int *,
+		     float *, float  *, int *);
+int BLASFUNC(dge2mm)(char *, char *, char *, int *, int *,
+		     double *, double  *, int *, double  *, int *,
+		     double *, double  *, int *);
+int BLASFUNC(cge2mm)(char *, char *, char *, int *, int *,
+		     float *, float  *, int *, float  *, int *,
+		     float *, float  *, int *);
+int BLASFUNC(zge2mm)(char *, char *, char *, int *, int *,
+		     double *, double  *, int *, double  *, int *,
+		     double *, double  *, int *);
+
+int BLASFUNC(strsm)(char *, char *, char *, char *, int *, int *,
+	   float *,  float *, int *, float *, int *);
+int BLASFUNC(dtrsm)(char *, char *, char *, char *, int *, int *,
+	   double *,  double *, int *, double *, int *);
+int BLASFUNC(qtrsm)(char *, char *, char *, char *, int *, int *,
+	   double *,  double *, int *, double *, int *);
+int BLASFUNC(ctrsm)(char *, char *, char *, char *, int *, int *,
+	   float *,  float *, int *, float *, int *);
+int BLASFUNC(ztrsm)(char *, char *, char *, char *, int *, int *,
+	   double *,  double *, int *, double *, int *);
+int BLASFUNC(xtrsm)(char *, char *, char *, char *, int *, int *,
+	   double *,  double *, int *, double *, int *);
+
+int BLASFUNC(strmm)(char *, char *, char *, char *, int *, int *,
+	   float *,  float *, int *, float *, int *);
+int BLASFUNC(dtrmm)(char *, char *, char *, char *, int *, int *,
+	   double *,  double *, int *, double *, int *);
+int BLASFUNC(qtrmm)(char *, char *, char *, char *, int *, int *,
+	   double *,  double *, int *, double *, int *);
+int BLASFUNC(ctrmm)(char *, char *, char *, char *, int *, int *,
+	   float *,  float *, int *, float *, int *);
+int BLASFUNC(ztrmm)(char *, char *, char *, char *, int *, int *,
+	   double *,  double *, int *, double *, int *);
+int BLASFUNC(xtrmm)(char *, char *, char *, char *, int *, int *,
+	   double *,  double *, int *, double *, int *);
+
+int BLASFUNC(ssymm)(char *, char *, int *, int *, float  *, float  *, int *,
+	   float  *, int *, float  *, float  *, int *);
+int BLASFUNC(dsymm)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, int *, double *, double *, int *);
+int BLASFUNC(qsymm)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, int *, double *, double *, int *);
+int BLASFUNC(csymm)(char *, char *, int *, int *, float  *, float  *, int *,
+	   float  *, int *, float  *, float  *, int *);
+int BLASFUNC(zsymm)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, int *, double *, double *, int *);
+int BLASFUNC(xsymm)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, int *, double *, double *, int *);
+
+int BLASFUNC(csymm3m)(char *, char *, int *, int *, float  *, float  *, int *,
+	   float  *, int *, float  *, float  *, int *);
+int BLASFUNC(zsymm3m)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, int *, double *, double *, int *);
+int BLASFUNC(xsymm3m)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, int *, double *, double *, int *);
+
+int BLASFUNC(ssyrk)(char *, char *, int *, int *, float  *, float  *, int *,
+	   float  *, float  *, int *);
+int BLASFUNC(dsyrk)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, double *, int *);
+int BLASFUNC(qsyrk)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, double *, int *);
+int BLASFUNC(csyrk)(char *, char *, int *, int *, float  *, float  *, int *,
+	   float  *, float  *, int *);
+int BLASFUNC(zsyrk)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, double *, int *);
+int BLASFUNC(xsyrk)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, double *, int *);
+
+int BLASFUNC(ssyr2k)(char *, char *, int *, int *, float  *, float  *, int *,
+	   float *, int *, float  *, float  *, int *);
+int BLASFUNC(dsyr2k)(char *, char *, int *, int *, double *, double *, int *,
+	   double*, int *, double *, double *, int *);
+int BLASFUNC(qsyr2k)(char *, char *, int *, int *, double *, double *, int *,
+	   double*, int *, double *, double *, int *);
+int BLASFUNC(csyr2k)(char *, char *, int *, int *, float  *, float  *, int *,
+	   float *, int *, float  *, float  *, int *);
+int BLASFUNC(zsyr2k)(char *, char *, int *, int *, double *, double *, int *,
+	   double*, int *, double *, double *, int *);
+int BLASFUNC(xsyr2k)(char *, char *, int *, int *, double *, double *, int *,
+	   double*, int *, double *, double *, int *);
+
+int BLASFUNC(chemm)(char *, char *, int *, int *, float  *, float  *, int *,
+	   float  *, int *, float  *, float  *, int *);
+int BLASFUNC(zhemm)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, int *, double *, double *, int *);
+int BLASFUNC(xhemm)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, int *, double *, double *, int *);
+
+int BLASFUNC(chemm3m)(char *, char *, int *, int *, float  *, float  *, int *,
+	   float  *, int *, float  *, float  *, int *);
+int BLASFUNC(zhemm3m)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, int *, double *, double *, int *);
+int BLASFUNC(xhemm3m)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, int *, double *, double *, int *);
+
+int BLASFUNC(cherk)(char *, char *, int *, int *, float  *, float  *, int *,
+	   float  *, float  *, int *);
+int BLASFUNC(zherk)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, double *, int *);
+int BLASFUNC(xherk)(char *, char *, int *, int *, double *, double *, int *,
+	   double *, double *, int *);
+
+int BLASFUNC(cher2k)(char *, char *, int *, int *, float  *, float  *, int *,
+	   float *, int *, float  *, float  *, int *);
+int BLASFUNC(zher2k)(char *, char *, int *, int *, double *, double *, int *,
+	   double*, int *, double *, double *, int *);
+int BLASFUNC(xher2k)(char *, char *, int *, int *, double *, double *, int *,
+	   double*, int *, double *, double *, int *);
+int BLASFUNC(cher2m)(char *, char *, char *, int *, int *, float  *, float  *, int *,
+	   float *, int *, float  *, float  *, int *);
+int BLASFUNC(zher2m)(char *, char *, char *, int *, int *, double *, double *, int *,
+	   double*, int *, double *, double *, int *);
+int BLASFUNC(xher2m)(char *, char *, char *, int *, int *, double *, double *, int *,
+	   double*, int *, double *, double *, int *);
+
+int BLASFUNC(sgemt)(char *, int *, int *, float  *, float  *, int *,
+		    float  *, int *);
+int BLASFUNC(dgemt)(char *, int *, int *, double *, double *, int *,
+		    double *, int *);
+int BLASFUNC(cgemt)(char *, int *, int *, float  *, float  *, int *,
+		    float  *, int *);
+int BLASFUNC(zgemt)(char *, int *, int *, double *, double *, int *,
+		    double *, int *);
+
+int BLASFUNC(sgema)(char *, char *, int *, int *, float  *,
+		    float  *, int *, float *, float  *, int *, float *, int *);
+int BLASFUNC(dgema)(char *, char *, int *, int *, double *,
+		    double *, int *, double*, double *, int *, double*, int *);
+int BLASFUNC(cgema)(char *, char *, int *, int *, float  *,
+		    float  *, int *, float *, float  *, int *, float *, int *);
+int BLASFUNC(zgema)(char *, char *, int *, int *, double *,
+		    double *, int *, double*, double *, int *, double*, int *);
+
+int BLASFUNC(sgems)(char *, char *, int *, int *, float  *,
+		    float  *, int *, float *, float  *, int *, float *, int *);
+int BLASFUNC(dgems)(char *, char *, int *, int *, double *,
+		    double *, int *, double*, double *, int *, double*, int *);
+int BLASFUNC(cgems)(char *, char *, int *, int *, float  *,
+		    float  *, int *, float *, float  *, int *, float *, int *);
+int BLASFUNC(zgems)(char *, char *, int *, int *, double *,
+		    double *, int *, double*, double *, int *, double*, int *);
+
+int BLASFUNC(sgetf2)(int *, int *, float  *, int *, int *, int *);
+int BLASFUNC(dgetf2)(int *, int *, double *, int *, int *, int *);
+int BLASFUNC(qgetf2)(int *, int *, double *, int *, int *, int *);
+int BLASFUNC(cgetf2)(int *, int *, float  *, int *, int *, int *);
+int BLASFUNC(zgetf2)(int *, int *, double *, int *, int *, int *);
+int BLASFUNC(xgetf2)(int *, int *, double *, int *, int *, int *);
+
+int BLASFUNC(sgetrf)(int *, int *, float  *, int *, int *, int *);
+int BLASFUNC(dgetrf)(int *, int *, double *, int *, int *, int *);
+int BLASFUNC(qgetrf)(int *, int *, double *, int *, int *, int *);
+int BLASFUNC(cgetrf)(int *, int *, float  *, int *, int *, int *);
+int BLASFUNC(zgetrf)(int *, int *, double *, int *, int *, int *);
+int BLASFUNC(xgetrf)(int *, int *, double *, int *, int *, int *);
+
+int BLASFUNC(slaswp)(int *, float  *, int *, int *, int *, int *, int *);
+int BLASFUNC(dlaswp)(int *, double *, int *, int *, int *, int *, int *);
+int BLASFUNC(qlaswp)(int *, double *, int *, int *, int *, int *, int *);
+int BLASFUNC(claswp)(int *, float  *, int *, int *, int *, int *, int *);
+int BLASFUNC(zlaswp)(int *, double *, int *, int *, int *, int *, int *);
+int BLASFUNC(xlaswp)(int *, double *, int *, int *, int *, int *, int *);
+
+int BLASFUNC(sgetrs)(char *, int *, int *, float  *, int *, int *, float  *, int *, int *);
+int BLASFUNC(dgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
+int BLASFUNC(qgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
+int BLASFUNC(cgetrs)(char *, int *, int *, float  *, int *, int *, float  *, int *, int *);
+int BLASFUNC(zgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
+int BLASFUNC(xgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *);
+
+int BLASFUNC(sgesv)(int *, int *, float  *, int *, int *, float *, int *, int *);
+int BLASFUNC(dgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
+int BLASFUNC(qgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
+int BLASFUNC(cgesv)(int *, int *, float  *, int *, int *, float *, int *, int *);
+int BLASFUNC(zgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
+int BLASFUNC(xgesv)(int *, int *, double *, int *, int *, double*, int *, int *);
+
+int BLASFUNC(spotf2)(char *, int *, float  *, int *, int *);
+int BLASFUNC(dpotf2)(char *, int *, double *, int *, int *);
+int BLASFUNC(qpotf2)(char *, int *, double *, int *, int *);
+int BLASFUNC(cpotf2)(char *, int *, float  *, int *, int *);
+int BLASFUNC(zpotf2)(char *, int *, double *, int *, int *);
+int BLASFUNC(xpotf2)(char *, int *, double *, int *, int *);
+
+int BLASFUNC(spotrf)(char *, int *, float  *, int *, int *);
+int BLASFUNC(dpotrf)(char *, int *, double *, int *, int *);
+int BLASFUNC(qpotrf)(char *, int *, double *, int *, int *);
+int BLASFUNC(cpotrf)(char *, int *, float  *, int *, int *);
+int BLASFUNC(zpotrf)(char *, int *, double *, int *, int *);
+int BLASFUNC(xpotrf)(char *, int *, double *, int *, int *);
+
+int BLASFUNC(slauu2)(char *, int *, float  *, int *, int *);
+int BLASFUNC(dlauu2)(char *, int *, double *, int *, int *);
+int BLASFUNC(qlauu2)(char *, int *, double *, int *, int *);
+int BLASFUNC(clauu2)(char *, int *, float  *, int *, int *);
+int BLASFUNC(zlauu2)(char *, int *, double *, int *, int *);
+int BLASFUNC(xlauu2)(char *, int *, double *, int *, int *);
+
+int BLASFUNC(slauum)(char *, int *, float  *, int *, int *);
+int BLASFUNC(dlauum)(char *, int *, double *, int *, int *);
+int BLASFUNC(qlauum)(char *, int *, double *, int *, int *);
+int BLASFUNC(clauum)(char *, int *, float  *, int *, int *);
+int BLASFUNC(zlauum)(char *, int *, double *, int *, int *);
+int BLASFUNC(xlauum)(char *, int *, double *, int *, int *);
+
+int BLASFUNC(strti2)(char *, char *, int *, float  *, int *, int *);
+int BLASFUNC(dtrti2)(char *, char *, int *, double *, int *, int *);
+int BLASFUNC(qtrti2)(char *, char *, int *, double *, int *, int *);
+int BLASFUNC(ctrti2)(char *, char *, int *, float  *, int *, int *);
+int BLASFUNC(ztrti2)(char *, char *, int *, double *, int *, int *);
+int BLASFUNC(xtrti2)(char *, char *, int *, double *, int *, int *);
+
+int BLASFUNC(strtri)(char *, char *, int *, float  *, int *, int *);
+int BLASFUNC(dtrtri)(char *, char *, int *, double *, int *, int *);
+int BLASFUNC(qtrtri)(char *, char *, int *, double *, int *, int *);
+int BLASFUNC(ctrtri)(char *, char *, int *, float  *, int *, int *);
+int BLASFUNC(ztrtri)(char *, char *, int *, double *, int *, int *);
+int BLASFUNC(xtrtri)(char *, char *, int *, double *, int *, int *);
+
+int BLASFUNC(spotri)(char *, int *, float  *, int *, int *);
+int BLASFUNC(dpotri)(char *, int *, double *, int *, int *);
+int BLASFUNC(qpotri)(char *, int *, double *, int *, int *);
+int BLASFUNC(cpotri)(char *, int *, float  *, int *, int *);
+int BLASFUNC(zpotri)(char *, int *, double *, int *, int *);
+int BLASFUNC(xpotri)(char *, int *, double *, int *, int *);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/extern/Eigen3/Eigen/src/plugins/CMakeLists.txt b/extern/Eigen3/Eigen/src/plugins/CMakeLists.txt
new file mode 100644
index 00000000000..1a1d3ffbd72
--- /dev/null
+++ b/extern/Eigen3/Eigen/src/plugins/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_plugins_SRCS "*.h")
+
+INSTALL(FILES
+  ${Eigen_plugins_SRCS}
+  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/plugins COMPONENT Devel
+  )