diff options
author | Daniel Genrich <daniel.genrich@gmx.net> | 2012-06-13 12:00:56 +0400 |
---|---|---|
committer | Daniel Genrich <daniel.genrich@gmx.net> | 2012-06-13 12:00:56 +0400 |
commit | 3ac5d889f6ddb620ab65703b3c4a5509950b41e2 (patch) | |
tree | 0ab84c1dadbb50b04a0bc1efaac4bfb228300a01 /extern | |
parent | 22892f40eeb7f484285722a3a64319af03dfe0e1 (diff) |
Update Eigen3 to latest git revision.
Diffstat (limited to 'extern')
-rw-r--r-- | extern/Eigen3/Eigen/Core | 2 | ||||
-rw-r--r-- | extern/Eigen3/Eigen/src/Core/Functors.h | 30 | ||||
-rw-r--r-- | extern/Eigen3/Eigen/src/Core/SolveTriangular.h | 12 | ||||
-rw-r--r-- | extern/Eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h | 12 | ||||
-rw-r--r-- | extern/Eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h | 14 | ||||
-rw-r--r-- | extern/Eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h | 53 | ||||
-rw-r--r-- | extern/Eigen3/Eigen/src/Geometry/Transform.h | 31 | ||||
-rw-r--r-- | extern/Eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h | 394 | ||||
-rw-r--r-- | extern/Eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h | 30 | ||||
-rw-r--r-- | extern/Eigen3/Eigen/src/SparseCore/SparseMatrixBase.h | 2 | ||||
-rw-r--r-- | extern/Eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h | 2 |
11 files changed, 293 insertions, 289 deletions
diff --git a/extern/Eigen3/Eigen/Core b/extern/Eigen3/Eigen/Core index 5e93d6b73f2..a8a15c05342 100644 --- a/extern/Eigen3/Eigen/Core +++ b/extern/Eigen3/Eigen/Core @@ -329,12 +329,12 @@ using std::ptrdiff_t; #include "src/Core/GeneralProduct.h" #include "src/Core/TriangularMatrix.h" #include "src/Core/SelfAdjointView.h" -#include "src/Core/SolveTriangular.h" #include "src/Core/products/Parallelizer.h" #include "src/Core/products/CoeffBasedProduct.h" #include "src/Core/products/GeneralBlockPanelKernel.h" #include "src/Core/products/GeneralMatrixVector.h" #include "src/Core/products/GeneralMatrixMatrix.h" +#include "src/Core/SolveTriangular.h" #include "src/Core/products/GeneralMatrixMatrixTriangular.h" #include "src/Core/products/SelfadjointMatrixVector.h" #include "src/Core/products/SelfadjointMatrixMatrix.h" diff --git a/extern/Eigen3/Eigen/src/Core/Functors.h b/extern/Eigen3/Eigen/src/Core/Functors.h index d9ad2e8821e..f33f636bb24 100644 --- a/extern/Eigen3/Eigen/src/Core/Functors.h +++ b/extern/Eigen3/Eigen/src/Core/Functors.h @@ -295,7 +295,7 @@ struct functor_traits<scalar_opposite_op<Scalar> > template<typename Scalar> struct scalar_abs_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_abs_op) typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return abs(a); } + EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return internal::abs(a); } template<typename Packet> EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pabs(a); } @@ -317,7 +317,7 @@ struct functor_traits<scalar_abs_op<Scalar> > template<typename Scalar> struct scalar_abs2_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_abs2_op) typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return abs2(a); } + EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return internal::abs2(a); } template<typename Packet> EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pmul(a,a); } @@ -333,7 +333,7 @@ struct functor_traits<scalar_abs2_op<Scalar> > */ template<typename Scalar> struct scalar_conjugate_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_conjugate_op) - EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return conj(a); } + EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return internal::conj(a); } template<typename Packet> EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); } }; @@ -370,7 +370,7 @@ template<typename Scalar> struct scalar_real_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_real_op) typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return real(a); } + EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return internal::real(a); } }; template<typename Scalar> struct functor_traits<scalar_real_op<Scalar> > @@ -385,7 +385,7 @@ template<typename Scalar> struct scalar_imag_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_op) typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return imag(a); } + EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return internal::imag(a); } }; template<typename Scalar> struct functor_traits<scalar_imag_op<Scalar> > @@ -400,7 +400,7 @@ template<typename Scalar> struct scalar_real_ref_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_real_ref_op) typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return real_ref(*const_cast<Scalar*>(&a)); } + EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return internal::real_ref(*const_cast<Scalar*>(&a)); } }; template<typename Scalar> struct functor_traits<scalar_real_ref_op<Scalar> > @@ -415,7 +415,7 @@ template<typename Scalar> struct scalar_imag_ref_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_ref_op) typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return imag_ref(*const_cast<Scalar*>(&a)); } + EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return internal::imag_ref(*const_cast<Scalar*>(&a)); } }; template<typename Scalar> struct functor_traits<scalar_imag_ref_op<Scalar> > @@ -429,7 +429,7 @@ struct functor_traits<scalar_imag_ref_op<Scalar> > */ template<typename Scalar> struct scalar_exp_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_exp_op) - inline const Scalar operator() (const Scalar& a) const { return exp(a); } + inline const Scalar operator() (const Scalar& a) const { return internal::exp(a); } typedef typename packet_traits<Scalar>::type Packet; inline Packet packetOp(const Packet& a) const { return internal::pexp(a); } }; @@ -445,7 +445,7 @@ struct functor_traits<scalar_exp_op<Scalar> > */ template<typename Scalar> struct scalar_log_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_log_op) - inline const Scalar operator() (const Scalar& a) const { return log(a); } + inline const Scalar operator() (const Scalar& a) const { return internal::log(a); } typedef typename packet_traits<Scalar>::type Packet; inline Packet packetOp(const Packet& a) const { return internal::plog(a); } }; @@ -703,7 +703,7 @@ struct functor_traits<scalar_add_op<Scalar> > */ template<typename Scalar> struct scalar_sqrt_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op) - inline const Scalar operator() (const Scalar& a) const { return sqrt(a); } + inline const Scalar operator() (const Scalar& a) const { return internal::sqrt(a); } typedef typename packet_traits<Scalar>::type Packet; inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); } }; @@ -721,7 +721,7 @@ struct functor_traits<scalar_sqrt_op<Scalar> > */ template<typename Scalar> struct scalar_cos_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_cos_op) - inline Scalar operator() (const Scalar& a) const { return cos(a); } + inline Scalar operator() (const Scalar& a) const { return internal::cos(a); } typedef typename packet_traits<Scalar>::type Packet; inline Packet packetOp(const Packet& a) const { return internal::pcos(a); } }; @@ -740,7 +740,7 @@ struct functor_traits<scalar_cos_op<Scalar> > */ template<typename Scalar> struct scalar_sin_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_sin_op) - inline const Scalar operator() (const Scalar& a) const { return sin(a); } + inline const Scalar operator() (const Scalar& a) const { return internal::sin(a); } typedef typename packet_traits<Scalar>::type Packet; inline Packet packetOp(const Packet& a) const { return internal::psin(a); } }; @@ -760,7 +760,7 @@ struct functor_traits<scalar_sin_op<Scalar> > */ template<typename Scalar> struct scalar_tan_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_tan_op) - inline const Scalar operator() (const Scalar& a) const { return tan(a); } + inline const Scalar operator() (const Scalar& a) const { return internal::tan(a); } typedef typename packet_traits<Scalar>::type Packet; inline Packet packetOp(const Packet& a) const { return internal::ptan(a); } }; @@ -779,7 +779,7 @@ struct functor_traits<scalar_tan_op<Scalar> > */ template<typename Scalar> struct scalar_acos_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_acos_op) - inline const Scalar operator() (const Scalar& a) const { return acos(a); } + inline const Scalar operator() (const Scalar& a) const { return internal::acos(a); } typedef typename packet_traits<Scalar>::type Packet; inline Packet packetOp(const Packet& a) const { return internal::pacos(a); } }; @@ -798,7 +798,7 @@ struct functor_traits<scalar_acos_op<Scalar> > */ template<typename Scalar> struct scalar_asin_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_asin_op) - inline const Scalar operator() (const Scalar& a) const { return asin(a); } + inline const Scalar operator() (const Scalar& a) const { return internal::asin(a); } typedef typename packet_traits<Scalar>::type Packet; inline Packet packetOp(const Packet& a) const { return internal::pasin(a); } }; diff --git a/extern/Eigen3/Eigen/src/Core/SolveTriangular.h b/extern/Eigen3/Eigen/src/Core/SolveTriangular.h index fc8496a4eba..ef09226a46e 100644 --- a/extern/Eigen3/Eigen/src/Core/SolveTriangular.h +++ b/extern/Eigen3/Eigen/src/Core/SolveTriangular.h @@ -100,12 +100,22 @@ struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic> typedef typename Rhs::Index Index; typedef blas_traits<Lhs> LhsProductTraits; typedef typename LhsProductTraits::DirectLinearAccessType ActualLhsType; + static void run(const Lhs& lhs, Rhs& rhs) { typename internal::add_const_on_value_type<ActualLhsType>::type actualLhs = LhsProductTraits::extract(lhs); + + const Index size = lhs.rows(); + const Index othersize = Side==OnTheLeft? rhs.cols() : rhs.rows(); + + typedef internal::gemm_blocking_space<(Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar, + Rhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxRowsAtCompileTime,4> BlockingType; + + BlockingType blocking(rhs.rows(), rhs.cols(), size); + triangular_solve_matrix<Scalar,Index,Side,Mode,LhsProductTraits::NeedToConjugate,(int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor, (Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor> - ::run(lhs.rows(), Side==OnTheLeft? rhs.cols() : rhs.rows(), &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &rhs.coeffRef(0,0), rhs.outerStride()); + ::run(size, othersize, &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &rhs.coeffRef(0,0), rhs.outerStride(), blocking); } }; diff --git a/extern/Eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h b/extern/Eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h index e1937321463..57285468bbc 100644 --- a/extern/Eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h +++ b/extern/Eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h @@ -42,9 +42,15 @@ inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff /** \internal */ inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1=0, std::ptrdiff_t* l2=0) { - static std::ptrdiff_t m_l1CacheSize = manage_caching_sizes_helper(queryL1CacheSize(),8 * 1024); - static std::ptrdiff_t m_l2CacheSize = manage_caching_sizes_helper(queryTopLevelCacheSize(),1*1024*1024); - + static std::ptrdiff_t m_l1CacheSize = 0; + static std::ptrdiff_t m_l2CacheSize = 0; + #pragma omp threadprivate(m_l1CacheSize,m_l2CacheSize) + if(m_l1CacheSize==0) + { + m_l1CacheSize = manage_caching_sizes_helper(queryL1CacheSize(),8 * 1024); + m_l2CacheSize = manage_caching_sizes_helper(queryTopLevelCacheSize(),1*1024*1024); + } + if(action==SetAction) { // set the cpu cache size and cache all block sizes from a global cache size in byte diff --git a/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h b/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h index 545beebc24a..76fc3203204 100644 --- a/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h +++ b/extern/Eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h @@ -79,7 +79,7 @@ static void run(Index rows, Index cols, Index depth, typedef gebp_traits<LhsScalar,RhsScalar> Traits; - Index kc = blocking.kc(); // cache block size along the K direction + Index kc = blocking.kc(); // cache block size along the K direction Index mc = (std::min)(rows,blocking.mc()); // cache block size along the M direction //Index nc = blocking.nc(); // cache block size along the N direction @@ -249,7 +249,7 @@ struct gemm_functor BlockingType& m_blocking; }; -template<int StorageOrder, typename LhsScalar, typename RhsScalar, int MaxRows, int MaxCols, int MaxDepth, +template<int StorageOrder, typename LhsScalar, typename RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor=1, bool FiniteAtCompileTime = MaxRows!=Dynamic && MaxCols!=Dynamic && MaxDepth != Dynamic> class gemm_blocking_space; template<typename _LhsScalar, typename _RhsScalar> @@ -282,8 +282,8 @@ class level3_blocking inline RhsScalar* blockW() { return m_blockW; } }; -template<int StorageOrder, typename _LhsScalar, typename _RhsScalar, int MaxRows, int MaxCols, int MaxDepth> -class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, MaxDepth, true> +template<int StorageOrder, typename _LhsScalar, typename _RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor> +class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, MaxDepth, KcFactor, true> : public level3_blocking< typename conditional<StorageOrder==RowMajor,_RhsScalar,_LhsScalar>::type, typename conditional<StorageOrder==RowMajor,_LhsScalar,_RhsScalar>::type> @@ -324,8 +324,8 @@ class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, M inline void allocateAll() {} }; -template<int StorageOrder, typename _LhsScalar, typename _RhsScalar, int MaxRows, int MaxCols, int MaxDepth> -class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, MaxDepth, false> +template<int StorageOrder, typename _LhsScalar, typename _RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor> +class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, MaxDepth, KcFactor, false> : public level3_blocking< typename conditional<StorageOrder==RowMajor,_RhsScalar,_LhsScalar>::type, typename conditional<StorageOrder==RowMajor,_LhsScalar,_RhsScalar>::type> @@ -349,7 +349,7 @@ class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, M this->m_nc = Transpose ? rows : cols; this->m_kc = depth; - computeProductBlockingSizes<LhsScalar,RhsScalar>(this->m_kc, this->m_mc, this->m_nc); + computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, this->m_mc, this->m_nc); m_sizeA = this->m_mc * this->m_kc; m_sizeB = this->m_kc * this->m_nc; m_sizeW = this->m_kc*Traits::WorkSpaceFactor; diff --git a/extern/Eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h b/extern/Eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h index 4bba12cfe9d..0dd94638bdd 100644 --- a/extern/Eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h +++ b/extern/Eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h @@ -36,14 +36,15 @@ struct triangular_solve_matrix<Scalar,Index,Side,Mode,Conjugate,TriStorageOrder, static EIGEN_DONT_INLINE void run( Index size, Index cols, const Scalar* tri, Index triStride, - Scalar* _other, Index otherStride) + Scalar* _other, Index otherStride, + level3_blocking<Scalar,Scalar>& blocking) { triangular_solve_matrix< Scalar, Index, Side==OnTheLeft?OnTheRight:OnTheLeft, (Mode&UnitDiag) | ((Mode&Upper) ? Lower : Upper), NumTraits<Scalar>::IsComplex && Conjugate, TriStorageOrder==RowMajor ? ColMajor : RowMajor, ColMajor> - ::run(size, cols, tri, triStride, _other, otherStride); + ::run(size, cols, tri, triStride, _other, otherStride, blocking); } }; @@ -55,7 +56,8 @@ struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageO static EIGEN_DONT_INLINE void run( Index size, Index otherSize, const Scalar* _tri, Index triStride, - Scalar* _other, Index otherStride) + Scalar* _other, Index otherStride, + level3_blocking<Scalar,Scalar>& blocking) { Index cols = otherSize; const_blas_data_mapper<Scalar, Index, TriStorageOrder> tri(_tri,triStride); @@ -67,17 +69,16 @@ struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageO IsLower = (Mode&Lower) == Lower }; - Index kc = size; // cache block size along the K direction - Index mc = size; // cache block size along the M direction - Index nc = cols; // cache block size along the N direction - computeProductBlockingSizes<Scalar,Scalar,4>(kc, mc, nc); + Index kc = blocking.kc(); // cache block size along the K direction + Index mc = (std::min)(size,blocking.mc()); // cache block size along the M direction + std::size_t sizeA = kc*mc; + std::size_t sizeB = kc*cols; std::size_t sizeW = kc*Traits::WorkSpaceFactor; - std::size_t sizeB = sizeW + kc*cols; - ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0); - ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0); - Scalar* blockB = allocatedBlockB + sizeW; - Scalar* blockW = allocatedBlockB; + + ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA()); + ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB()); + ei_declare_aligned_stack_constructed_variable(Scalar, blockW, sizeW, blocking.blockW()); conj_if<Conjugate> conj; gebp_kernel<Scalar, Scalar, Index, Traits::mr, Traits::nr, Conjugate, false> gebp_kernel; @@ -181,7 +182,7 @@ struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageO { pack_lhs(blockA, &tri(i2, IsLower ? k2 : k2-kc), triStride, actual_kc, actual_mc); - gebp_kernel(_other+i2, otherStride, blockA, blockB, actual_mc, actual_kc, cols, Scalar(-1)); + gebp_kernel(_other+i2, otherStride, blockA, blockB, actual_mc, actual_kc, cols, Scalar(-1), -1, -1, 0, 0, blockW); } } } @@ -197,7 +198,8 @@ struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorage static EIGEN_DONT_INLINE void run( Index size, Index otherSize, const Scalar* _tri, Index triStride, - Scalar* _other, Index otherStride) + Scalar* _other, Index otherStride, + level3_blocking<Scalar,Scalar>& blocking) { Index rows = otherSize; const_blas_data_mapper<Scalar, Index, TriStorageOrder> rhs(_tri,triStride); @@ -210,19 +212,16 @@ struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorage IsLower = (Mode&Lower) == Lower }; -// Index kc = std::min<Index>(Traits::Max_kc/4,size); // cache block size along the K direction -// Index mc = std::min<Index>(Traits::Max_mc,size); // cache block size along the M direction - // check that !!!! - Index kc = size; // cache block size along the K direction - Index mc = size; // cache block size along the M direction - Index nc = rows; // cache block size along the N direction - computeProductBlockingSizes<Scalar,Scalar,4>(kc, mc, nc); + Index kc = blocking.kc(); // cache block size along the K direction + Index mc = (std::min)(rows,blocking.mc()); // cache block size along the M direction + std::size_t sizeA = kc*mc; + std::size_t sizeB = kc*size; std::size_t sizeW = kc*Traits::WorkSpaceFactor; - std::size_t sizeB = sizeW + kc*size; - ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0); - ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0); - Scalar* blockB = allocatedBlockB + sizeW; + + ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA()); + ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB()); + ei_declare_aligned_stack_constructed_variable(Scalar, blockW, sizeW, blocking.blockW()); conj_if<Conjugate> conj; gebp_kernel<Scalar,Scalar, Index, Traits::mr, Traits::nr, false, Conjugate> gebp_kernel; @@ -289,7 +288,7 @@ struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorage Scalar(-1), actual_kc, actual_kc, // strides panelOffset, panelOffset, // offsets - allocatedBlockB); // workspace + blockW); // workspace } // unblocked triangular solve @@ -320,7 +319,7 @@ struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorage if (rs>0) gebp_kernel(_other+i2+startPanel*otherStride, otherStride, blockA, geb, actual_mc, actual_kc, rs, Scalar(-1), - -1, -1, 0, 0, allocatedBlockB); + -1, -1, 0, 0, blockW); } } } diff --git a/extern/Eigen3/Eigen/src/Geometry/Transform.h b/extern/Eigen3/Eigen/src/Geometry/Transform.h index be588fe4179..4bd6859b7e1 100644 --- a/extern/Eigen3/Eigen/src/Geometry/Transform.h +++ b/extern/Eigen3/Eigen/src/Geometry/Transform.h @@ -468,15 +468,40 @@ public: { return internal::transform_transform_product_impl<Transform,Transform>::run(*this,other); } - + + #ifdef __INTEL_COMPILER +private: + // this intermediate structure permits to workaround a bug in ICC 11: + // error: template instantiation resulted in unexpected function type of "Eigen::Transform<double, 3, 32, 0> + // (const Eigen::Transform<double, 3, 2, 0> &) const" + // (the meaning of a name may have changed since the template declaration -- the type of the template is: + // "Eigen::internal::transform_transform_product_impl<Eigen::Transform<double, 3, 32, 0>, + // Eigen::Transform<double, 3, Mode, Options>, <expression>>::ResultType (const Eigen::Transform<double, 3, Mode, Options> &) const") + // + template<int OtherMode,int OtherOptions> struct icc_11_workaround + { + typedef internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> > ProductType; + typedef typename ProductType::ResultType ResultType; + }; + +public: /** Concatenates two different transformations */ template<int OtherMode,int OtherOptions> - inline const typename internal::transform_transform_product_impl< - Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> >::ResultType + inline typename icc_11_workaround<OtherMode,OtherOptions>::ResultType + operator * (const Transform<Scalar,Dim,OtherMode,OtherOptions>& other) const + { + typedef typename icc_11_workaround<OtherMode,OtherOptions>::ProductType ProductType; + return ProductType::run(*this,other); + } + #else + /** Concatenates two different transformations */ + template<int OtherMode,int OtherOptions> + inline typename internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> >::ResultType operator * (const Transform<Scalar,Dim,OtherMode,OtherOptions>& other) const { return internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> >::run(*this,other); } + #endif /** \sa MatrixBase::setIdentity() */ void setIdentity() { m_matrix.setIdentity(); } diff --git a/extern/Eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h b/extern/Eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h index f42826208e3..f687a5e6be6 100644 --- a/extern/Eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h +++ b/extern/Eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h @@ -35,7 +35,6 @@ namespace Eigen { * * \sa TutorialSparseDirectSolvers */ - template<typename _MatrixType, bool IsStrSym = false> class PastixLU; template<typename _MatrixType, int Options> class PastixLLT; template<typename _MatrixType, int Options> class PastixLDLT; @@ -75,32 +74,34 @@ namespace internal 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; + if (nbrhs == 0) {x = NULL; nbrhs=1;} 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; + if (nbrhs == 0) {x = NULL; nbrhs=1;} 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) { + if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; } + if (nbrhs == 0) {x = NULL; nbrhs=1;} 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; + if (nbrhs == 0) {x = NULL; nbrhs=1;} 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) + void c_to_fortran_numbering (MatrixType& mat) { if ( !(mat.outerIndexPtr()[0]) ) { @@ -114,7 +115,7 @@ namespace internal // Convert to C-style Numbering template <typename MatrixType> - void EigenToCNumbering (MatrixType& mat) + void fortran_to_c_numbering (MatrixType& mat) { // Check the Numbering if ( mat.outerIndexPtr()[0] == 1 ) @@ -126,38 +127,12 @@ namespace internal --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 +class PastixBase : internal::noncopyable { public: typedef typename internal::pastix_traits<Derived>::MatrixType _MatrixType; @@ -166,29 +141,19 @@ class PastixBase typedef typename MatrixType::RealScalar RealScalar; typedef typename MatrixType::Index Index; typedef Matrix<Scalar,Dynamic,1> Vector; + typedef SparseMatrix<Scalar, ColMajor> ColSpMatrix; public: - PastixBase():m_initisOk(false),m_analysisIsOk(false),m_factorizationIsOk(false),m_isInitialized(false) + PastixBase() : m_initisOk(false), m_analysisIsOk(false), m_factorizationIsOk(false), m_isInitialized(false), m_pastixdata(0), m_size(0) { - m_pastixdata = 0; - m_hasTranspose = false; - PastixInit(); + init(); } ~PastixBase() { - PastixDestroy(); + clean(); } - - // 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. * @@ -269,7 +234,6 @@ class PastixBase /** Return a reference to a particular index parameter of the DPARM vector * \sa dparm() */ - double& dparm(int idxparam) { return m_dparm(idxparam); @@ -307,17 +271,27 @@ class PastixBase } protected: + + // Initialize the Pastix data structure, check the matrix + void init(); + + // Compute the ordering and the symbolic factorization + void analyzePattern(ColSpMatrix& mat); + + // Compute the numerical factorization + void factorize(ColSpMatrix& mat); + // Free all the data allocated by Pastix - void PastixDestroy() + void clean() { 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()); + internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0, + m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data()); } - Derived& compute (MatrixType& mat); + void compute(ColSpMatrix& mat); int m_initisOk; int m_analysisIsOk; @@ -325,22 +299,12 @@ class PastixBase 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<int,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. @@ -348,29 +312,29 @@ class PastixBase * \sa iparm() dparm() */ template <class Derived> -void PastixBase<Derived>::PastixInit() +void PastixBase<Derived>::init() { m_size = 0; - m_iparm.resize(IPARM_SIZE); - m_dparm.resize(DPARM_SIZE); + m_iparm.setZero(IPARM_SIZE); + m_dparm.setZero(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; - } + pastix(&m_pastixdata, MPI_COMM_WORLD, + 0, 0, 0, 0, + 0, 0, 0, 1, m_iparm.data(), m_dparm.data()); - m_iparm(IPARM_START_TASK) = API_TASK_INIT; - m_iparm(IPARM_END_TASK) = API_TASK_INIT; + m_iparm[IPARM_MATRIX_VERIFICATION] = API_NO; + m_iparm[IPARM_VERBOSE] = 2; + m_iparm[IPARM_ORDERING] = API_ORDER_SCOTCH; + m_iparm[IPARM_INCOMPLETE] = API_NO; + m_iparm[IPARM_OOC_LIMIT] = 2000; + m_iparm[IPARM_RHS_MAKING] = API_RHS_B; 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; + m_iparm(IPARM_START_TASK) = API_TASK_INIT; + m_iparm(IPARM_END_TASK) = API_TASK_INIT; + internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0, + 0, 0, 0, 0, m_iparm.data(), m_dparm.data()); // Check the returned error if(m_iparm(IPARM_ERROR_NUMBER)) { @@ -384,82 +348,74 @@ void PastixBase<Derived>::PastixInit() } template <class Derived> -Derived& PastixBase<Derived>::compute(MatrixType& mat) +void PastixBase<Derived>::compute(ColSpMatrix& 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); + 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(); + m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO; + m_isInitialized = m_factorizationIsOk; } template <class Derived> -Derived& PastixBase<Derived>::analyzePattern(MatrixType& mat) -{ - eigen_assert(m_initisOk && "PastixInit should be called first to set the default parameters"); +void PastixBase<Derived>::analyzePattern(ColSpMatrix& mat) +{ + eigen_assert(m_initisOk && "The initialization of PaSTiX failed"); + + // clean previous calls + if(m_size>0) + clean(); + 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()); + mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data()); // Check the returned error - if(m_iparm(IPARM_ERROR_NUMBER)) { + if(m_iparm(IPARM_ERROR_NUMBER)) + { m_info = NumericalIssue; m_analysisIsOk = false; } - else { + else + { m_info = Success; m_analysisIsOk = true; } - return derived(); } template <class Derived> -Derived& PastixBase<Derived>::factorize(MatrixType& mat) +void PastixBase<Derived>::factorize(ColSpMatrix& mat) { +// if(&m_cpyMat != &mat) m_cpyMat = 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()); + mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data()); // Check the returned error - if(m_iparm(IPARM_ERROR_NUMBER)) { + if(m_iparm(IPARM_ERROR_NUMBER)) + { m_info = NumericalIssue; m_factorizationIsOk = false; m_isInitialized = false; } - else { + else + { m_info = Success; m_factorizationIsOk = true; m_isInitialized = true; } - return derived(); } /* Solve the system */ @@ -475,20 +431,17 @@ bool PastixBase<Base>::_solve (const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) co 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()); + m_iparm[IPARM_START_TASK] = API_TASK_SOLVE; + m_iparm[IPARM_END_TASK] = API_TASK_REFINE; + + internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, x.rows(), 0, 0, 0, + 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; - } + m_info = m_iparm(IPARM_ERROR_NUMBER)==0 ? Success : NumericalIssue; + + return m_iparm(IPARM_ERROR_NUMBER)==0; } /** \ingroup PaStiXSupport_Module @@ -516,14 +469,18 @@ 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; + typedef typename Base::ColSpMatrix ColSpMatrix; + typedef typename MatrixType::Index Index; public: - PastixLU():Base() {} + PastixLU() : Base() + { + init(); + } PastixLU(const MatrixType& matrix):Base() { + init(); compute(matrix); } /** Compute the LU supernodal factorization of \p matrix. @@ -533,18 +490,9 @@ class PastixLU : public PastixBase< PastixLU<_MatrixType> > */ 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; + m_structureIsUptodate = false; + ColSpMatrix temp; + grabMatrix(matrix, temp); Base::compute(temp); } /** Compute the LU symbolic factorization of \p matrix using its sparsity pattern. @@ -554,20 +502,9 @@ class PastixLU : public PastixBase< PastixLU<_MatrixType> > */ 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; + m_structureIsUptodate = false; + ColSpMatrix temp; + grabMatrix(matrix, temp); Base::analyzePattern(temp); } @@ -578,27 +515,48 @@ class PastixLU : public PastixBase< PastixLU<_MatrixType> > */ 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; + ColSpMatrix temp; + grabMatrix(matrix, temp); Base::factorize(temp); } protected: + + void init() + { + m_structureIsUptodate = false; + m_iparm(IPARM_SYM) = API_SYM_NO; + m_iparm(IPARM_FACTORIZATION) = API_FACT_LU; + } + + void grabMatrix(const MatrixType& matrix, ColSpMatrix& out) + { + if(IsStrSym) + out = matrix; + else + { + if(!m_structureIsUptodate) + { + // update the transposed structure + m_transposedStructure = matrix.transpose(); + + // Set the elements of the matrix to zero + for (Index j=0; j<m_transposedStructure.outerSize(); ++j) + for(typename ColSpMatrix::InnerIterator it(m_transposedStructure, j); it; ++it) + it.valueRef() = 0.0; + + m_structureIsUptodate = true; + } + + out = m_transposedStructure + matrix; + } + internal::c_to_fortran_numbering(out); + } + using Base::m_iparm; using Base::m_dparm; - using Base::m_StrMatTrans; - using Base::m_hasTranspose; - private: - PastixLU(PastixLU& ) {} + ColSpMatrix m_transposedStructure; + bool m_structureIsUptodate; }; /** \ingroup PaStiXSupport_Module @@ -621,15 +579,18 @@ 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; + typedef typename Base::ColSpMatrix ColSpMatrix; public: enum { UpLo = _UpLo }; - PastixLLT():Base() {} + PastixLLT() : Base() + { + init(); + } PastixLLT(const MatrixType& matrix):Base() { + init(); compute(matrix); } @@ -638,13 +599,8 @@ class PastixLLT : public PastixBase< PastixLLT<_MatrixType, _UpLo> > */ 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; + ColSpMatrix temp; + grabMatrix(matrix, temp); Base::compute(temp); } @@ -654,13 +610,8 @@ class PastixLLT : public PastixBase< PastixLLT<_MatrixType, _UpLo> > */ 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; + ColSpMatrix temp; + grabMatrix(matrix, temp); Base::analyzePattern(temp); } /** Compute the LL^T supernodal numerical factorization of \p matrix @@ -668,19 +619,25 @@ class PastixLLT : public PastixBase< PastixLLT<_MatrixType, _UpLo> > */ 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; + ColSpMatrix temp; + grabMatrix(matrix, temp); Base::factorize(temp); } protected: using Base::m_iparm; - private: - PastixLLT(PastixLLT& ) {} + void init() + { + m_iparm(IPARM_SYM) = API_SYM_YES; + m_iparm(IPARM_FACTORIZATION) = API_FACT_LLT; + } + + void grabMatrix(const MatrixType& matrix, ColSpMatrix& out) + { + // Pastix supports only lower, column-major matrices + out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>(); + internal::c_to_fortran_numbering(out); + } }; /** \ingroup PaStiXSupport_Module @@ -700,18 +657,21 @@ class PastixLLT : public PastixBase< PastixLLT<_MatrixType, _UpLo> > template<typename _MatrixType, int _UpLo> class PastixLDLT : public PastixBase< PastixLDLT<_MatrixType, _UpLo> > { -public: + public: typedef _MatrixType MatrixType; typedef PastixBase<PastixLDLT<MatrixType, _UpLo> > Base; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::Index Index; + typedef typename Base::ColSpMatrix ColSpMatrix; public: enum { UpLo = _UpLo }; - PastixLDLT():Base() {} + PastixLDLT():Base() + { + init(); + } PastixLDLT(const MatrixType& matrix):Base() { + init(); compute(matrix); } @@ -720,13 +680,8 @@ public: */ 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; + ColSpMatrix temp; + grabMatrix(matrix, temp); Base::compute(temp); } @@ -736,14 +691,8 @@ public: */ 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; + ColSpMatrix temp; + grabMatrix(matrix, temp); Base::analyzePattern(temp); } /** Compute the LDL^T supernodal numerical factorization of \p matrix @@ -751,21 +700,26 @@ public: */ 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; + ColSpMatrix temp; + grabMatrix(matrix, temp); Base::factorize(temp); } protected: using Base::m_iparm; - private: - PastixLDLT(PastixLDLT& ) {} + void init() + { + m_iparm(IPARM_SYM) = API_SYM_YES; + m_iparm(IPARM_FACTORIZATION) = API_FACT_LDLT; + } + + void grabMatrix(const MatrixType& matrix, ColSpMatrix& out) + { + // Pastix supports only lower, column-major matrices + out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>(); + internal::c_to_fortran_numbering(out); + } }; namespace internal { diff --git a/extern/Eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h b/extern/Eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h index 5a1255a27a3..3c577f8d2f4 100644 --- a/extern/Eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h +++ b/extern/Eigen3/Eigen/src/SparseCholesky/SimplicialCholesky.h @@ -76,6 +76,9 @@ enum SimplicialCholeskyMode { * 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. + * + * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization + * such that the factorized matrix is P A P^-1. * * \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 @@ -208,7 +211,7 @@ class SimplicialCholeskyBase : internal::noncopyable return; if(m_P.size()>0) - dest = m_Pinv * b; + dest = m_P * b; else dest = b; @@ -222,7 +225,7 @@ class SimplicialCholeskyBase : internal::noncopyable derived().matrixU().solveInPlace(dest); if(m_P.size()>0) - dest = m_P * dest; + dest = m_Pinv * dest; } /** \internal */ @@ -268,7 +271,7 @@ class SimplicialCholeskyBase : internal::noncopyable 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); + ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_P); factorize_preordered<DoLDLT>(ap); } @@ -358,6 +361,9 @@ template<typename _MatrixType, int _UpLo> struct traits<SimplicialCholesky<_Matr * 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. + * + * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization + * such that the factorized matrix is P A P^-1. * * \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 @@ -443,6 +449,9 @@ public: * 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. + * + * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization + * such that the factorized matrix is P A P^-1. * * \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 @@ -628,7 +637,7 @@ public: return; if(Base::m_P.size()>0) - dest = Base::m_Pinv * b; + dest = Base::m_P * b; else dest = b; @@ -652,7 +661,7 @@ public: } if(Base::m_P.size()>0) - dest = Base::m_P * dest; + dest = Base::m_Pinv * dest; } Scalar determinant() const @@ -678,22 +687,23 @@ void SimplicialCholeskyBase<Derived>::ordering(const MatrixType& a, CholMatrixTy eigen_assert(a.rows()==a.cols()); const Index size = a.rows(); // TODO allows to configure the permutation + // Note that amd compute the inverse 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); + internal::minimum_degree_ordering(C, m_Pinv); } - if(m_P.size()>0) - m_Pinv = m_P.inverse(); + if(m_Pinv.size()>0) + m_P = m_Pinv.inverse(); else - m_Pinv.resize(0); + m_P.resize(0); ap.resize(size,size); - ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_Pinv); + ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_P); } template<typename Derived> diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseMatrixBase.h b/extern/Eigen3/Eigen/src/SparseCore/SparseMatrixBase.h index ed7da68928d..9d7b83577e0 100644 --- a/extern/Eigen3/Eigen/src/SparseCore/SparseMatrixBase.h +++ b/extern/Eigen3/Eigen/src/SparseCore/SparseMatrixBase.h @@ -372,7 +372,7 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived> const typename SparseDenseProductReturnType<Derived,OtherDerived>::Type operator*(const MatrixBase<OtherDerived> &other) const; - /** \returns an expression of P^-1 H P */ + /** \returns an expression of P H P^-1 where H is the matrix represented by \c *this */ SparseSymmetricPermutationProduct<Derived,Upper|Lower> twistedBy(const PermutationMatrix<Dynamic,Dynamic,Index>& perm) const { return SparseSymmetricPermutationProduct<Derived,Upper|Lower>(derived(), perm); diff --git a/extern/Eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h b/extern/Eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h index fc23b24d652..c925a894dd5 100644 --- a/extern/Eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h +++ b/extern/Eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h @@ -125,7 +125,7 @@ template<typename MatrixType, unsigned int UpLo> class SparseSelfAdjointView _dest = tmp; } - /** \returns an expression of P^-1 H P */ + /** \returns an expression of P H P^-1 */ SparseSymmetricPermutationProduct<_MatrixTypeNested,UpLo> twistedBy(const PermutationMatrix<Dynamic,Dynamic,Index>& perm) const { return SparseSymmetricPermutationProduct<_MatrixTypeNested,UpLo>(m_matrix, perm); |