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Diffstat (limited to 'extern/Eigen2/Eigen/src/Sparse/SparseLLT.h')
| -rw-r--r-- | extern/Eigen2/Eigen/src/Sparse/SparseLLT.h | 205 |
1 files changed, 0 insertions, 205 deletions
diff --git a/extern/Eigen2/Eigen/src/Sparse/SparseLLT.h b/extern/Eigen2/Eigen/src/Sparse/SparseLLT.h deleted file mode 100644 index e7c314c2cad..00000000000 --- a/extern/Eigen2/Eigen/src/Sparse/SparseLLT.h +++ /dev/null @@ -1,205 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. Eigen itself is part of the KDE project. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.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_SPARSELLT_H -#define EIGEN_SPARSELLT_H - -/** \ingroup Sparse_Module - * - * \class SparseLLT - * - * \brief LLT Cholesky decomposition of a sparse matrix and associated features - * - * \param MatrixType the type of the matrix of which we are computing the LLT Cholesky decomposition - * - * \sa class LLT, class LDLT - */ -template<typename MatrixType, int Backend = DefaultBackend> -class SparseLLT -{ - protected: - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - typedef SparseMatrix<Scalar,LowerTriangular> CholMatrixType; - - enum { - SupernodalFactorIsDirty = 0x10000, - MatrixLIsDirty = 0x20000 - }; - - public: - - /** Creates a dummy LLT factorization object with flags \a flags. */ - SparseLLT(int flags = 0) - : m_flags(flags), m_status(0) - { - m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>(); - } - - /** Creates a LLT object and compute the respective factorization of \a matrix using - * flags \a flags. */ - SparseLLT(const MatrixType& matrix, int flags = 0) - : m_matrix(matrix.rows(), matrix.cols()), m_flags(flags), m_status(0) - { - m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>(); - compute(matrix); - } - - /** Sets the relative threshold value used to prune zero coefficients during the decomposition. - * - * Setting a value greater than zero speeds up computation, and yields to an imcomplete - * factorization with fewer non zero coefficients. Such approximate factors are especially - * useful to initialize an iterative solver. - * - * \warning if precision is greater that zero, the LLT factorization is not guaranteed to succeed - * even if the matrix is positive definite. - * - * Note that the exact meaning of this parameter might depends on the actual - * backend. Moreover, not all backends support this feature. - * - * \sa precision() */ - void setPrecision(RealScalar v) { m_precision = v; } - - /** \returns the current precision. - * - * \sa setPrecision() */ - RealScalar precision() const { return m_precision; } - - /** Sets the flags. Possible values are: - * - CompleteFactorization - * - IncompleteFactorization - * - MemoryEfficient (hint to use the memory most efficient method offered by the backend) - * - SupernodalMultifrontal (implies a complete factorization if supported by the backend, - * overloads the MemoryEfficient flags) - * - SupernodalLeftLooking (implies a complete factorization if supported by the backend, - * overloads the MemoryEfficient flags) - * - * \sa flags() */ - void setFlags(int f) { m_flags = f; } - /** \returns the current flags */ - int flags() const { return m_flags; } - - /** Computes/re-computes the LLT factorization */ - void compute(const MatrixType& matrix); - - /** \returns the lower triangular matrix L */ - inline const CholMatrixType& matrixL(void) const { return m_matrix; } - - template<typename Derived> - bool solveInPlace(MatrixBase<Derived> &b) const; - - /** \returns true if the factorization succeeded */ - inline bool succeeded(void) const { return m_succeeded; } - - protected: - CholMatrixType m_matrix; - RealScalar m_precision; - int m_flags; - mutable int m_status; - bool m_succeeded; -}; - -/** Computes / recomputes the LLT decomposition of matrix \a a - * using the default algorithm. - */ -template<typename MatrixType, int Backend> -void SparseLLT<MatrixType,Backend>::compute(const MatrixType& a) -{ - assert(a.rows()==a.cols()); - const int size = a.rows(); - m_matrix.resize(size, size); - - // allocate a temporary vector for accumulations - AmbiVector<Scalar> tempVector(size); - RealScalar density = a.nonZeros()/RealScalar(size*size); - - // TODO estimate the number of non zeros - m_matrix.startFill(a.nonZeros()*2); - for (int j = 0; j < size; ++j) - { - Scalar x = ei_real(a.coeff(j,j)); - - // TODO better estimate of the density ! - tempVector.init(density>0.001? IsDense : IsSparse); - tempVector.setBounds(j+1,size); - tempVector.setZero(); - // init with current matrix a - { - typename MatrixType::InnerIterator it(a,j); - ++it; // skip diagonal element - for (; it; ++it) - tempVector.coeffRef(it.index()) = it.value(); - } - for (int k=0; k<j+1; ++k) - { - typename CholMatrixType::InnerIterator it(m_matrix, k); - while (it && it.index()<j) - ++it; - if (it && it.index()==j) - { - Scalar y = it.value(); - x -= ei_abs2(y); - ++it; // skip j-th element, and process remaining column coefficients - tempVector.restart(); - for (; it; ++it) - { - tempVector.coeffRef(it.index()) -= it.value() * y; - } - } - } - // copy the temporary vector to the respective m_matrix.col() - // while scaling the result by 1/real(x) - RealScalar rx = ei_sqrt(ei_real(x)); - m_matrix.fill(j,j) = rx; - Scalar y = Scalar(1)/rx; - for (typename AmbiVector<Scalar>::Iterator it(tempVector, m_precision*rx); it; ++it) - { - m_matrix.fill(it.index(), j) = it.value() * y; - } - } - m_matrix.endFill(); -} - -/** Computes b = L^-T L^-1 b */ -template<typename MatrixType, int Backend> -template<typename Derived> -bool SparseLLT<MatrixType, Backend>::solveInPlace(MatrixBase<Derived> &b) const -{ - const int size = m_matrix.rows(); - ei_assert(size==b.rows()); - - m_matrix.solveTriangularInPlace(b); - // FIXME should be simply .adjoint() but it fails to compile... - if (NumTraits<Scalar>::IsComplex) - { - CholMatrixType aux = m_matrix.conjugate(); - aux.transpose().solveTriangularInPlace(b); - } - else - m_matrix.transpose().solveTriangularInPlace(b); - - return true; -} - -#endif // EIGEN_SPARSELLT_H |