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Diffstat (limited to 'Eigen/src/Sparse/SparseLU.h')
| -rw-r--r-- | Eigen/src/Sparse/SparseLU.h | 162 |
1 files changed, 0 insertions, 162 deletions
diff --git a/Eigen/src/Sparse/SparseLU.h b/Eigen/src/Sparse/SparseLU.h deleted file mode 100644 index 0211b78e2..000000000 --- a/Eigen/src/Sparse/SparseLU.h +++ /dev/null @@ -1,162 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// 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_SPARSELU_H -#define EIGEN_SPARSELU_H - -enum { - SvNoTrans = 0, - SvTranspose = 1, - SvAdjoint = 2 -}; - -/** \ingroup Sparse_Module - * - * \class SparseLU - * - * \brief LU decomposition of a sparse matrix and associated features - * - * \param MatrixType the type of the matrix of which we are computing the LU factorization - * - * \sa class FullPivLU, class SparseLLT - */ -template<typename MatrixType, int Backend = DefaultBackend> -class SparseLU -{ - protected: - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - typedef SparseMatrix<Scalar> LUMatrixType; - - enum { - MatrixLUIsDirty = 0x10000 - }; - - public: - - /** Creates a dummy LU factorization object with flags \a flags. */ - SparseLU(int flags = 0) - : m_flags(flags), m_status(0) - { - m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision(); - } - - /** Creates a LU object and compute the respective factorization of \a matrix using - * flags \a flags. */ - SparseLU(const MatrixType& matrix, int flags = 0) - : /*m_matrix(matrix.rows(), matrix.cols()),*/ m_flags(flags), m_status(0) - { - m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision(); - 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. - * - * 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 - * - one of the ordering methods - * - etc... - * - * \sa flags() */ - void setFlags(int f) { m_flags = f; } - /** \returns the current flags */ - int flags() const { return m_flags; } - - void setOrderingMethod(int m) - { - ei_assert( (m&~OrderingMask) == 0 && m!=0 && "invalid ordering method"); - m_flags = m_flags&~OrderingMask | m&OrderingMask; - } - - int orderingMethod() const - { - return m_flags&OrderingMask; - } - - /** Computes/re-computes the LU factorization */ - void compute(const MatrixType& matrix); - - /** \returns the lower triangular matrix L */ - //inline const MatrixType& matrixL() const { return m_matrixL; } - - /** \returns the upper triangular matrix U */ - //inline const MatrixType& matrixU() const { return m_matrixU; } - - template<typename BDerived, typename XDerived> - bool solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x, - const int transposed = SvNoTrans) const; - - /** \returns true if the factorization succeeded */ - inline bool succeeded(void) const { return m_succeeded; } - - protected: - RealScalar m_precision; - int m_flags; - mutable int m_status; - bool m_succeeded; -}; - -/** Computes / recomputes the LU decomposition of matrix \a a - * using the default algorithm. - */ -template<typename MatrixType, int Backend> -void SparseLU<MatrixType,Backend>::compute(const MatrixType& ) -{ - ei_assert(false && "not implemented yet"); -} - -/** Computes *x = U^-1 L^-1 b - * - * If \a transpose is set to SvTranspose or SvAdjoint, the solution - * of the transposed/adjoint system is computed instead. - * - * Not all backends implement the solution of the transposed or - * adjoint system. - */ -template<typename MatrixType, int Backend> -template<typename BDerived, typename XDerived> -bool SparseLU<MatrixType,Backend>::solve(const MatrixBase<BDerived> &, MatrixBase<XDerived>* , const int ) const -{ - ei_assert(false && "not implemented yet"); - return false; -} - -#endif // EIGEN_SPARSELU_H |