diff options
Diffstat (limited to 'eigen/Eigen/src/SparseLU/SparseLU.h')
| -rw-r--r-- | eigen/Eigen/src/SparseLU/SparseLU.h | 221 |
1 files changed, 95 insertions, 126 deletions
diff --git a/eigen/Eigen/src/SparseLU/SparseLU.h b/eigen/Eigen/src/SparseLU/SparseLU.h index bdc4f19..f883ab3 100644 --- a/eigen/Eigen/src/SparseLU/SparseLU.h +++ b/eigen/Eigen/src/SparseLU/SparseLU.h @@ -2,7 +2,7 @@ // for linear algebra. // // Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> +// Copyright (C) 2012-2014 Gael Guennebaud <gael.guennebaud@inria.fr> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed @@ -14,7 +14,7 @@ namespace Eigen { -template <typename _MatrixType, typename _OrderingType = COLAMDOrdering<typename _MatrixType::Index> > class SparseLU; +template <typename _MatrixType, typename _OrderingType = COLAMDOrdering<typename _MatrixType::StorageIndex> > class SparseLU; template <typename MappedSparseMatrixType> struct SparseLUMatrixLReturnType; template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixUReturnType; @@ -64,33 +64,45 @@ template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixURetu * * \tparam _MatrixType The type of the sparse matrix. It must be a column-major SparseMatrix<> * \tparam _OrderingType The ordering method to use, either AMD, COLAMD or METIS. Default is COLMAD + * + * \implsparsesolverconcept * - * - * \sa \ref TutorialSparseDirectSolvers + * \sa \ref TutorialSparseSolverConcept * \sa \ref OrderingMethods_Module */ template <typename _MatrixType, typename _OrderingType> -class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typename _MatrixType::Index> +class SparseLU : public SparseSolverBase<SparseLU<_MatrixType,_OrderingType> >, public internal::SparseLUImpl<typename _MatrixType::Scalar, typename _MatrixType::StorageIndex> { + protected: + typedef SparseSolverBase<SparseLU<_MatrixType,_OrderingType> > APIBase; + using APIBase::m_isInitialized; public: + using APIBase::_solve_impl; + typedef _MatrixType MatrixType; typedef _OrderingType OrderingType; typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef SparseMatrix<Scalar,ColMajor,Index> NCMatrix; - typedef internal::MappedSuperNodalMatrix<Scalar, Index> SCMatrix; + typedef typename MatrixType::StorageIndex StorageIndex; + typedef SparseMatrix<Scalar,ColMajor,StorageIndex> NCMatrix; + typedef internal::MappedSuperNodalMatrix<Scalar, StorageIndex> SCMatrix; typedef Matrix<Scalar,Dynamic,1> ScalarVector; - typedef Matrix<Index,Dynamic,1> IndexVector; - typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; - typedef internal::SparseLUImpl<Scalar, Index> Base; + typedef Matrix<StorageIndex,Dynamic,1> IndexVector; + typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType; + typedef internal::SparseLUImpl<Scalar, StorageIndex> Base; + + enum { + ColsAtCompileTime = MatrixType::ColsAtCompileTime, + MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime + }; public: - SparseLU():m_isInitialized(true),m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1) + SparseLU():m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1) { initperfvalues(); } - SparseLU(const MatrixType& matrix):m_isInitialized(true),m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1) + explicit SparseLU(const MatrixType& matrix) + : m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1) { initperfvalues(); compute(matrix); @@ -141,9 +153,9 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ * y = b; matrixU().solveInPlace(y); * \endcode */ - SparseLUMatrixUReturnType<SCMatrix,MappedSparseMatrix<Scalar,ColMajor,Index> > matrixU() const + SparseLUMatrixUReturnType<SCMatrix,MappedSparseMatrix<Scalar,ColMajor,StorageIndex> > matrixU() const { - return SparseLUMatrixUReturnType<SCMatrix, MappedSparseMatrix<Scalar,ColMajor,Index> >(m_Lstore, m_Ustore); + return SparseLUMatrixUReturnType<SCMatrix, MappedSparseMatrix<Scalar,ColMajor,StorageIndex> >(m_Lstore, m_Ustore); } /** @@ -168,6 +180,7 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ m_diagpivotthresh = thresh; } +#ifdef EIGEN_PARSED_BY_DOXYGEN /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A. * * \warning the destination matrix X in X = this->solve(B) must be colmun-major. @@ -175,26 +188,8 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ * \sa compute() */ template<typename Rhs> - inline const internal::solve_retval<SparseLU, Rhs> solve(const MatrixBase<Rhs>& B) const - { - eigen_assert(m_factorizationIsOk && "SparseLU is not initialized."); - eigen_assert(rows()==B.rows() - && "SparseLU::solve(): invalid number of rows of the right hand side matrix B"); - return internal::solve_retval<SparseLU, 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<SparseLU, Rhs> solve(const SparseMatrixBase<Rhs>& B) const - { - eigen_assert(m_factorizationIsOk && "SparseLU is not initialized."); - eigen_assert(rows()==B.rows() - && "SparseLU::solve(): invalid number of rows of the right hand side matrix B"); - return internal::sparse_solve_retval<SparseLU, Rhs>(*this, B.derived()); - } + inline const Solve<SparseLU, Rhs> solve(const MatrixBase<Rhs>& B) const; +#endif // EIGEN_PARSED_BY_DOXYGEN /** \brief Reports whether previous computation was successful. * @@ -219,7 +214,7 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ } template<typename Rhs, typename Dest> - bool _solve(const MatrixBase<Rhs> &B, MatrixBase<Dest> &X_base) const + bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &X_base) const { Dest& X(X_base.derived()); eigen_assert(m_factorizationIsOk && "The matrix should be factorized first"); @@ -255,8 +250,9 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ * * \sa logAbsDeterminant(), signDeterminant() */ - Scalar absDeterminant() + Scalar absDeterminant() { + using std::abs; eigen_assert(m_factorizationIsOk && "The matrix should be factorized first."); // Initialize with the determinant of the row matrix Scalar det = Scalar(1.); @@ -268,42 +264,43 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ { if(it.index() == j) { - using std::abs; det *= abs(it.value()); break; } } - } - return det; - } + } + return det; + } - /** \returns the natural log of the absolute value of the determinant of the matrix - * of which **this is the QR decomposition - * - * \note This method is useful to work around the risk of overflow/underflow that's - * inherent to the determinant computation. - * - * \sa absDeterminant(), signDeterminant() - */ - Scalar logAbsDeterminant() const - { - eigen_assert(m_factorizationIsOk && "The matrix should be factorized first."); - Scalar det = Scalar(0.); - for (Index j = 0; j < this->cols(); ++j) - { - for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it) - { - if(it.row() < j) continue; - if(it.row() == j) - { - using std::log; using std::abs; - det += log(abs(it.value())); - break; - } - } - } - return det; - } + /** \returns the natural log of the absolute value of the determinant of the matrix + * of which **this is the QR decomposition + * + * \note This method is useful to work around the risk of overflow/underflow that's + * inherent to the determinant computation. + * + * \sa absDeterminant(), signDeterminant() + */ + Scalar logAbsDeterminant() const + { + using std::log; + using std::abs; + + eigen_assert(m_factorizationIsOk && "The matrix should be factorized first."); + Scalar det = Scalar(0.); + for (Index j = 0; j < this->cols(); ++j) + { + for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it) + { + if(it.row() < j) continue; + if(it.row() == j) + { + det += log(abs(it.value())); + break; + } + } + } + return det; + } /** \returns A number representing the sign of the determinant * @@ -355,7 +352,7 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ } } } - return det * Scalar(m_detPermR * m_detPermC); + return (m_detPermR * m_detPermC) > 0 ? det : -det; } protected: @@ -372,13 +369,12 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ // Variables mutable ComputationInfo m_info; - bool m_isInitialized; bool m_factorizationIsOk; bool m_analysisIsOk; std::string m_lastError; NCMatrix m_mat; // The input (permuted ) matrix SCMatrix m_Lstore; // The lower triangular matrix (supernodal) - MappedSparseMatrix<Scalar,ColMajor,Index> m_Ustore; // The upper triangular matrix + MappedSparseMatrix<Scalar,ColMajor,StorageIndex> m_Ustore; // The upper triangular matrix PermutationType m_perm_c; // Column permutation PermutationType m_perm_r ; // Row permutation IndexVector m_etree; // Column elimination tree @@ -388,7 +384,7 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ // SparseLU options bool m_symmetricmode; // values for performance - internal::perfvalues<Index> m_perfv; + internal::perfvalues m_perfv; RealScalar m_diagpivotthresh; // Specifies the threshold used for a diagonal entry to be an acceptable pivot Index m_nnzL, m_nnzU; // Nonzeros in L and U factors Index m_detPermR, m_detPermC; // Determinants of the permutation matrices @@ -417,30 +413,32 @@ void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat) //TODO It is possible as in SuperLU to compute row and columns scaling vectors to equilibrate the matrix mat. + // Firstly, copy the whole input matrix. + m_mat = mat; + + // Compute fill-in ordering OrderingType ord; - ord(mat,m_perm_c); + ord(m_mat,m_perm_c); // Apply the permutation to the column of the input matrix - //First copy the whole input matrix. - m_mat = mat; - if (m_perm_c.size()) { + if (m_perm_c.size()) + { m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers. FIXME : This vector is filled but not subsequently used. - //Then, permute only the column pointers - const Index * outerIndexPtr; - if (mat.isCompressed()) outerIndexPtr = mat.outerIndexPtr(); - else - { - Index *outerIndexPtr_t = new Index[mat.cols()+1]; - for(Index i = 0; i <= mat.cols(); i++) outerIndexPtr_t[i] = m_mat.outerIndexPtr()[i]; - outerIndexPtr = outerIndexPtr_t; - } + // Then, permute only the column pointers + ei_declare_aligned_stack_constructed_variable(StorageIndex,outerIndexPtr,mat.cols()+1,mat.isCompressed()?const_cast<StorageIndex*>(mat.outerIndexPtr()):0); + + // If the input matrix 'mat' is uncompressed, then the outer-indices do not match the ones of m_mat, and a copy is thus needed. + if(!mat.isCompressed()) + IndexVector::Map(outerIndexPtr, mat.cols()+1) = IndexVector::Map(m_mat.outerIndexPtr(),mat.cols()+1); + + // Apply the permutation and compute the nnz per column. for (Index i = 0; i < mat.cols(); i++) { m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i]; m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i+1] - outerIndexPtr[i]; } - if(!mat.isCompressed()) delete[] outerIndexPtr; } + // Compute the column elimination tree of the permuted matrix IndexVector firstRowElt; internal::coletree(m_mat, m_etree,firstRowElt); @@ -449,7 +447,7 @@ void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat) if (!m_symmetricmode) { IndexVector post, iwork; // Post order etree - internal::treePostorder(m_mat.cols(), m_etree, post); + internal::treePostorder(StorageIndex(m_mat.cols()), m_etree, post); // Renumber etree in postorder @@ -501,7 +499,9 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix) eigen_assert(m_analysisIsOk && "analyzePattern() should be called first"); eigen_assert((matrix.rows() == matrix.cols()) && "Only for squared matrices"); - typedef typename IndexVector::Scalar Index; + typedef typename IndexVector::Scalar StorageIndex; + + m_isInitialized = true; // Apply the column permutation computed in analyzepattern() @@ -511,11 +511,11 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix) { m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers. //Then, permute only the column pointers - const Index * outerIndexPtr; + const StorageIndex * outerIndexPtr; if (matrix.isCompressed()) outerIndexPtr = matrix.outerIndexPtr(); else { - Index* outerIndexPtr_t = new Index[matrix.cols()+1]; + StorageIndex* outerIndexPtr_t = new StorageIndex[matrix.cols()+1]; for(Index i = 0; i <= matrix.cols(); i++) outerIndexPtr_t[i] = m_mat.outerIndexPtr()[i]; outerIndexPtr = outerIndexPtr_t; } @@ -529,7 +529,7 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix) else { //FIXME This should not be needed if the empty permutation is handled transparently m_perm_c.resize(matrix.cols()); - for(Index i = 0; i < matrix.cols(); ++i) m_perm_c.indices()(i) = i; + for(StorageIndex i = 0; i < matrix.cols(); ++i) m_perm_c.indices()(i) = i; } Index m = m_mat.rows(); @@ -694,7 +694,7 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix) // Create supernode matrix L m_Lstore.setInfos(m, n, m_glu.lusup, m_glu.xlusup, m_glu.lsub, m_glu.xlsub, m_glu.supno, m_glu.xsup); // Create the column major upper sparse matrix U; - new (&m_Ustore) MappedSparseMatrix<Scalar, ColMajor, Index> ( m, n, m_nnzU, m_glu.xusub.data(), m_glu.usub.data(), m_glu.ucol.data() ); + new (&m_Ustore) MappedSparseMatrix<Scalar, ColMajor, StorageIndex> ( m, n, m_nnzU, m_glu.xusub.data(), m_glu.usub.data(), m_glu.ucol.data() ); m_info = Success; m_factorizationIsOk = true; @@ -703,9 +703,8 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix) template<typename MappedSupernodalType> struct SparseLUMatrixLReturnType : internal::no_assignment_operator { - typedef typename MappedSupernodalType::Index Index; typedef typename MappedSupernodalType::Scalar Scalar; - SparseLUMatrixLReturnType(const MappedSupernodalType& mapL) : m_mapL(mapL) + explicit SparseLUMatrixLReturnType(const MappedSupernodalType& mapL) : m_mapL(mapL) { } Index rows() { return m_mapL.rows(); } Index cols() { return m_mapL.cols(); } @@ -720,7 +719,6 @@ struct SparseLUMatrixLReturnType : internal::no_assignment_operator template<typename MatrixLType, typename MatrixUType> struct SparseLUMatrixUReturnType : internal::no_assignment_operator { - typedef typename MatrixLType::Index Index; typedef typename MatrixLType::Scalar Scalar; SparseLUMatrixUReturnType(const MatrixLType& mapL, const MatrixUType& mapU) : m_mapL(mapL),m_mapU(mapU) @@ -731,7 +729,7 @@ struct SparseLUMatrixUReturnType : internal::no_assignment_operator template<typename Dest> void solveInPlace(MatrixBase<Dest> &X) const { Index nrhs = X.cols(); - Index n = X.rows(); + Index n = X.rows(); // Backward solve with U for (Index k = m_mapL.nsuper(); k >= 0; k--) { @@ -750,7 +748,7 @@ struct SparseLUMatrixUReturnType : internal::no_assignment_operator else { Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) ); - Map< Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) ); + Map< Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) ); U = A.template triangularView<Upper>().solve(U); } @@ -772,35 +770,6 @@ struct SparseLUMatrixUReturnType : internal::no_assignment_operator const MatrixUType& m_mapU; }; -namespace internal { - -template<typename _MatrixType, typename Derived, typename Rhs> -struct solve_retval<SparseLU<_MatrixType,Derived>, Rhs> - : solve_retval_base<SparseLU<_MatrixType,Derived>, Rhs> -{ - typedef SparseLU<_MatrixType,Derived> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -template<typename _MatrixType, typename Derived, typename Rhs> -struct sparse_solve_retval<SparseLU<_MatrixType,Derived>, Rhs> - : sparse_solve_retval_base<SparseLU<_MatrixType,Derived>, Rhs> -{ - typedef SparseLU<_MatrixType,Derived> Dec; - EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - this->defaultEvalTo(dst); - } -}; -} // end namespace internal - } // End namespace Eigen #endif |