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Diffstat (limited to 'third_party/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h')
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1 files changed, 0 insertions, 581 deletions
diff --git a/third_party/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h b/third_party/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h deleted file mode 100644 index b6571069e4..0000000000 --- a/third_party/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h +++ /dev/null @@ -1,581 +0,0 @@ -/* - 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 : internal::noncopyable -{ - 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; - typedef Array<Index,64,1,DontAlign> ParameterType; - 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. */ - ParameterType& 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; - - 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 = std::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 ParameterType m_iparm; - mutable IntColVectorType m_perm; - Index m_size; - -}; - -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; - } -}; - -/** \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); - } -}; - -/** \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); - } -}; - -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 - { - this->defaultEvalTo(dst); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_PARDISOSUPPORT_H |