diff options
| -rw-r--r-- | cmake/FindKLU.cmake | 51 | ||||
| -rw-r--r-- | test/CMakeLists.txt | 16 | ||||
| -rw-r--r-- | test/klu_support.cpp | 32 | ||||
| -rw-r--r-- | unsupported/Eigen/KLUSupport | 41 | ||||
| -rw-r--r-- | unsupported/Eigen/src/KLUSupport/KLUSupport.h | 364 |
5 files changed, 504 insertions, 0 deletions
diff --git a/cmake/FindKLU.cmake b/cmake/FindKLU.cmake new file mode 100644 index 000000000..2783b63d2 --- /dev/null +++ b/cmake/FindKLU.cmake @@ -0,0 +1,51 @@ +# KLU lib usually requires linking to a blas library. +# It is up to the user of this module to find a BLAS and link to it. + +if (KLU_INCLUDES AND KLU_LIBRARIES) + set(KLU_FIND_QUIETLY TRUE) +endif (KLU_INCLUDES AND KLU_LIBRARIES) + +find_path(KLU_INCLUDES + NAMES + klu.h + PATHS + $ENV{KLUDIR} + ${INCLUDE_INSTALL_DIR} + PATH_SUFFIXES + suitesparse + ufsparse +) + +if(KLU_LIBRARIES) + + if(NOT KLU_LIBDIR) + get_filename_component(KLU_LIBDIR ${KLU_LIBRARIES} PATH) + endif(NOT KLU_LIBDIR) + + find_library(COLAMD_LIBRARY colamd PATHS ${KLU_LIBDIR} $ENV{KLUDIR} ${LIB_INSTALL_DIR}) + if(COLAMD_LIBRARY) + set(KLU_LIBRARIES ${KLU_LIBRARIES} ${COLAMD_LIBRARY}) + endif () + + find_library(AMD_LIBRARY amd PATHS ${KLU_LIBDIR} $ENV{KLUDIR} ${LIB_INSTALL_DIR}) + if(AMD_LIBRARY) + set(KLU_LIBRARIES ${KLU_LIBRARIES} ${AMD_LIBRARY}) + endif () + + find_library(SUITESPARSE_LIBRARY SuiteSparse PATHS ${KLU_LIBDIR} $ENV{KLUDIR} ${LIB_INSTALL_DIR}) + if(SUITESPARSE_LIBRARY) + set(KLU_LIBRARIES ${KLU_LIBRARIES} ${SUITESPARSE_LIBRARY}) + endif () + + find_library(CHOLMOD_LIBRARY cholmod PATHS $ENV{KLU_LIBDIR} $ENV{KLUDIR} ${LIB_INSTALL_DIR}) + if(CHOLMOD_LIBRARY) + set(KLU_LIBRARIES ${KLU_LIBRARIES} ${CHOLMOD_LIBRARY}) + endif() + +endif(KLU_LIBRARIES) + +include(FindPackageHandleStandardArgs) +find_package_handle_standard_args(KLU DEFAULT_MSG + KLU_INCLUDES KLU_LIBRARIES) + +mark_as_advanced(KLU_INCLUDES KLU_LIBRARIES AMD_LIBRARY COLAMD_LIBRARY CHOLMOD_LIBRARY SUITESPARSE_LIBRARY) diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt index e73ab92b4..8bd086ce3 100644 --- a/test/CMakeLists.txt +++ b/test/CMakeLists.txt @@ -68,6 +68,17 @@ else() ei_add_property(EIGEN_MISSING_BACKENDS "UmfPack, ") endif() +find_package(KLU) +if(KLU_FOUND) + add_definitions("-DEIGEN_KLU_SUPPORT") + include_directories(${KLU_INCLUDES}) + set(SPARSE_LIBS ${SPARSE_LIBS} ${KLU_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) + set(KLU_ALL_LIBS ${KLU_LIBRARIES} ${EIGEN_BLAS_LIBRARIES}) + ei_add_property(EIGEN_TESTED_BACKENDS "KLU, ") +else() + ei_add_property(EIGEN_MISSING_BACKENDS "KLU, ") +endif() + find_package(SuperLU 4.0) if(SUPERLU_FOUND) add_definitions("-DEIGEN_SUPERLU_SUPPORT") @@ -297,6 +308,11 @@ if(UMFPACK_FOUND) ei_add_test(umfpack_support "" "${UMFPACK_ALL_LIBS}") endif() +if(KLU_FOUND OR SuiteSparse_FOUND) + message("ADDING KLU TEST") + ei_add_test(klu_support "" "${KLU_ALL_LIBS}") +endif() + if(SUPERLU_FOUND) ei_add_test(superlu_support "" "${SUPERLU_ALL_LIBS}") endif() diff --git a/test/klu_support.cpp b/test/klu_support.cpp new file mode 100644 index 000000000..8b1fdeb41 --- /dev/null +++ b/test/klu_support.cpp @@ -0,0 +1,32 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2011 Gael Guennebaud <g.gael@free.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 +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#define EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS +#include "sparse_solver.h" + +#include <unsupported/Eigen/KLUSupport> + +template<typename T> void test_klu_support_T() +{ + KLU<SparseMatrix<T, ColMajor> > klu_colmajor; + KLU<SparseMatrix<T, RowMajor> > klu_rowmajor; + + check_sparse_square_solving(klu_colmajor); + check_sparse_square_solving(klu_rowmajor); + + //check_sparse_square_determinant(umfpack_colmajor); + //check_sparse_square_determinant(umfpack_rowmajor); +} + +void test_klu_support() +{ + CALL_SUBTEST_1(test_klu_support_T<double>()); + CALL_SUBTEST_2(test_klu_support_T<std::complex<double> >()); +} + diff --git a/unsupported/Eigen/KLUSupport b/unsupported/Eigen/KLUSupport new file mode 100644 index 000000000..b23d90535 --- /dev/null +++ b/unsupported/Eigen/KLUSupport @@ -0,0 +1,41 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// 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 +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_KLUSUPPORT_MODULE_H +#define EIGEN_KLUSUPPORT_MODULE_H + +#include <Eigen/SparseCore> + +#include <Eigen/src/Core/util/DisableStupidWarnings.h> + +extern "C" { +#include <btf.h> +#include <klu.h> + } + +/** \ingroup Support_modules + * \defgroup KLUSupport_Module KLUSupport module + * + * This module provides an interface to the KLU library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package. + * It provides the following factorization class: + * - class KLU: a sparse LU factorization, well-suited for circuit simulation. + * + * \code + * #include <Eigen/KLUSupport> + * \endcode + * + * In order to use this module, the klu and btf headers must be accessible from the include paths, and your binary must be linked to the klu library and its dependencies. + * The dependencies depend on how umfpack has been compiled. + * For a cmake based project, you can use our FindKLU.cmake module to help you in this task. + * + */ + +#include "src/KLUSupport/KLUSupport.h" + +#include <Eigen/src/Core/util/ReenableStupidWarnings.h> + +#endif // EIGEN_KLUSUPPORT_MODULE_H diff --git a/unsupported/Eigen/src/KLUSupport/KLUSupport.h b/unsupported/Eigen/src/KLUSupport/KLUSupport.h new file mode 100644 index 000000000..d2781202e --- /dev/null +++ b/unsupported/Eigen/src/KLUSupport/KLUSupport.h @@ -0,0 +1,364 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2017 Kyle Macfarlan <kyle.macfarlan@gmail.com> +// +// 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 +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_KLUSUPPORT_H +#define EIGEN_KLUSUPPORT_H + +namespace Eigen { + +/* TODO extract L, extract U, compute det, etc... */ + +/** \ingroup KLUSupport_Module + * \brief A sparse LU factorization and solver based on KLU + * + * This class allows to solve for A.X = B sparse linear problems via a LU factorization + * using the KLU library. The sparse matrix A must be squared and full rank. + * The vectors or matrices X and B can be either dense or sparse. + * + * \warning The input matrix A should be in a \b compressed and \b column-major form. + * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix. + * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> + * + * \implsparsesolverconcept + * + * \sa \ref TutorialSparseSolverConcept, class SparseLU + */ + + +inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, int ldim, int nrhs, double B [ ], klu_common *Common, double) { + return klu_solve(Symbolic, Numeric, ldim, nrhs, B, Common); +} + +inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, int ldim, int nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) { + return klu_z_solve(Symbolic, Numeric, ldim, nrhs, &numext::real_ref(B[0]), Common); +} + +inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, int ldim, int nrhs, double B[], klu_common *Common, double) { + return klu_tsolve(Symbolic, Numeric, ldim, nrhs, B, Common); +} + +inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, int ldim, int nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) { + return klu_z_tsolve(Symbolic, Numeric, ldim, nrhs, &numext::real_ref(B[0]), 0, Common); +} + +inline klu_numeric* klu_factor(int Ap [ ], int Ai [ ], double Ax [ ], klu_symbolic *Symbolic, klu_common *Common, double) { + return klu_factor(Ap, Ai, Ax, Symbolic, Common); +} + +inline klu_numeric* klu_factor(int Ap[], int Ai[], std::complex<double> Ax[], klu_symbolic *Symbolic, klu_common *Common, std::complex<double>) { + return klu_z_factor(Ap, Ai, &numext::real_ref(Ax[0]), Symbolic, Common); +} + + +template<typename _MatrixType> +class KLU : public SparseSolverBase<KLU<_MatrixType> > +{ + protected: + typedef SparseSolverBase<KLU<_MatrixType> > Base; + using Base::m_isInitialized; + public: + using Base::_solve_impl; + typedef _MatrixType MatrixType; + typedef typename MatrixType::Scalar Scalar; + typedef typename MatrixType::RealScalar RealScalar; + typedef typename MatrixType::StorageIndex StorageIndex; + typedef Matrix<Scalar,Dynamic,1> Vector; + typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType; + typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType; + typedef SparseMatrix<Scalar> LUMatrixType; + typedef SparseMatrix<Scalar,ColMajor,int> KLUMatrixType; + typedef Ref<const KLUMatrixType, StandardCompressedFormat> KLUMatrixRef; + enum { + ColsAtCompileTime = MatrixType::ColsAtCompileTime, + MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime + }; + + public: + + KLU() + : m_dummy(0,0), mp_matrix(m_dummy) + { + init(); + } + + template<typename InputMatrixType> + explicit KLU(const InputMatrixType& matrix) + : mp_matrix(matrix) + { + init(); + compute(matrix); + } + + ~KLU() + { + if(m_symbolic) klu_free_symbolic(&m_symbolic,&m_common); + if(m_numeric) klu_free_numeric(&m_numeric,&m_common); + } + + inline Index rows() const { return mp_matrix.rows(); } + inline Index cols() const { return mp_matrix.cols(); } + + /** \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_isInitialized && "Decomposition is not initialized."); + return m_info; + } + + inline const LUMatrixType& matrixL() const + { + if (m_extractedDataAreDirty) extractData(); + return m_l; + } + + inline const LUMatrixType& matrixU() const + { + if (m_extractedDataAreDirty) extractData(); + return m_u; + } + + inline const IntColVectorType& permutationP() const + { + if (m_extractedDataAreDirty) extractData(); + return m_p; + } + + inline const IntRowVectorType& permutationQ() const + { + if (m_extractedDataAreDirty) extractData(); + return m_q; + } + + /** Computes the sparse Cholesky decomposition of \a matrix + * Note that the matrix should be column-major, and in compressed format for best performance. + * \sa SparseMatrix::makeCompressed(). + */ + template<typename InputMatrixType> + void compute(const InputMatrixType& matrix) + { + if(m_symbolic) klu_free_symbolic(&m_symbolic, &m_common); + if(m_numeric) klu_free_numeric(&m_numeric, &m_common); + grab(matrix.derived()); + analyzePattern_impl(); + factorize_impl(); + } + + /** 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(), compute() + */ + template<typename InputMatrixType> + void analyzePattern(const InputMatrixType& matrix) + { + if(m_symbolic) klu_free_symbolic(&m_symbolic, &m_common); + if(m_numeric) klu_free_numeric(&m_numeric, &m_common); + + grab(matrix.derived()); + + analyzePattern_impl(); + } + + + /** Provides access to the control settings array used by KLU. + * + * See KLU documentation for details. + */ + inline const klu_common& kluCommon() const + { + return m_common; + } + + /** Provides access to the control settings array used by UmfPack. + * + * If this array contains NaN's, the default values are used. + * + * See KLU documentation for details. + */ + inline klu_common& kluCommon() + { + return m_common; + } + + /** Performs a numeric decomposition of \a matrix + * + * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed. + * + * \sa analyzePattern(), compute() + */ + template<typename InputMatrixType> + void factorize(const InputMatrixType& matrix) + { + eigen_assert(m_analysisIsOk && "KLU: you must first call analyzePattern()"); + if(m_numeric) + klu_free_numeric(&m_numeric,&m_common); + + grab(matrix.derived()); + + factorize_impl(); + } + + /** \internal */ + template<typename BDerived,typename XDerived> + bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const; + + Scalar determinant() const; + + void extractData() const; + + protected: + + void init() + { + m_info = InvalidInput; + m_isInitialized = false; + m_numeric = 0; + m_symbolic = 0; + m_extractedDataAreDirty = true; + + klu_defaults(&m_common); + } + + void analyzePattern_impl() + { + m_info = InvalidInput; + m_analysisIsOk = false; + m_factorizationIsOk = false; + m_symbolic = klu_analyze(internal::convert_index<int>(mp_matrix.rows()), + const_cast<StorageIndex*>(mp_matrix.outerIndexPtr()), const_cast<StorageIndex*>(mp_matrix.innerIndexPtr()), + &m_common); + if (m_symbolic) { + m_isInitialized = true; + m_info = Success; + m_analysisIsOk = true; + m_extractedDataAreDirty = true; + } + } + + void factorize_impl() + { + + m_numeric = klu_factor(const_cast<StorageIndex*>(mp_matrix.outerIndexPtr()), const_cast<StorageIndex*>(mp_matrix.innerIndexPtr()), const_cast<Scalar*>(mp_matrix.valuePtr()), + m_symbolic, &m_common, Scalar()); + + + m_info = m_numeric ? Success : NumericalIssue; + m_factorizationIsOk = m_numeric ? 1 : 0; + m_extractedDataAreDirty = true; + } + + template<typename MatrixDerived> + void grab(const EigenBase<MatrixDerived> &A) + { + mp_matrix.~KLUMatrixRef(); + ::new (&mp_matrix) KLUMatrixRef(A.derived()); + } + + void grab(const KLUMatrixRef &A) + { + if(&(A.derived()) != &mp_matrix) + { + mp_matrix.~KLUMatrixRef(); + ::new (&mp_matrix) KLUMatrixRef(A); + } + } + + // cached data to reduce reallocation, etc. + mutable LUMatrixType m_l; + + mutable LUMatrixType m_u; + mutable IntColVectorType m_p; + mutable IntRowVectorType m_q; + + KLUMatrixType m_dummy; + KLUMatrixRef mp_matrix; + + klu_numeric* m_numeric; + klu_symbolic* m_symbolic; + klu_common m_common; + mutable ComputationInfo m_info; + int m_factorizationIsOk; + int m_analysisIsOk; + mutable bool m_extractedDataAreDirty; + + private: + KLU(const KLU& ) { } +}; + + +template<typename MatrixType> +void KLU<MatrixType>::extractData() const +{ + if (m_extractedDataAreDirty) + { + eigen_assert(false && "KLU: extractData Not Yet Implemented"); + +// // get size of the data +// int lnz, unz, rows, cols, nz_udiag; +// umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar()); +// +// // allocate data +// m_l.resize(rows,(std::min)(rows,cols)); +// m_l.resizeNonZeros(lnz); +// +// m_u.resize((std::min)(rows,cols),cols); +// m_u.resizeNonZeros(unz); +// +// m_p.resize(rows); +// m_q.resize(cols); +// +// // extract +// umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(), +// m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(), +// m_p.data(), m_q.data(), 0, 0, 0, m_numeric); +// +// m_extractedDataAreDirty = false; + } +} + +template<typename MatrixType> +typename KLU<MatrixType>::Scalar KLU<MatrixType>::determinant() const +{ + eigen_assert(false && "KLU: extractData Not Yet Implemented"); + return Scalar(); +} + +template<typename MatrixType> +template<typename BDerived,typename XDerived> +bool KLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const +{ + Index rhsCols = b.cols(); + eigen_assert((BDerived::Flags&RowMajorBit)==0 && "KLU backend does not support non col-major rhs yet"); + eigen_assert((XDerived::Flags&RowMajorBit)==0 && "KLU backend does not support non col-major result yet"); + eigen_assert(b.derived().data() != x.derived().data() && " KLU does not support inplace solve"); + eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()"); + + x = b; + int info = 0; + if (true/*(MatrixType::Flags&RowMajorBit) == 0*/) + { + info = klu_solve(m_symbolic, m_numeric, b.rows(), rhsCols, x.const_cast_derived().data(), const_cast<klu_common*>(&m_common), Scalar()); + } + else + { + info = klu_tsolve(m_symbolic, m_numeric, b.rows(), rhsCols, x.const_cast_derived().data(), const_cast<klu_common*>(&m_common), Scalar()); + } + + m_info = info!=0 ? Success : NumericalIssue; + return true; +} + +} // end namespace Eigen + +#endif // EIGEN_KLUSUPPORT_H |