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Diffstat (limited to 'Eigen/src/KLUSupport/KLUSupport.h')
-rw-r--r-- | Eigen/src/KLUSupport/KLUSupport.h | 358 |
1 files changed, 358 insertions, 0 deletions
diff --git a/Eigen/src/KLUSupport/KLUSupport.h b/Eigen/src/KLUSupport/KLUSupport.h new file mode 100644 index 000000000..215db35b0 --- /dev/null +++ b/Eigen/src/KLUSupport/KLUSupport.h @@ -0,0 +1,358 @@ +// 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 UmfPackLU, class SparseLU + */ + + +inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B [ ], klu_common *Common, double) { + return klu_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B, Common); +} + +inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) { + return klu_z_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), &numext::real_ref(B[0]), Common); +} + +inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B[], klu_common *Common, double) { + return klu_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B, Common); +} + +inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) { + return klu_z_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(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); + } + + EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return mp_matrix.rows(); } + EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return mp_matrix.cols(); } + + /** \brief Reports whether previous computation was successful. + * + * \returns \c Success if computation was successful, + * \c NumericalIssue if the matrix.appears to be negative. + */ + ComputationInfo info() const + { + eigen_assert(m_isInitialized && "Decomposition is not initialized."); + return m_info; + } +#if 0 // not implemented yet + 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; + } +#endif + /** 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; + +#if 0 // not implemented yet + Scalar determinant() const; + + void extractData() const; +#endif + + 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. +#if 0 // not implemented yet + mutable LUMatrixType m_l; + mutable LUMatrixType m_u; + mutable IntColVectorType m_p; + mutable IntRowVectorType m_q; +#endif + + 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& ) { } +}; + +#if 0 // not implemented yet +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(); +} +#endif + +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_STATIC_ASSERT((XDerived::Flags&RowMajorBit)==0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES); + 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 = klu_solve(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 |