| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2008-2011 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 |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #ifndef EIGEN_UMFPACKSUPPORT_H |
| #define EIGEN_UMFPACKSUPPORT_H |
| |
| namespace Eigen { |
| |
| /* TODO extract L, extract U, compute det, etc... */ |
| |
| // generic double/complex<double> wrapper functions: |
| |
| inline void umfpack_free_numeric(void **Numeric, double) |
| { umfpack_di_free_numeric(Numeric); *Numeric = 0; } |
| |
| inline void umfpack_free_numeric(void **Numeric, std::complex<double>) |
| { umfpack_zi_free_numeric(Numeric); *Numeric = 0; } |
| |
| inline void umfpack_free_symbolic(void **Symbolic, double) |
| { umfpack_di_free_symbolic(Symbolic); *Symbolic = 0; } |
| |
| inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>) |
| { umfpack_zi_free_symbolic(Symbolic); *Symbolic = 0; } |
| |
| inline int umfpack_symbolic(int n_row,int n_col, |
| const int Ap[], const int Ai[], const double Ax[], void **Symbolic, |
| const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO]) |
| { |
| return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info); |
| } |
| |
| inline int umfpack_symbolic(int n_row,int n_col, |
| const int Ap[], const int Ai[], const std::complex<double> Ax[], void **Symbolic, |
| const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO]) |
| { |
| return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info); |
| } |
| |
| inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[], |
| void *Symbolic, void **Numeric, |
| const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO]) |
| { |
| return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info); |
| } |
| |
| inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex<double> Ax[], |
| void *Symbolic, void **Numeric, |
| const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO]) |
| { |
| return umfpack_zi_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info); |
| } |
| |
| inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[], |
| double X[], const double B[], void *Numeric, |
| const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) |
| { |
| return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info); |
| } |
| |
| inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[], |
| std::complex<double> X[], const std::complex<double> B[], void *Numeric, |
| const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) |
| { |
| return umfpack_zi_solve(sys,Ap,Ai,&numext::real_ref(Ax[0]),0,&numext::real_ref(X[0]),0,&numext::real_ref(B[0]),0,Numeric,Control,Info); |
| } |
| |
| inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double) |
| { |
| return umfpack_di_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric); |
| } |
| |
| inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, std::complex<double>) |
| { |
| return umfpack_zi_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric); |
| } |
| |
| inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[], |
| int P[], int Q[], double Dx[], int *do_recip, double Rs[], void *Numeric) |
| { |
| return umfpack_di_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric); |
| } |
| |
| inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex<double> Lx[], int Up[], int Ui[], std::complex<double> Ux[], |
| int P[], int Q[], std::complex<double> Dx[], int *do_recip, double Rs[], void *Numeric) |
| { |
| double& lx0_real = numext::real_ref(Lx[0]); |
| double& ux0_real = numext::real_ref(Ux[0]); |
| double& dx0_real = numext::real_ref(Dx[0]); |
| return umfpack_zi_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q, |
| Dx?&dx0_real:0,0,do_recip,Rs,Numeric); |
| } |
| |
| inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO]) |
| { |
| return umfpack_di_get_determinant(Mx,Ex,NumericHandle,User_Info); |
| } |
| |
| inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO]) |
| { |
| double& mx_real = numext::real_ref(*Mx); |
| return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info); |
| } |
| |
| |
| /** \ingroup UmfPackSupport_Module |
| * \brief A sparse LU factorization and solver based on UmfPack |
| * |
| * This class allows to solve for A.X = B sparse linear problems via a LU factorization |
| * using the UmfPack 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<> |
| * |
| * \sa \ref TutorialSparseDirectSolvers |
| */ |
| template<typename _MatrixType> |
| class UmfPackLU : public SparseSolverBase<UmfPackLU<_MatrixType> > |
| { |
| protected: |
| typedef SparseSolverBase<UmfPackLU<_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> UmfpackMatrixType; |
| typedef Ref<const UmfpackMatrixType, StandardCompressedFormat> UmfpackMatrixRef; |
| |
| public: |
| |
| UmfPackLU() |
| : m_dummy(0,0), mp_matrix(m_dummy) |
| { |
| init(); |
| } |
| |
| explicit UmfPackLU(const MatrixType& matrix) |
| : mp_matrix(matrix) |
| { |
| init(); |
| compute(matrix); |
| } |
| |
| ~UmfPackLU() |
| { |
| if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar()); |
| if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar()); |
| } |
| |
| 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) umfpack_free_symbolic(&m_symbolic,Scalar()); |
| if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar()); |
| 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) umfpack_free_symbolic(&m_symbolic,Scalar()); |
| if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar()); |
| |
| grab(matrix.derived()); |
| |
| analyzePattern_impl(); |
| } |
| |
| /** 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 && "UmfPackLU: you must first call analyzePattern()"); |
| if(m_numeric) |
| umfpack_free_numeric(&m_numeric,Scalar()); |
| |
| 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; |
| } |
| |
| void analyzePattern_impl() |
| { |
| int errorCode = 0; |
| errorCode = umfpack_symbolic(internal::convert_index<int>(mp_matrix.rows()), |
| internal::convert_index<int>(mp_matrix.cols()), |
| mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(), |
| &m_symbolic, 0, 0); |
| |
| m_isInitialized = true; |
| m_info = errorCode ? InvalidInput : Success; |
| m_analysisIsOk = true; |
| m_factorizationIsOk = false; |
| m_extractedDataAreDirty = true; |
| } |
| |
| void factorize_impl() |
| { |
| int errorCode; |
| errorCode = umfpack_numeric(mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(), |
| m_symbolic, &m_numeric, 0, 0); |
| |
| m_info = errorCode ? NumericalIssue : Success; |
| m_factorizationIsOk = true; |
| m_extractedDataAreDirty = true; |
| } |
| |
| template<typename MatrixDerived> |
| void grab(const EigenBase<MatrixDerived> &A) |
| { |
| mp_matrix.~UmfpackMatrixRef(); |
| ::new (&mp_matrix) UmfpackMatrixRef(A.derived()); |
| } |
| |
| void grab(const UmfpackMatrixRef &A) |
| { |
| if(&(A.derived()) != &mp_matrix) |
| { |
| mp_matrix.~UmfpackMatrixRef(); |
| ::new (&mp_matrix) UmfpackMatrixRef(A); |
| } |
| } |
| |
| // cached data to reduce reallocation, etc. |
| mutable LUMatrixType m_l; |
| mutable LUMatrixType m_u; |
| mutable IntColVectorType m_p; |
| mutable IntRowVectorType m_q; |
| |
| UmfpackMatrixType m_dummy; |
| UmfpackMatrixRef mp_matrix; |
| |
| void* m_numeric; |
| void* m_symbolic; |
| |
| mutable ComputationInfo m_info; |
| int m_factorizationIsOk; |
| int m_analysisIsOk; |
| mutable bool m_extractedDataAreDirty; |
| |
| private: |
| UmfPackLU(UmfPackLU& ) { } |
| }; |
| |
| |
| template<typename MatrixType> |
| void UmfPackLU<MatrixType>::extractData() const |
| { |
| if (m_extractedDataAreDirty) |
| { |
| // 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 UmfPackLU<MatrixType>::Scalar UmfPackLU<MatrixType>::determinant() const |
| { |
| Scalar det; |
| umfpack_get_determinant(&det, 0, m_numeric, 0); |
| return det; |
| } |
| |
| template<typename MatrixType> |
| template<typename BDerived,typename XDerived> |
| bool UmfPackLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const |
| { |
| Index rhsCols = b.cols(); |
| eigen_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major rhs yet"); |
| eigen_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major result yet"); |
| eigen_assert(b.derived().data() != x.derived().data() && " Umfpack does not support inplace solve"); |
| |
| int errorCode; |
| Scalar* x_ptr = 0; |
| Matrix<Scalar,Dynamic,1> x_tmp; |
| if(x.innerStride()!=1) |
| { |
| x_tmp.resize(x.rows()); |
| x_ptr = x_tmp.data(); |
| } |
| for (int j=0; j<rhsCols; ++j) |
| { |
| if(x.innerStride()==1) |
| x_ptr = &x.col(j).coeffRef(0); |
| errorCode = umfpack_solve(UMFPACK_A, |
| mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(), |
| x_ptr, &b.const_cast_derived().col(j).coeffRef(0), m_numeric, 0, 0); |
| if(x.innerStride()!=1) |
| x.col(j) = x_tmp; |
| if (errorCode!=0) |
| return false; |
| } |
| |
| return true; |
| } |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_UMFPACKSUPPORT_H |
