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457 lines
15 KiB
457 lines
15 KiB
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_UMFPACKSUPPORT_H
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#define EIGEN_UMFPACKSUPPORT_H
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namespace Eigen {
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/* TODO extract L, extract U, compute det, etc... */
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// generic double/complex<double> wrapper functions:
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inline void umfpack_defaults(double control[UMFPACK_CONTROL], double)
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{ umfpack_di_defaults(control); }
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inline void umfpack_defaults(double control[UMFPACK_CONTROL], std::complex<double>)
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{ umfpack_zi_defaults(control); }
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inline void umfpack_free_numeric(void **Numeric, double)
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{ umfpack_di_free_numeric(Numeric); *Numeric = 0; }
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inline void umfpack_free_numeric(void **Numeric, std::complex<double>)
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{ umfpack_zi_free_numeric(Numeric); *Numeric = 0; }
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inline void umfpack_free_symbolic(void **Symbolic, double)
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{ umfpack_di_free_symbolic(Symbolic); *Symbolic = 0; }
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inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>)
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{ umfpack_zi_free_symbolic(Symbolic); *Symbolic = 0; }
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inline int umfpack_symbolic(int n_row,int n_col,
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const int Ap[], const int Ai[], const double Ax[], void **Symbolic,
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const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
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{
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return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info);
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}
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inline int umfpack_symbolic(int n_row,int n_col,
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const int Ap[], const int Ai[], const std::complex<double> Ax[], void **Symbolic,
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const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
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{
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return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info);
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}
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inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[],
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void *Symbolic, void **Numeric,
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const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
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{
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return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info);
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}
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inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex<double> Ax[],
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void *Symbolic, void **Numeric,
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const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
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{
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return umfpack_zi_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info);
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}
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inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[],
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double X[], const double B[], void *Numeric,
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const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
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{
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return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info);
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}
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inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[],
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std::complex<double> X[], const std::complex<double> B[], void *Numeric,
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const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
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{
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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);
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}
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inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double)
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{
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return umfpack_di_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
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}
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inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, std::complex<double>)
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{
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return umfpack_zi_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
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}
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inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[],
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int P[], int Q[], double Dx[], int *do_recip, double Rs[], void *Numeric)
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{
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return umfpack_di_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric);
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}
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inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex<double> Lx[], int Up[], int Ui[], std::complex<double> Ux[],
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int P[], int Q[], std::complex<double> Dx[], int *do_recip, double Rs[], void *Numeric)
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{
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double& lx0_real = numext::real_ref(Lx[0]);
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double& ux0_real = numext::real_ref(Ux[0]);
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double& dx0_real = numext::real_ref(Dx[0]);
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return umfpack_zi_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q,
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Dx?&dx0_real:0,0,do_recip,Rs,Numeric);
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}
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inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO])
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{
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return umfpack_di_get_determinant(Mx,Ex,NumericHandle,User_Info);
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}
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inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO])
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{
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double& mx_real = numext::real_ref(*Mx);
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return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info);
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}
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/** \ingroup UmfPackSupport_Module
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* \brief A sparse LU factorization and solver based on UmfPack
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*
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* This class allows to solve for A.X = B sparse linear problems via a LU factorization
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* using the UmfPack library. The sparse matrix A must be squared and full rank.
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* The vectors or matrices X and B can be either dense or sparse.
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*
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* \warning The input matrix A should be in a \b compressed and \b column-major form.
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* Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
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* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
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*
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* \implsparsesolverconcept
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*
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* \sa \ref TutorialSparseSolverConcept, class SparseLU
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*/
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template<typename _MatrixType>
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class UmfPackLU : public SparseSolverBase<UmfPackLU<_MatrixType> >
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{
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protected:
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typedef SparseSolverBase<UmfPackLU<_MatrixType> > Base;
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using Base::m_isInitialized;
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public:
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using Base::_solve_impl;
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typedef _MatrixType MatrixType;
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typedef typename MatrixType::Scalar Scalar;
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typedef typename MatrixType::RealScalar RealScalar;
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typedef typename MatrixType::StorageIndex StorageIndex;
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typedef Matrix<Scalar,Dynamic,1> Vector;
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typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
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typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
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typedef SparseMatrix<Scalar> LUMatrixType;
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typedef SparseMatrix<Scalar,ColMajor,int> UmfpackMatrixType;
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typedef Ref<const UmfpackMatrixType, StandardCompressedFormat> UmfpackMatrixRef;
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enum {
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ColsAtCompileTime = MatrixType::ColsAtCompileTime,
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MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
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};
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public:
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typedef Array<double, UMFPACK_CONTROL, 1> UmfpackControl;
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UmfPackLU()
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: m_dummy(0,0), mp_matrix(m_dummy)
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{
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init();
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}
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template<typename InputMatrixType>
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explicit UmfPackLU(const InputMatrixType& matrix)
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: mp_matrix(matrix)
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{
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init();
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compute(matrix);
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}
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~UmfPackLU()
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{
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if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
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if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar());
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}
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inline Index rows() const { return mp_matrix.rows(); }
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inline Index cols() const { return mp_matrix.cols(); }
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/** \brief Reports whether previous computation was successful.
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*
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* \returns \c Success if computation was succesful,
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* \c NumericalIssue if the matrix.appears to be negative.
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*/
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ComputationInfo info() const
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{
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eigen_assert(m_isInitialized && "Decomposition is not initialized.");
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return m_info;
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}
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inline const LUMatrixType& matrixL() const
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{
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if (m_extractedDataAreDirty) extractData();
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return m_l;
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}
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inline const LUMatrixType& matrixU() const
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{
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if (m_extractedDataAreDirty) extractData();
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return m_u;
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}
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inline const IntColVectorType& permutationP() const
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{
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if (m_extractedDataAreDirty) extractData();
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return m_p;
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}
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inline const IntRowVectorType& permutationQ() const
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{
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if (m_extractedDataAreDirty) extractData();
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return m_q;
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}
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/** Computes the sparse Cholesky decomposition of \a matrix
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* Note that the matrix should be column-major, and in compressed format for best performance.
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* \sa SparseMatrix::makeCompressed().
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*/
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template<typename InputMatrixType>
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void compute(const InputMatrixType& matrix)
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{
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if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
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if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar());
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grab(matrix.derived());
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analyzePattern_impl();
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factorize_impl();
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}
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/** Performs a symbolic decomposition on the sparcity of \a matrix.
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*
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* This function is particularly useful when solving for several problems having the same structure.
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*
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* \sa factorize(), compute()
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*/
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template<typename InputMatrixType>
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void analyzePattern(const InputMatrixType& matrix)
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{
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if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar());
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if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar());
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grab(matrix.derived());
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analyzePattern_impl();
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}
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/** Provides the return status code returned by UmfPack during the numeric
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* factorization.
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*
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* \sa factorize(), compute()
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*/
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inline int umfpackFactorizeReturncode() const
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{
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eigen_assert(m_numeric && "UmfPackLU: you must first call factorize()");
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return m_fact_errorCode;
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}
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/** Provides access to the control settings array used by UmfPack.
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*
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* If this array contains NaN's, the default values are used.
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*
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* See UMFPACK documentation for details.
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*/
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inline const UmfpackControl& umfpackControl() const
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{
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return m_control;
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}
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/** Provides access to the control settings array used by UmfPack.
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*
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* If this array contains NaN's, the default values are used.
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*
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* See UMFPACK documentation for details.
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*/
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inline UmfpackControl& umfpackControl()
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{
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return m_control;
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}
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/** Performs a numeric decomposition of \a matrix
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*
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* The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.
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*
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* \sa analyzePattern(), compute()
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*/
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template<typename InputMatrixType>
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void factorize(const InputMatrixType& matrix)
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{
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eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
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if(m_numeric)
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umfpack_free_numeric(&m_numeric,Scalar());
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grab(matrix.derived());
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factorize_impl();
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}
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/** \internal */
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template<typename BDerived,typename XDerived>
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bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
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Scalar determinant() const;
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void extractData() const;
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protected:
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void init()
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{
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m_info = InvalidInput;
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m_isInitialized = false;
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m_numeric = 0;
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m_symbolic = 0;
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m_extractedDataAreDirty = true;
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}
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void analyzePattern_impl()
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{
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umfpack_defaults(m_control.data(), Scalar());
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int errorCode = 0;
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errorCode = umfpack_symbolic(internal::convert_index<int>(mp_matrix.rows()),
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internal::convert_index<int>(mp_matrix.cols()),
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mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
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&m_symbolic, m_control.data(), 0);
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m_isInitialized = true;
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m_info = errorCode ? InvalidInput : Success;
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m_analysisIsOk = true;
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m_factorizationIsOk = false;
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m_extractedDataAreDirty = true;
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}
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void factorize_impl()
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{
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m_fact_errorCode = umfpack_numeric(mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
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m_symbolic, &m_numeric, m_control.data(), 0);
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m_info = m_fact_errorCode == UMFPACK_OK ? Success : NumericalIssue;
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m_factorizationIsOk = true;
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m_extractedDataAreDirty = true;
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}
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template<typename MatrixDerived>
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void grab(const EigenBase<MatrixDerived> &A)
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{
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mp_matrix.~UmfpackMatrixRef();
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::new (&mp_matrix) UmfpackMatrixRef(A.derived());
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}
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void grab(const UmfpackMatrixRef &A)
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{
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if(&(A.derived()) != &mp_matrix)
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{
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mp_matrix.~UmfpackMatrixRef();
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::new (&mp_matrix) UmfpackMatrixRef(A);
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}
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}
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// cached data to reduce reallocation, etc.
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mutable LUMatrixType m_l;
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int m_fact_errorCode;
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UmfpackControl m_control;
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mutable LUMatrixType m_u;
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mutable IntColVectorType m_p;
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mutable IntRowVectorType m_q;
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UmfpackMatrixType m_dummy;
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UmfpackMatrixRef mp_matrix;
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void* m_numeric;
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void* m_symbolic;
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mutable ComputationInfo m_info;
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int m_factorizationIsOk;
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int m_analysisIsOk;
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mutable bool m_extractedDataAreDirty;
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private:
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UmfPackLU(const UmfPackLU& ) { }
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};
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template<typename MatrixType>
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void UmfPackLU<MatrixType>::extractData() const
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{
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if (m_extractedDataAreDirty)
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{
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// get size of the data
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int lnz, unz, rows, cols, nz_udiag;
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umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
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// allocate data
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m_l.resize(rows,(std::min)(rows,cols));
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m_l.resizeNonZeros(lnz);
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m_u.resize((std::min)(rows,cols),cols);
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m_u.resizeNonZeros(unz);
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m_p.resize(rows);
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m_q.resize(cols);
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// extract
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umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(),
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m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(),
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m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
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m_extractedDataAreDirty = false;
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}
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}
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template<typename MatrixType>
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typename UmfPackLU<MatrixType>::Scalar UmfPackLU<MatrixType>::determinant() const
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{
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Scalar det;
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umfpack_get_determinant(&det, 0, m_numeric, 0);
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return det;
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}
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template<typename MatrixType>
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template<typename BDerived,typename XDerived>
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bool UmfPackLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const
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{
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Index rhsCols = b.cols();
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eigen_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major rhs yet");
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eigen_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major result yet");
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eigen_assert(b.derived().data() != x.derived().data() && " Umfpack does not support inplace solve");
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int errorCode;
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Scalar* x_ptr = 0;
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Matrix<Scalar,Dynamic,1> x_tmp;
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if(x.innerStride()!=1)
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{
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x_tmp.resize(x.rows());
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x_ptr = x_tmp.data();
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}
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for (int j=0; j<rhsCols; ++j)
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{
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if(x.innerStride()==1)
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x_ptr = &x.col(j).coeffRef(0);
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errorCode = umfpack_solve(UMFPACK_A,
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mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
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x_ptr, &b.const_cast_derived().col(j).coeffRef(0), m_numeric, m_control.data(), 0);
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if(x.innerStride()!=1)
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x.col(j) = x_tmp;
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if (errorCode!=0)
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return false;
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}
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return true;
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}
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} // end namespace Eigen
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#endif // EIGEN_UMFPACKSUPPORT_H
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