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author | Daniel Genrich <daniel.genrich@gmx.net> | 2012-10-15 20:29:23 +0400 |
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committer | Daniel Genrich <daniel.genrich@gmx.net> | 2012-10-15 20:29:23 +0400 |
commit | 827c70abd8c81089af13c4738e0586bf44e501ea (patch) | |
tree | 2485e8cadb6fcc7363322431eebd22fe846de858 /extern/Eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h | |
parent | e3ea7187cedfb92741dc6667a604a873a56670e7 (diff) |
Update to stable Eigen 3.1.1
- Fixes several bugs within the Eigen library:
http://eigen.tuxfamily.org/index.php?title=ChangeLog#Eigen_3.1.1
Diffstat (limited to 'extern/Eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h')
-rw-r--r-- | extern/Eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h | 431 |
1 files changed, 431 insertions, 0 deletions
diff --git a/extern/Eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h b/extern/Eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h new file mode 100644 index 00000000000..f01720362de --- /dev/null +++ b/extern/Eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h @@ -0,0 +1,431 @@ +// 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,&internal::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,&internal::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,&internal::real_ref(Ax[0]),0,&internal::real_ref(X[0]),0,&internal::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 = internal::real_ref(Lx[0]); + double& ux0_real = internal::real_ref(Ux[0]); + double& dx0_real = internal::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 = internal::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 : internal::noncopyable +{ + public: + typedef _MatrixType MatrixType; + typedef typename MatrixType::Scalar Scalar; + typedef typename MatrixType::RealScalar RealScalar; + typedef typename MatrixType::Index Index; + 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; + + public: + + UmfPackLU() { init(); } + + UmfPackLU(const MatrixType& 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 m_copyMatrix.rows(); } + inline Index cols() const { return m_copyMatrix.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(). + */ + void compute(const MatrixType& matrix) + { + analyzePattern(matrix); + factorize(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<UmfPackLU, Rhs> solve(const MatrixBase<Rhs>& b) const + { + eigen_assert(m_isInitialized && "UmfPackLU is not initialized."); + eigen_assert(rows()==b.rows() + && "UmfPackLU::solve(): invalid number of rows of the right hand side matrix b"); + return internal::solve_retval<UmfPackLU, 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<UmfPAckLU, Rhs> solve(const SparseMatrixBase<Rhs>& b) const +// { +// eigen_assert(m_isInitialized && "UmfPAckLU is not initialized."); +// eigen_assert(rows()==b.rows() +// && "UmfPAckLU::solve(): invalid number of rows of the right hand side matrix b"); +// return internal::sparse_solve_retval<UmfPAckLU, Rhs>(*this, b.derived()); +// } + + /** 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() + */ + void analyzePattern(const MatrixType& matrix) + { + if(m_symbolic) + umfpack_free_symbolic(&m_symbolic,Scalar()); + if(m_numeric) + umfpack_free_numeric(&m_numeric,Scalar()); + + grapInput(matrix); + + int errorCode = 0; + errorCode = umfpack_symbolic(matrix.rows(), matrix.cols(), m_outerIndexPtr, m_innerIndexPtr, m_valuePtr, + &m_symbolic, 0, 0); + + m_isInitialized = true; + m_info = errorCode ? InvalidInput : Success; + m_analysisIsOk = true; + m_factorizationIsOk = false; + } + + /** 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() + */ + void factorize(const MatrixType& matrix) + { + eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()"); + if(m_numeric) + umfpack_free_numeric(&m_numeric,Scalar()); + + grapInput(matrix); + + int errorCode; + errorCode = umfpack_numeric(m_outerIndexPtr, m_innerIndexPtr, m_valuePtr, + m_symbolic, &m_numeric, 0, 0); + + m_info = errorCode ? NumericalIssue : Success; + m_factorizationIsOk = true; + } + + #ifndef EIGEN_PARSED_BY_DOXYGEN + /** \internal */ + template<typename BDerived,typename XDerived> + bool _solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const; + #endif + + Scalar determinant() const; + + void extractData() const; + + protected: + + + void init() + { + m_info = InvalidInput; + m_isInitialized = false; + m_numeric = 0; + m_symbolic = 0; + m_outerIndexPtr = 0; + m_innerIndexPtr = 0; + m_valuePtr = 0; + } + + void grapInput(const MatrixType& mat) + { + m_copyMatrix.resize(mat.rows(), mat.cols()); + if( ((MatrixType::Flags&RowMajorBit)==RowMajorBit) || sizeof(typename MatrixType::Index)!=sizeof(int) || !mat.isCompressed() ) + { + // non supported input -> copy + m_copyMatrix = mat; + m_outerIndexPtr = m_copyMatrix.outerIndexPtr(); + m_innerIndexPtr = m_copyMatrix.innerIndexPtr(); + m_valuePtr = m_copyMatrix.valuePtr(); + } + else + { + m_outerIndexPtr = mat.outerIndexPtr(); + m_innerIndexPtr = mat.innerIndexPtr(); + m_valuePtr = mat.valuePtr(); + } + } + + // cached data to reduce reallocation, etc. + mutable LUMatrixType m_l; + mutable LUMatrixType m_u; + mutable IntColVectorType m_p; + mutable IntRowVectorType m_q; + + UmfpackMatrixType m_copyMatrix; + const Scalar* m_valuePtr; + const int* m_outerIndexPtr; + const int* m_innerIndexPtr; + void* m_numeric; + void* m_symbolic; + + mutable ComputationInfo m_info; + bool m_isInitialized; + 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(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const +{ + const int 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"); + + int errorCode; + for (int j=0; j<rhsCols; ++j) + { + errorCode = umfpack_solve(UMFPACK_A, + m_outerIndexPtr, m_innerIndexPtr, m_valuePtr, + &x.col(j).coeffRef(0), &b.const_cast_derived().col(j).coeffRef(0), m_numeric, 0, 0); + if (errorCode!=0) + return false; + } + + return true; +} + + +namespace internal { + +template<typename _MatrixType, typename Rhs> +struct solve_retval<UmfPackLU<_MatrixType>, Rhs> + : solve_retval_base<UmfPackLU<_MatrixType>, Rhs> +{ + typedef UmfPackLU<_MatrixType> Dec; + EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) + + template<typename Dest> void evalTo(Dest& dst) const + { + dec()._solve(rhs(),dst); + } +}; + +template<typename _MatrixType, typename Rhs> +struct sparse_solve_retval<UmfPackLU<_MatrixType>, Rhs> + : sparse_solve_retval_base<UmfPackLU<_MatrixType>, Rhs> +{ + typedef UmfPackLU<_MatrixType> Dec; + EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) + + template<typename Dest> void evalTo(Dest& dst) const + { + dec()._solve(rhs(),dst); + } +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_UMFPACKSUPPORT_H |