lu.cpp revision 7faaa9f3f0df9d23790277834d426c3d992ac3ba
1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2010-2011 Gael Guennebaud <gael.guennebaud@inria.fr> 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla 7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed 8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "common.h" 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/LU> 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_LAPACK_FUNC(getrf,(int *m, int *n, RealScalar *pa, int *lda, int *ipiv, int *info)) 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath *info = 0; 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(*m<0) *info = -1; 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else if(*n<0) *info = -2; 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else if(*lda<std::max(1,*m)) *info = -4; 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(*info!=0) 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int e = -*info; 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return xerbla_(SCALAR_SUFFIX_UP"GETRF", &e, 6); 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(*m==0 || *n==0) 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return 0; 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar* a = reinterpret_cast<Scalar*>(pa); 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int nb_transpositions; 317faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez int ret = int(Eigen::internal::partial_lu_impl<Scalar,ColMajor,int> 327faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez ::blocked_lu(*m, *n, a, *lda, ipiv, nb_transpositions)); 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i=0; i<std::min(*m,*n); ++i) 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ipiv[i]++; 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(ret>=0) 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath *info = ret+1; 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return 0; 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//GETRS solves a system of linear equations 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// A * X = B or A' * X = B 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with a general N-by-N matrix A using the LU factorization computed by GETRF 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_LAPACK_FUNC(getrs,(char *trans, int *n, int *nrhs, RealScalar *pa, int *lda, int *ipiv, RealScalar *pb, int *ldb, int *info)) 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath *info = 0; 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(OP(*trans)==INVALID) *info = -1; 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else if(*n<0) *info = -2; 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else if(*nrhs<0) *info = -3; 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else if(*lda<std::max(1,*n)) *info = -5; 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else if(*ldb<std::max(1,*n)) *info = -8; 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(*info!=0) 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int e = -*info; 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return xerbla_(SCALAR_SUFFIX_UP"GETRS", &e, 6); 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar* a = reinterpret_cast<Scalar*>(pa); 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar* b = reinterpret_cast<Scalar*>(pb); 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType lu(a,*n,*n,*lda); 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType B(b,*n,*nrhs,*ldb); 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i=0; i<*n; ++i) 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ipiv[i]--; 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(OP(*trans)==NOTR) 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath B = PivotsType(ipiv,*n) * B; 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath lu.triangularView<UnitLower>().solveInPlace(B); 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath lu.triangularView<Upper>().solveInPlace(B); 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else if(OP(*trans)==TR) 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath lu.triangularView<Upper>().transpose().solveInPlace(B); 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath lu.triangularView<UnitLower>().transpose().solveInPlace(B); 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath B = PivotsType(ipiv,*n).transpose() * B; 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else if(OP(*trans)==ADJ) 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath lu.triangularView<Upper>().adjoint().solveInPlace(B); 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath lu.triangularView<UnitLower>().adjoint().solveInPlace(B); 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath B = PivotsType(ipiv,*n).transpose() * B; 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i=0; i<*n; ++i) 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ipiv[i]++; 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return 0; 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 90