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Diffstat (limited to 'eigen/lapack/svd.cpp')
-rw-r--r-- | eigen/lapack/svd.cpp | 138 |
1 files changed, 138 insertions, 0 deletions
diff --git a/eigen/lapack/svd.cpp b/eigen/lapack/svd.cpp new file mode 100644 index 0000000..77b302b --- /dev/null +++ b/eigen/lapack/svd.cpp @@ -0,0 +1,138 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2014 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/. + +#include "lapack_common.h" +#include <Eigen/SVD> + +// computes the singular values/vectors a general M-by-N matrix A using divide-and-conquer +EIGEN_LAPACK_FUNC(gesdd,(char *jobz, int *m, int* n, Scalar* a, int *lda, RealScalar *s, Scalar *u, int *ldu, Scalar *vt, int *ldvt, Scalar* /*work*/, int* lwork, + EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int * /*iwork*/, int *info)) +{ + // TODO exploit the work buffer + bool query_size = *lwork==-1; + int diag_size = (std::min)(*m,*n); + + *info = 0; + if(*jobz!='A' && *jobz!='S' && *jobz!='O' && *jobz!='N') *info = -1; + else if(*m<0) *info = -2; + else if(*n<0) *info = -3; + else if(*lda<std::max(1,*m)) *info = -5; + else if(*lda<std::max(1,*m)) *info = -8; + else if(*ldu <1 || (*jobz=='A' && *ldu <*m) + || (*jobz=='O' && *m<*n && *ldu<*m)) *info = -8; + else if(*ldvt<1 || (*jobz=='A' && *ldvt<*n) + || (*jobz=='S' && *ldvt<diag_size) + || (*jobz=='O' && *m>=*n && *ldvt<*n)) *info = -10; + + if(*info!=0) + { + int e = -*info; + return xerbla_(SCALAR_SUFFIX_UP"GESDD ", &e, 6); + } + + if(query_size) + { + *lwork = 0; + return 0; + } + + if(*n==0 || *m==0) + return 0; + + PlainMatrixType mat(*m,*n); + mat = matrix(a,*m,*n,*lda); + + int option = *jobz=='A' ? ComputeFullU|ComputeFullV + : *jobz=='S' ? ComputeThinU|ComputeThinV + : *jobz=='O' ? ComputeThinU|ComputeThinV + : 0; + + BDCSVD<PlainMatrixType> svd(mat,option); + + make_vector(s,diag_size) = svd.singularValues().head(diag_size); + + if(*jobz=='A') + { + matrix(u,*m,*m,*ldu) = svd.matrixU(); + matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint(); + } + else if(*jobz=='S') + { + matrix(u,*m,diag_size,*ldu) = svd.matrixU(); + matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint(); + } + else if(*jobz=='O' && *m>=*n) + { + matrix(a,*m,*n,*lda) = svd.matrixU(); + matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint(); + } + else if(*jobz=='O') + { + matrix(u,*m,*m,*ldu) = svd.matrixU(); + matrix(a,diag_size,*n,*lda) = svd.matrixV().adjoint(); + } + + return 0; +} + +// computes the singular values/vectors a general M-by-N matrix A using two sided jacobi algorithm +EIGEN_LAPACK_FUNC(gesvd,(char *jobu, char *jobv, int *m, int* n, Scalar* a, int *lda, RealScalar *s, Scalar *u, int *ldu, Scalar *vt, int *ldvt, Scalar* /*work*/, int* lwork, + EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int *info)) +{ + // TODO exploit the work buffer + bool query_size = *lwork==-1; + int diag_size = (std::min)(*m,*n); + + *info = 0; + if( *jobu!='A' && *jobu!='S' && *jobu!='O' && *jobu!='N') *info = -1; + else if((*jobv!='A' && *jobv!='S' && *jobv!='O' && *jobv!='N') + || (*jobu=='O' && *jobv=='O')) *info = -2; + else if(*m<0) *info = -3; + else if(*n<0) *info = -4; + else if(*lda<std::max(1,*m)) *info = -6; + else if(*ldu <1 || ((*jobu=='A' || *jobu=='S') && *ldu<*m)) *info = -9; + else if(*ldvt<1 || (*jobv=='A' && *ldvt<*n) + || (*jobv=='S' && *ldvt<diag_size)) *info = -11; + + if(*info!=0) + { + int e = -*info; + return xerbla_(SCALAR_SUFFIX_UP"GESVD ", &e, 6); + } + + if(query_size) + { + *lwork = 0; + return 0; + } + + if(*n==0 || *m==0) + return 0; + + PlainMatrixType mat(*m,*n); + mat = matrix(a,*m,*n,*lda); + + int option = (*jobu=='A' ? ComputeFullU : *jobu=='S' || *jobu=='O' ? ComputeThinU : 0) + | (*jobv=='A' ? ComputeFullV : *jobv=='S' || *jobv=='O' ? ComputeThinV : 0); + + JacobiSVD<PlainMatrixType> svd(mat,option); + + make_vector(s,diag_size) = svd.singularValues().head(diag_size); + { + if(*jobu=='A') matrix(u,*m,*m,*ldu) = svd.matrixU(); + else if(*jobu=='S') matrix(u,*m,diag_size,*ldu) = svd.matrixU(); + else if(*jobu=='O') matrix(a,*m,diag_size,*lda) = svd.matrixU(); + } + { + if(*jobv=='A') matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint(); + else if(*jobv=='S') matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint(); + else if(*jobv=='O') matrix(a,diag_size,*n,*lda) = svd.matrixV().adjoint(); + } + return 0; +} |