139 lines
4.8 KiB
C++
139 lines
4.8 KiB
C++
// 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) 2014 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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#include "lapack_common.h"
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#include <Eigen/SVD>
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// computes the singular values/vectors a general M-by-N matrix A using divide-and-conquer
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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,
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EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int * /*iwork*/, int *info))
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{
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// TODO exploit the work buffer
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bool query_size = *lwork==-1;
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int diag_size = (std::min)(*m,*n);
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*info = 0;
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if(*jobz!='A' && *jobz!='S' && *jobz!='O' && *jobz!='N') *info = -1;
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else if(*m<0) *info = -2;
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else if(*n<0) *info = -3;
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else if(*lda<std::max(1,*m)) *info = -5;
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else if(*lda<std::max(1,*m)) *info = -8;
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else if(*ldu <1 || (*jobz=='A' && *ldu <*m)
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|| (*jobz=='O' && *m<*n && *ldu<*m)) *info = -8;
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else if(*ldvt<1 || (*jobz=='A' && *ldvt<*n)
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|| (*jobz=='S' && *ldvt<diag_size)
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|| (*jobz=='O' && *m>=*n && *ldvt<*n)) *info = -10;
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if(*info!=0)
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{
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int e = -*info;
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return xerbla_(SCALAR_SUFFIX_UP"GESDD ", &e, 6);
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}
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if(query_size)
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{
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*lwork = 0;
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return 0;
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}
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if(*n==0 || *m==0)
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return 0;
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PlainMatrixType mat(*m,*n);
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mat = matrix(a,*m,*n,*lda);
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int option = *jobz=='A' ? ComputeFullU|ComputeFullV
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: *jobz=='S' ? ComputeThinU|ComputeThinV
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: *jobz=='O' ? ComputeThinU|ComputeThinV
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: 0;
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BDCSVD<PlainMatrixType> svd(mat,option);
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make_vector(s,diag_size) = svd.singularValues().head(diag_size);
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if(*jobz=='A')
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{
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matrix(u,*m,*m,*ldu) = svd.matrixU();
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matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
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}
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else if(*jobz=='S')
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{
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matrix(u,*m,diag_size,*ldu) = svd.matrixU();
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matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint();
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}
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else if(*jobz=='O' && *m>=*n)
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{
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matrix(a,*m,*n,*lda) = svd.matrixU();
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matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
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}
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else if(*jobz=='O')
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{
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matrix(u,*m,*m,*ldu) = svd.matrixU();
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matrix(a,diag_size,*n,*lda) = svd.matrixV().adjoint();
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}
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return 0;
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}
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// computes the singular values/vectors a general M-by-N matrix A using two sided jacobi algorithm
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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,
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EIGEN_LAPACK_ARG_IF_COMPLEX(RealScalar */*rwork*/) int *info))
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{
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// TODO exploit the work buffer
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bool query_size = *lwork==-1;
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int diag_size = (std::min)(*m,*n);
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*info = 0;
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if( *jobu!='A' && *jobu!='S' && *jobu!='O' && *jobu!='N') *info = -1;
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else if((*jobv!='A' && *jobv!='S' && *jobv!='O' && *jobv!='N')
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|| (*jobu=='O' && *jobv=='O')) *info = -2;
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else if(*m<0) *info = -3;
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else if(*n<0) *info = -4;
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else if(*lda<std::max(1,*m)) *info = -6;
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else if(*ldu <1 || ((*jobu=='A' || *jobu=='S') && *ldu<*m)) *info = -9;
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else if(*ldvt<1 || (*jobv=='A' && *ldvt<*n)
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|| (*jobv=='S' && *ldvt<diag_size)) *info = -11;
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if(*info!=0)
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{
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int e = -*info;
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return xerbla_(SCALAR_SUFFIX_UP"GESVD ", &e, 6);
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}
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if(query_size)
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{
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*lwork = 0;
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return 0;
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}
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if(*n==0 || *m==0)
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return 0;
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PlainMatrixType mat(*m,*n);
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mat = matrix(a,*m,*n,*lda);
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int option = (*jobu=='A' ? ComputeFullU : *jobu=='S' || *jobu=='O' ? ComputeThinU : 0)
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| (*jobv=='A' ? ComputeFullV : *jobv=='S' || *jobv=='O' ? ComputeThinV : 0);
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JacobiSVD<PlainMatrixType> svd(mat,option);
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make_vector(s,diag_size) = svd.singularValues().head(diag_size);
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{
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if(*jobu=='A') matrix(u,*m,*m,*ldu) = svd.matrixU();
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else if(*jobu=='S') matrix(u,*m,diag_size,*ldu) = svd.matrixU();
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else if(*jobu=='O') matrix(a,*m,diag_size,*lda) = svd.matrixU();
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}
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{
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if(*jobv=='A') matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
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else if(*jobv=='S') matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint();
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else if(*jobv=='O') matrix(a,diag_size,*n,*lda) = svd.matrixV().adjoint();
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}
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return 0;
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}
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