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// 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;
}
|
