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Add lapack interface to JacobiSVD and BDCSVD

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This commit is contained in:
Gael Guennebaud 2014-10-17 15:31:11 +02:00
parent c566cfe2ba
commit 8472e697ca
9 changed files with 157 additions and 27 deletions
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2
Eigen/src/Core/util/Memory.h
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@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2009 Kenneth Riddile <kfriddile@yahoo.com>
// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>

2
Eigen/src/SparseCore/CompressedStorage.h
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@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2008-2014 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

3
lapack/complex_double.cpp
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@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009-2014 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
@ -15,3 +15,4 @@
#include "cholesky.cpp"
#include "lu.cpp"
#include "svd.cpp"

3
lapack/complex_single.cpp
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@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009-2014 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
@ -15,3 +15,4 @@
#include "cholesky.cpp"
#include "lu.cpp"
#include "svd.cpp"

3
lapack/double.cpp
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@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009-2014 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
@ -15,3 +15,4 @@
#include "cholesky.cpp"
#include "lu.cpp"
#include "eigenvalues.cpp"
#include "svd.cpp"

23
lapack/eigenvalues.cpp
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@ -7,10 +7,10 @@
// 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 "common.h"
#include "lapack_common.h"
#include <Eigen/Eigenvalues>
// computes an LU factorization of a general M-by-N matrix A using partial pivoting with row interchanges
// computes eigen values and vectors of a general N-by-N matrix A
EIGEN_LAPACK_FUNC(syev,(char *jobz, char *uplo, int* n, Scalar* a, int *lda, Scalar* w, Scalar* /*work*/, int* lwork, int *info))
{
// TODO exploit the work buffer
@ -22,24 +22,7 @@ EIGEN_LAPACK_FUNC(syev,(char *jobz, char *uplo, int* n, Scalar* a, int *lda, Sca
else if(*n<0) *info = -3;
else if(*lda<std::max(1,*n)) *info = -5;
else if((!query_size) && *lwork<std::max(1,3**n-1)) *info = -8;
// if(*info==0)
// {
// int nb = ILAENV( 1, 'SSYTRD', UPLO, N, -1, -1, -1 )
// LWKOPT = MAX( 1, ( NB+2 )*N )
// WORK( 1 ) = LWKOPT
// *
// IF( LWORK.LT.MAX( 1, 3*N-1 ) .AND. .NOT.LQUERY )
// $ INFO = -8
// END IF
// *
// IF( INFO.NE.0 ) THEN
// CALL XERBLA( 'SSYEV ', -INFO )
// RETURN
// ELSE IF( LQUERY ) THEN
// RETURN
// END IF
if(*info!=0)
{
int e = -*info;

7
lapack/lapack_common.h
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@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2010-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010-2014 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
@ -18,6 +18,11 @@
typedef Eigen::Map<Eigen::Transpositions<Eigen::Dynamic,Eigen::Dynamic,int> > PivotsType;
#if ISCOMPLEX
#define EIGEN_LAPACK_ARG_IF_COMPLEX(X) X,
#else
#define EIGEN_LAPACK_ARG_IF_COMPLEX(X)
#endif
#endif // EIGEN_LAPACK_COMMON_H

3
lapack/single.cpp
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@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009-2014 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
@ -15,3 +15,4 @@
#include "cholesky.cpp"
#include "lu.cpp"
#include "eigenvalues.cpp"
#include "svd.cpp"

138
lapack/svd.cpp Normal file
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@ -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>
#include <unsupported/Eigen/BDCSVD>
// 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;
}