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Revert "Update SVD Module to allow specifying computation options with a...

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This commit is contained in:
Rasmus Munk Larsen
2021-11-30 18:45:54 +00:00
committed by David Tellenbach
parent 4dd126c630
commit 085c2fc5d5
23 changed files with 634 additions and 764 deletions
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104
lapack/svd.cpp
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@@ -10,7 +10,6 @@
#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))
@@ -48,97 +47,40 @@ EIGEN_LAPACK_FUNC(gesdd,(char *jobz, int *m, int* n, Scalar* a, int *lda, RealSc
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')
{
BDCSVD<PlainMatrixType, ComputeFullU|ComputeFullV> svd(mat);
make_vector(s,diag_size) = svd.singularValues().head(diag_size);
matrix(u,*m,*m,*ldu) = svd.matrixU();
matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
matrix(u,*m,*m,*ldu) = svd.matrixU();
matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
}
else if(*jobz=='S')
{
BDCSVD<PlainMatrixType, ComputeThinU|ComputeThinV> svd(mat);
make_vector(s,diag_size) = svd.singularValues().head(diag_size);
matrix(u,*m,diag_size,*ldu) = svd.matrixU();
matrix(vt,diag_size,*n,*ldvt) = svd.matrixV().adjoint();
}
else if(*jobz=='O' && *m>=*n)
{
BDCSVD<PlainMatrixType, ComputeThinU|ComputeThinV> svd(mat);
make_vector(s,diag_size) = svd.singularValues().head(diag_size);
matrix(a,*m,*n,*lda) = svd.matrixU();
matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
matrix(a,*m,*n,*lda) = svd.matrixU();
matrix(vt,*n,*n,*ldvt) = svd.matrixV().adjoint();
}
else if(*jobz=='O')
{
BDCSVD<PlainMatrixType, ComputeThinU|ComputeThinV> svd(mat);
make_vector(s,diag_size) = svd.singularValues().head(diag_size);
matrix(u,*m,*m,*ldu) = svd.matrixU();
matrix(a,diag_size,*n,*lda) = svd.matrixV().adjoint();
}
else
{
BDCSVD<PlainMatrixType> svd(mat);
make_vector(s,diag_size) = svd.singularValues().head(diag_size);
}
return 0;
}
template<typename MatrixType, int Options>
void gesvdAssignmentHelper(MatrixType& mat, char* jobu, char* jobv, int* m, int* n, int diag_size, Scalar* a, int* lda, RealScalar* s, Scalar* u, int* ldu, Scalar* vt, int* ldvt)
{
JacobiSVD<MatrixType, Options> svd(mat);
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();
}
}
template<typename MatrixType, int Options, typename ...Args>
void gesvdSetVOptions(MatrixType& mat, char* jobu, char* jobv, Args... args)
{
if (*jobv=='A')
{
gesvdAssignmentHelper<MatrixType, Options | ComputeFullV>(mat, jobu, jobv, args...);
}
else if (*jobv=='S' || *jobv=='O')
{
gesvdAssignmentHelper<MatrixType, Options | ComputeThinV>(mat, jobu, jobv, args...);
}
else
{
gesvdAssignmentHelper<MatrixType, Options>(mat, jobu, jobv, args...);
}
}
template<typename MatrixType, typename ...Args>
void gesvdSetUOptions(MatrixType& mat, char* jobu, char* jobv, Args... args)
{
if (*jobu=='A')
{
gesvdSetVOptions<MatrixType, ComputeFullU>(mat, jobu, jobv, args...);
}
else if (*jobu=='S' || *jobu=='O')
{
gesvdSetVOptions<MatrixType, ComputeThinU>(mat, jobu, jobv, args...);
}
else
{
gesvdSetVOptions<MatrixType, 0>(mat, jobu, jobv, args...);
}
}
// 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))
@@ -175,8 +117,22 @@ EIGEN_LAPACK_FUNC(gesvd,(char *jobu, char *jobv, int *m, int* n, Scalar* a, int
PlainMatrixType mat(*m,*n);
mat = matrix(a,*m,*n,*lda);
gesvdSetUOptions<PlainMatrixType>(mat, jobu, jobv, m, n, diag_size, a, lda, s, u, ldu, vt, ldvt);
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;
}
}