svd.cpp
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#ifndef _MSC_VER
# include <itpp/config.h>
#else
# include <itpp/config_msvc.h>
#endif
#if defined(HAVE_LAPACK)
# include <itpp/base/algebra/lapack.h>
#endif
#include <itpp/base/algebra/svd.h>
namespace itpp
{
#if defined(HAVE_LAPACK)
bool svd(const mat &A, vec &S)
{
char jobu = 'N', jobvt = 'N';
int m, n, lda, ldu, ldvt, lwork, info;
m = lda = ldu = A.rows();
n = ldvt = A.cols();
lwork = std::max(3 * std::min(m, n) + std::max(m, n), 5 * std::min(m, n));
mat U, V;
S.set_size(std::min(m, n), false);
vec work(lwork);
mat B(A);
// The theoretical calculation of lwork above results in the minimum size
// needed for dgesvd_ to run to completion without having memory errors.
// For speed improvement it is best to set lwork=-1 and have dgesvd_
// calculate the best workspace requirement.
int lwork_tmp = -1;
dgesvd_(&jobu, &jobvt, &m, &n, B._data(), &lda, S._data(), U._data(), &ldu,
V._data(), &ldvt, work._data(), &lwork_tmp, &info);
if (info == 0) {
lwork = static_cast<int>(work(0));
work.set_size(lwork, false);
}
dgesvd_(&jobu, &jobvt, &m, &n, B._data(), &lda, S._data(), U._data(), &ldu,
V._data(), &ldvt, work._data(), &lwork, &info);
return (info == 0);
}
bool svd(const cmat &A, vec &S)
{
char jobu = 'N', jobvt = 'N';
int m, n, lda, ldu, ldvt, lwork, info;
m = lda = ldu = A.rows();
n = ldvt = A.cols();
lwork = 2 * std::min(m, n) + std::max(m, n);
cvec U, V;
S.set_size(std::min(m, n), false);
cvec work(lwork);
vec rwork(5*std::min(m, n));
cmat B(A);
// The theoretical calculation of lwork above results in the minimum size
// needed for zgesvd_ to run to completion without having memory errors.
// For speed improvement it is best to set lwork=-1 and have zgesvd_
// calculate the best workspace requirement.
int lwork_tmp = -1;
zgesvd_(&jobu, &jobvt, &m, &n, B._data(), &lda, S._data(), U._data(), &ldu,
V._data(), &ldvt, work._data(), &lwork_tmp, rwork._data(), &info);
if (info == 0) {
lwork = static_cast<int>(real(work(0)));
work.set_size(lwork, false);
}
zgesvd_(&jobu, &jobvt, &m, &n, B._data(), &lda, S._data(), U._data(), &ldu,
V._data(), &ldvt, work._data(), &lwork, rwork._data(), &info);
return (info == 0);
}
bool svd(const mat &A, mat &U, vec &S, mat &V)
{
char jobu = 'A', jobvt = 'A';
int m, n, lda, ldu, ldvt, lwork, info;
m = lda = ldu = A.rows();
n = ldvt = A.cols();
lwork = std::max(3 * std::min(m, n) + std::max(m, n), 5 * std::min(m, n));
U.set_size(m, m, false);
V.set_size(n, n, false);
S.set_size(std::min(m, n), false);
vec work(lwork);
mat B(A);
// The theoretical calculation of lwork above results in the minimum size
// needed for dgesvd_ to run to completion without having memory errors.
// For speed improvement it is best to set lwork=-1 and have dgesvd_
// calculate the best workspace requirement.
int lwork_tmp = -1;
dgesvd_(&jobu, &jobvt, &m, &n, B._data(), &lda, S._data(), U._data(), &ldu,
V._data(), &ldvt, work._data(), &lwork_tmp, &info);
if (info == 0) {
lwork = static_cast<int>(work(0));
work.set_size(lwork, false);
}
dgesvd_(&jobu, &jobvt, &m, &n, B._data(), &lda, S._data(), U._data(), &ldu,
V._data(), &ldvt, work._data(), &lwork, &info);
V = V.T(); // This is probably slow!!!
return (info == 0);
}
bool svd(const cmat &A, cmat &U, vec &S, cmat &V)
{
char jobu = 'A', jobvt = 'A';
int m, n, lda, ldu, ldvt, lwork, info;
m = lda = ldu = A.rows();
n = ldvt = A.cols();
lwork = 2 * std::min(m, n) + std::max(m, n);
U.set_size(m, m, false);
V.set_size(n, n, false);
S.set_size(std::min(m, n), false);
cvec work(lwork);
vec rwork(5 * std::min(m, n));
cmat B(A);
// The theoretical calculation of lwork above results in the minimum size
// needed for zgesvd_ to run to completion without having memory errors.
// For speed improvement it is best to set lwork=-1 and have zgesvd_
// calculate the best workspace requirement.
int lwork_tmp = -1;
zgesvd_(&jobu, &jobvt, &m, &n, B._data(), &lda, S._data(), U._data(), &ldu,
V._data(), &ldvt, work._data(), &lwork_tmp, rwork._data(), &info);
if (info == 0) {
lwork = static_cast<int>(real(work(0)));
work.set_size(lwork, false);
}
zgesvd_(&jobu, &jobvt, &m, &n, B._data(), &lda, S._data(), U._data(), &ldu,
V._data(), &ldvt, work._data(), &lwork, rwork._data(), &info);
V = V.H(); // This is slow!!!
return (info == 0);
}
#else
bool svd(const mat &A, vec &S)
{
it_error("LAPACK library is needed to use svd() function");
return false;
}
bool svd(const cmat &A, vec &S)
{
it_error("LAPACK library is needed to use svd() function");
return false;
}
bool svd(const mat &A, mat &U, vec &S, mat &V)
{
it_error("LAPACK library is needed to use svd() function");
return false;
}
bool svd(const cmat &A, cmat &U, vec &S, cmat &V)
{
it_error("LAPACK library is needed to use svd() function");
return false;
}
#endif // HAVE_LAPACK
vec svd(const mat &A)
{
vec S;
svd(A, S);
return S;
}
vec svd(const cmat &A)
{
vec S;
svd(A, S);
return S;
}
} //namespace itpp
itpp::min
T min(const Vec< T > &in)
Minimum value of vector.
Definition: min_max.h:125
itpp::svd
bool svd(const mat &A, vec &S)
Get singular values s of a real matrix A using SVD.
Definition: svd.cpp:185
itpp::max
T max(const Vec< T > &v)
Maximum value of vector.
Definition: min_max.h:45
it_error
#define it_error(s)
Abort unconditionally.
Definition: itassert.h:126
svd.h
Definitions of Singular Value Decompositions.
itpp::real
vec real(const cvec &data)
Real part of complex values.
Definition: elem_math.cpp:157

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