Compute the SVD for a general matrix A = USV^T. More...

Public Member Functions
subroutine	lapack_dgesvd (JOBU, JOBVT, M, N, A, S, U, VT, WORK, LWORK, ErrStat, ErrMsg)
	Compute singular value decomposition (SVD) for a general matrix, A. More...

subroutine	lapack_sgesvd (JOBU, JOBVT, M, N, A, S, U, VT, WORK, LWORK, ErrStat, ErrMsg)
	Compute singular value decomposition (SVD) for a general matrix, A. More...

Detailed Description

Compute the SVD for a general matrix A = USV^T.

Member Function/Subroutine Documentation

◆ lapack_dgesvd()

subroutine nwtc_lapack::lapack_gesvd::lapack_dgesvd	(	character(1), intent(in)	JOBU,
		character(1), intent(in)	JOBVT,
		integer, intent(in)	M,
		integer, intent(in)	N,
		real(r8ki), dimension( :, : ), intent(inout)	A,
		real(r8ki), dimension( : ), intent(out)	S,
		real(r8ki), dimension( :, : ), intent(out)	U,
		real(r8ki), dimension( :, : ), intent(out)	VT,
		real(r8ki), dimension( : ), intent(inout)	WORK,
		integer, intent(in)	LWORK,
		integer(intki), intent(out)	ErrStat,
		character(*), intent(out)	ErrMsg
	)

Compute singular value decomposition (SVD) for a general matrix, A.

use LAPACK_DGESVD (nwtc_lapack::lapack_dgesvd) instead of this specific function.

Parameters

[in]	jobu	'A': all M columns of U are returned in array U; 'S': the first min(m,n) columns of U (the left singular vectors) are returned in the array U; 'O': the first min(m,n) columns of U (the left singular vectors) are overwritten on the array A; 'N': no columns of U (no left singular vectors) are computed.
[in]	jobvt	'A': all N rows of V^T are returned in the array VT; 'S': the first min(m,n) rows of V^T (the right singular vectors) are returned in the array VT; 'O': the first min(m,n) rows of V^T (the right singular vectors) are overwritten on the array A; 'N': no rows of V**T (no right singular vectors) are computed.
[in]	m	The number of rows of the input matrix A. M >= 0.
[in]	n	The number of columns of the input matrix A. N >= 0.
[in,out]	a	A is DOUBLE PRECISION array, dimension (LDA,N) On entry, the M-by-N matrix A. On exit, if JOBU = 'O', A is overwritten with the first min(m,n) columns of U (the left singular vectors, stored columnwise); if JOBVT = 'O', A is overwritten with the first min(m,n) rows of V**T (the right singular vectors, stored rowwise); if JOBU .ne. 'O' and JOBVT .ne. 'O', the contents of A are destroyed.
[out]	s	S is DOUBLE PRECISION array, dimension (min(M,N)) The singular values of A, sorted so that S(i) >= S(i+1).
[out]	u	U is DOUBLE PRECISION array, dimension (LDU,UCOL) (LDU,M) if JOBU = 'A' or (LDU,min(M,N)) if JOBU = 'S'. If JOBU = 'A', U contains the M-by-M orthogonal matrix U; if JOBU = 'S', U contains the first min(m,n) columns of U (the left singular vectors, stored columnwise); if JOBU = 'N' or 'O', U is not referenced.
[out]	vt	VT is DOUBLE PRECISION array, dimension (LDVT,N) If JOBVT = 'A', VT contains the N-by-N orthogonal matrix VT; if JOBVT = 'S', VT contains the first min(m,n) rows of VT (the right singular vectors, stored rowwise); if JOBVT = 'N' or 'O', VT is not referenced.
[in,out]	work	dimension (MAX(1,LWORK)). On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
[in]	lwork	The dimension of the array WORK. LWORK >= MAX(1,5MIN(M,N)) for the paths (see comments inside code): PATH 1 (M much larger than N, JOBU='N') PATH 1t (N much larger than M, JOBVT='N') LWORK >= MAX(1,3MIN(M,N) + MAX(M,N),5*MIN(M,N)) for the other paths For good performance, LWORK should generally be larger. If LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the WORK array, returns this value as the first entry of the WORK array, and no error message related to LWORK is issued by XERBLA.
[out]	errstat	Error level
[out]	errmsg	Message describing error

◆ lapack_sgesvd()

subroutine nwtc_lapack::lapack_gesvd::lapack_sgesvd	(	character(1), intent(in)	JOBU,
		character(1), intent(in)	JOBVT,
		integer, intent(in)	M,
		integer, intent(in)	N,
		real(siki), dimension( :, : ), intent(inout)	A,
		real(siki), dimension( : ), intent(out)	S,
		real(siki), dimension( :, : ), intent(out)	U,
		real(siki), dimension( :, : ), intent(out)	VT,
		real(siki), dimension( : ), intent(inout)	WORK,
		integer, intent(in)	LWORK,
		integer(intki), intent(out)	ErrStat,
		character(*), intent(out)	ErrMsg
	)

Compute singular value decomposition (SVD) for a general matrix, A.

use LAPACK_SGESVD (nwtc_lapack::lapack_sgesvd) instead of this specific function.

Parameters

[in]	jobu	'A': all M columns of U are returned in array U; 'S': the first min(m,n) columns of U (the left singular vectors) are returned in the array U; 'O': the first min(m,n) columns of U (the left singular vectors) are overwritten on the array A; 'N': no columns of U (no left singular vectors) are computed.
[in]	jobvt	'A': all N rows of V^T are returned in the array VT; 'S': the first min(m,n) rows of V^T (the right singular vectors) are returned in the array VT; 'O': the first min(m,n) rows of V^T (the right singular vectors) are overwritten on the array A; 'N': no rows of V**T (no right singular vectors) are computed.
[in]	m	The number of rows of the input matrix A. M >= 0.
[in]	n	The number of columns of the input matrix A. N >= 0.
[in,out]	a	A is SINGLE PRECISION array, dimension (LDA,N) On entry, the M-by-N matrix A. On exit, if JOBU = 'O', A is overwritten with the first min(m,n) columns of U (the left singular vectors, stored columnwise); if JOBVT = 'O', A is overwritten with the first min(m,n) rows of V**T (the right singular vectors, stored rowwise); if JOBU .ne. 'O' and JOBVT .ne. 'O', the contents of A are destroyed.
[out]	s	S is SINGLE PRECISION array, dimension (min(M,N)) The singular values of A, sorted so that S(i) >= S(i+1).
[out]	u	U is SINGLE PRECISION array, dimension (LDU,UCOL) (LDU,M) if JOBU = 'A' or (LDU,min(M,N)) if JOBU = 'S'. If JOBU = 'A', U contains the M-by-M orthogonal matrix U; if JOBU = 'S', U contains the first min(m,n) columns of U (the left singular vectors, stored columnwise); if JOBU = 'N' or 'O', U is not referenced.
[out]	vt	VT is SINGLE PRECISION array, dimension (LDVT,N) If JOBVT = 'A', VT contains the N-by-N orthogonal matrix VT; if JOBVT = 'S', VT contains the first min(m,n) rows of VT (the right singular vectors, stored rowwise); if JOBVT = 'N' or 'O', VT is not referenced.
[in,out]	work	dimension (MAX(1,LWORK)). On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
[in]	lwork	The dimension of the array WORK. LWORK >= MAX(1,5MIN(M,N)) for the paths (see comments inside code): PATH 1 (M much larger than N, JOBU='N') PATH 1t (N much larger than M, JOBVT='N') LWORK >= MAX(1,3MIN(M,N) + MAX(M,N),5*MIN(M,N)) for the other paths For good performance, LWORK should generally be larger. If LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the WORK array, returns this value as the first entry of the WORK array, and no error message related to LWORK is issued by XERBLA.
[out]	errstat	Error level
[out]	errmsg	Message describing error

The documentation for this interface was generated from the following file:

NWTC_LAPACK.f90

Public Member Functions

Detailed Description

Member Function/Subroutine Documentation

◆ lapack_dgesvd()

◆ lapack_sgesvd()