zgesdd

NAME

zgesdd - compute the singular value decomposition (SVD) of a complex M-by-N matrix A, optionally computing the left and/or right singular vectors, by using divide-and-conquer method

SYNOPSIS

  SUBROUTINE ZGESDD( JOBZ, M, N, A, LDA, S, U, LDU, VT, LDVT, WORK, 
 *      LWORK, RWORK, IWORK, INFO)
  CHARACTER * 1 JOBZ
  DOUBLE COMPLEX A(LDA,*), U(LDU,*), VT(LDVT,*), WORK(*)
  INTEGER M, N, LDA, LDU, LDVT, LWORK, INFO
  INTEGER IWORK(*)
  DOUBLE PRECISION S(*), RWORK(*)
 
  SUBROUTINE ZGESDD_64( JOBZ, M, N, A, LDA, S, U, LDU, VT, LDVT, WORK, 
 *      LWORK, RWORK, IWORK, INFO)
  CHARACTER * 1 JOBZ
  DOUBLE COMPLEX A(LDA,*), U(LDU,*), VT(LDVT,*), WORK(*)
  INTEGER*8 M, N, LDA, LDU, LDVT, LWORK, INFO
  INTEGER*8 IWORK(*)
  DOUBLE PRECISION S(*), RWORK(*)

F95 INTERFACE

  SUBROUTINE GESDD( JOBZ, [M], [N], A, [LDA], S, U, [LDU], VT, [LDVT], 
 *       [WORK], [LWORK], [RWORK], [IWORK], [INFO])
  CHARACTER(LEN=1) :: JOBZ
  COMPLEX(8), DIMENSION(:) :: WORK
  COMPLEX(8), DIMENSION(:,:) :: A, U, VT
  INTEGER :: M, N, LDA, LDU, LDVT, LWORK, INFO
  INTEGER, DIMENSION(:) :: IWORK
  REAL(8), DIMENSION(:) :: S, RWORK
 
  SUBROUTINE GESDD_64( JOBZ, [M], [N], A, [LDA], S, U, [LDU], VT, 
 *       [LDVT], [WORK], [LWORK], [RWORK], [IWORK], [INFO])
  CHARACTER(LEN=1) :: JOBZ
  COMPLEX(8), DIMENSION(:) :: WORK
  COMPLEX(8), DIMENSION(:,:) :: A, U, VT
  INTEGER(8) :: M, N, LDA, LDU, LDVT, LWORK, INFO
  INTEGER(8), DIMENSION(:) :: IWORK
  REAL(8), DIMENSION(:) :: S, RWORK

C INTERFACE

#include <sunperf.h>

void zgesdd(char jobz, int m, int n, doublecomplex *a, int lda, double *s, doublecomplex *u, int ldu, doublecomplex *vt, int ldvt, int *info);

void zgesdd_64(char jobz, long m, long n, doublecomplex *a, long lda, double *s, doublecomplex *u, long ldu, doublecomplex *vt, long ldvt, long *info);

PURPOSE

zgesdd computes the singular value decomposition (SVD) of a complex M-by-N matrix A, optionally computing the left and/or right singular vectors, by using divide-and-conquer method. The SVD is written = U * SIGMA * conjugate-transpose(V)

where SIGMA is an M-by-N matrix which is zero except for its min(m,n) diagonal elements, U is an M-by-M unitary matrix, and V is an N-by-N unitary matrix. The diagonal elements of SIGMA are the singular values of A; they are real and non-negative, and are returned in descending order. The first min(m,n) columns of U and V are the left and right singular vectors of A.

Note that the routine returns VT = V**H, not V.

The divide and conquer algorithm makes very mild assumptions about floating point arithmetic. It will work on machines with a guard digit in add/subtract, or on those binary machines without guard digits which subtract like the Cray X-MP, Cray Y-MP, Cray C-90, or Cray-2. It could conceivably fail on hexadecimal or decimal machines without guard digits, but we know of none.

ARGUMENTS

* JOBZ (input): Specifies options for computing all or part of the matrix U:
* M (input): The number of rows of the input matrix A. M >= 0.
* N (input): The number of columns of the input matrix A. N >= 0.
* A (input/output): On entry, the M-by-N matrix A. On exit, if JOBZ = 'O', A is overwritten with the first N columns of U (the left singular vectors, stored columnwise) if M >= N; A is overwritten with the first M rows of V**H (the right singular vectors, stored rowwise) otherwise. if JOBZ .ne. 'O', the contents of A are destroyed.
* LDA (input): The leading dimension of the array A. LDA >= max(1,M).
* S (output): The singular values of A, sorted so that S(i) >= S(i+1).
* U (output): UCOL = M if JOBZ = 'A' or JOBZ = 'O' and M < N; UCOL = min(M,N) if JOBZ = 'S'. If JOBZ = 'A' or JOBZ = 'O' and M < N, U contains the M-by-M unitary matrix U; if JOBZ = 'S', U contains the first min(M,N) columns of U (the left singular vectors, stored columnwise); if JOBZ = 'O' and M >= N, or JOBZ = 'N', U is not referenced.
* LDU (input): The leading dimension of the array U. LDU >= 1; if JOBZ = 'S' or 'A' or JOBZ = 'O' and M < N, LDU >= M.
* VT (output): If JOBZ = 'A' or JOBZ = 'O' and M >= N, VT contains the N-by-N unitary matrix V**H; if JOBZ = 'S', VT contains the first min(M,N) rows of V**H (the right singular vectors, stored rowwise); if JOBZ = 'O' and M < N, or JOBZ = 'N', VT is not referenced.
* LDVT (input): The leading dimension of the array VT. LDVT >= 1; if JOBZ = 'A' or JOBZ = 'O' and M >= N, LDVT >= N; if JOBZ = 'S', LDVT >= min(M,N).
* WORK (workspace): On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
* LWORK (input): The dimension of the array WORK. LWORK >= 1. if JOBZ = 'N', LWORK >= 2*min(M,N)+max(M,N). if JOBZ = 'O', LWORK >= 2*min(M,N)*min(M,N)+2*min(M,N)+max(M,N). if JOBZ = 'S' or 'A', LWORK >= min(M,N)*min(M,N)+2*min(M,N)+max(M,N). For good performance, LWORK should generally be larger. If LWORK < 0 but other input arguments are legal, WORK(1) returns optimal LWORK.
* RWORK (workspace): If JOBZ = 'N', LRWORK >= 7*min(M,N). Otherwise, LRWORK >= 5*min(M,N)*min(M,N) + 5*min(M,N)
* IWORK (workspace): dimension(8*MIN(M,N))
* INFO (output)