NAME

SYNOPSIS

  SUBROUTINE ZGELSD( M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, 
 *      LWORK, RWORK, IWORK, INFO)
  DOUBLE COMPLEX A(LDA,*), B(LDB,*), WORK(*)
  INTEGER M, N, NRHS, LDA, LDB, RANK, LWORK, INFO
  INTEGER IWORK(*)
  DOUBLE PRECISION RCOND
  DOUBLE PRECISION S(*), RWORK(*)
 
  SUBROUTINE ZGELSD_64( M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, 
 *      WORK, LWORK, RWORK, IWORK, INFO)
  DOUBLE COMPLEX A(LDA,*), B(LDB,*), WORK(*)
  INTEGER*8 M, N, NRHS, LDA, LDB, RANK, LWORK, INFO
  INTEGER*8 IWORK(*)
  DOUBLE PRECISION RCOND
  DOUBLE PRECISION S(*), RWORK(*)
 

F95 INTERFACE

  SUBROUTINE GELSD( [M], [N], [NRHS], A, [LDA], B, [LDB], S, RCOND, 
 *       RANK, [WORK], [LWORK], [RWORK], [IWORK], [INFO])
  COMPLEX(8), DIMENSION(:) :: WORK
  COMPLEX(8), DIMENSION(:,:) :: A, B
  INTEGER :: M, N, NRHS, LDA, LDB, RANK, LWORK, INFO
  INTEGER, DIMENSION(:) :: IWORK
  REAL(8) :: RCOND
  REAL(8), DIMENSION(:) :: S, RWORK
 
  SUBROUTINE GELSD_64( [M], [N], [NRHS], A, [LDA], B, [LDB], S, RCOND, 
 *       RANK, [WORK], [LWORK], [RWORK], [IWORK], [INFO])
  COMPLEX(8), DIMENSION(:) :: WORK
  COMPLEX(8), DIMENSION(:,:) :: A, B
  INTEGER(8) :: M, N, NRHS, LDA, LDB, RANK, LWORK, INFO
  INTEGER(8), DIMENSION(:) :: IWORK
  REAL(8) :: RCOND
  REAL(8), DIMENSION(:) :: S, RWORK
 

C INTERFACE

void zgelsd(int m, int n, int nrhs, doublecomplex *a, int lda, doublecomplex *b, int ldb, double *s, double rcond, int *rank, int *info);

void zgelsd_64(long m, long n, long nrhs, doublecomplex *a, long lda, doublecomplex *b, long ldb, double *s, double rcond, long *rank, long *info);

PURPOSE

using the singular value decomposition (SVD) of A. A is an M-by-N matrix which may be rank-deficient.

Several right hand side vectors b and solution vectors x can be handled in a single call; they are stored as the columns of the M-by-NRHS right hand side matrix B and the N-by-NRHS solution matrix X.

The problem is solved in three steps:

(1) Reduce the coefficient matrix A to bidiagonal form with Householder tranformations, reducing the original problem into a ``bidiagonal least squares problem'' (BLS)

(2) Solve the BLS using a divide and conquer approach.

(3) Apply back all the Householder tranformations to solve the original least squares problem.

The effective rank of A is determined by treating as zero those singular values which are less than RCOND times the largest singular value.

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

* M (input)

The number of rows of the matrix A. M >= 0.

* N (input)

The number of columns of the matrix A. N >= 0.

* NRHS (input)

The number of right hand sides, i.e., the number of columns of the matrices B and X. NRHS >= 0.

* A (input/output)

On entry, the M-by-N matrix A. On exit, A has been destroyed.

* LDA (input)

The leading dimension of the array A. LDA >= max(1,M).

* B (input/output)

On entry, the M-by-NRHS right hand side matrix B. On exit, B is overwritten by the N-by-NRHS solution matrix X. If m >= n and RANK = n, the residual sum-of-squares for the solution in the i-th column is given by the sum of squares of elements n+1:m in that column.

* LDB (input)

The leading dimension of the array B. LDB >= max(1,M,N).

* S (output)

The singular values of A in decreasing order. The condition number of A in the 2-norm = S(1)/S(min(m,n)).

* RCOND (input)

RCOND is used to determine the effective rank of A. Singular values S(i) <= RCOND*S(1) are treated as zero. If RCOND < 0, machine precision is used instead.

* RANK (output)

The effective rank of A, i.e., the number of singular values which are greater than RCOND*S(1).

* WORK (workspace)

On exit, if INFO = 0, WORK(1) returns the optimal LWORK.

* LWORK (input)

The dimension of the array WORK. LWORK >= 1. The exact minimum amount of workspace needed depends on M, N and NRHS. If M >= N, LWORK >= 2*N + N*NRHS. If M < N, LWORK >= 2*M + M*NRHS. 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.

* RWORK (workspace)

If M >= N, LRWORK >= 8*N + 2*N*SMLSIZ + 8*N*NLVL + N*NRHS. If M < N, LRWORK >= 8*M + 2*M*SMLSIZ + 8*M*NLVL + M*NRHS. SMLSIZ is returned by ILAENV and is equal to the maximum size of the subproblems at the bottom of the computation tree (usually about 25), and NLVL = INT( LOG_2( MIN( M,N )/(SMLSIZ+1) ) ) + 1

* IWORK (workspace)

LIWORK >= 3 * MINMN * NLVL + 11 * MINMN, where MINMN = MIN( M,N ).

* INFO (output)