• NAME
• SYNOPSIS
• F95 INTERFACE
• C INTERFACE
• PURPOSE
• ARGUMENTS
• FURTHER DETAILS

NAME

sgelsd - compute the minimum-norm solution to a real linear least squares problem

SYNOPSIS

  SUBROUTINE SGELSD( M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, WORK, 
 *      LWORK, IWORK, INFO)
  INTEGER M, N, NRHS, LDA, LDB, RANK, LWORK, INFO
  INTEGER IWORK(*)
  REAL RCOND
  REAL A(LDA,*), B(LDB,*), S(*), WORK(*)

  SUBROUTINE SGELSD_64( M, N, NRHS, A, LDA, B, LDB, S, RCOND, RANK, 
 *      WORK, LWORK, IWORK, INFO)
  INTEGER*8 M, N, NRHS, LDA, LDB, RANK, LWORK, INFO
  INTEGER*8 IWORK(*)
  REAL RCOND
  REAL A(LDA,*), B(LDB,*), S(*), WORK(*)

F95 INTERFACE

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

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

C INTERFACE

#include <sunperf.h>

void sgelsd(int m, int n, int nrhs, float *a, int lda, float *b, int ldb, float *s, float rcond, int *rank, int *info);

void sgelsd_64(long m, long n, long nrhs, float *a, long lda, float *b, long ldb, float *s, float rcond, long *rank, long *info);

PURPOSE

sgelsd computes the minimum-norm solution to a real linear least squares problem: minimize 2-norm(| b - A*x |)

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 transformations, 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 A. M > = 0.

• N (input)
  The number of columns of 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,max(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. As long as LWORK is at least 12*N + 2*N*SMLSIZ + 8*N*NLVL + N*NRHS * (SMLSIZ+1)**2, if M is greater than or equal to N or 12*M + 2*M*SMLSIZ + 8*M*NLVL + M*NRHS + (SMLSIZ+1)**2, if M is less than N, the code will execute correctly. 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 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.

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

• INFO (output)
  

   = 0:  successful exit

   < 0:  if INFO  = -i, the i-th argument had an illegal value.

   > 0:  the algorithm for computing the SVD failed to converge;
  if INFO  = i, i off-diagonal elements of an intermediate
  bidiagonal form did not converge to zero.

FURTHER DETAILS

Based on contributions by

   Ming Gu and Ren-Cang Li, Computer Science Division, University of California at Berkeley, USA

   Osni Marques, LBNL/NERSC, USA