NAME

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


SYNOPSIS

  SUBROUTINE SGELSY( M, N, NRHS, A, LDA, B, LDB, JPVT, RCOND, RANK, 
 *      WORK, LWORK, INFO)
  INTEGER M, N, NRHS, LDA, LDB, RANK, LWORK, INFO
  INTEGER JPVT(*)
  REAL RCOND
  REAL A(LDA,*), B(LDB,*), WORK(*)
  SUBROUTINE SGELSY_64( M, N, NRHS, A, LDA, B, LDB, JPVT, RCOND, RANK, 
 *      WORK, LWORK, INFO)
  INTEGER*8 M, N, NRHS, LDA, LDB, RANK, LWORK, INFO
  INTEGER*8 JPVT(*)
  REAL RCOND
  REAL A(LDA,*), B(LDB,*), WORK(*)

F95 INTERFACE

  SUBROUTINE GELSY( [M], [N], [NRHS], A, [LDA], B, [LDB], JPVT, RCOND, 
 *       RANK, [WORK], [LWORK], [INFO])
  INTEGER :: M, N, NRHS, LDA, LDB, RANK, LWORK, INFO
  INTEGER, DIMENSION(:) :: JPVT
  REAL :: RCOND
  REAL, DIMENSION(:) :: WORK
  REAL, DIMENSION(:,:) :: A, B
  SUBROUTINE GELSY_64( [M], [N], [NRHS], A, [LDA], B, [LDB], JPVT, 
 *       RCOND, RANK, [WORK], [LWORK], [INFO])
  INTEGER(8) :: M, N, NRHS, LDA, LDB, RANK, LWORK, INFO
  INTEGER(8), DIMENSION(:) :: JPVT
  REAL :: RCOND
  REAL, DIMENSION(:) :: WORK
  REAL, DIMENSION(:,:) :: A, B

C INTERFACE

#include <sunperf.h>

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

void sgelsy_64(long m, long n, long nrhs, float *a, long lda, float *b, long ldb, long *jpvt, float rcond, long *rank, long *info);


PURPOSE

sgelsy computes the minimum-norm solution to a real linear least squares problem: minimize || A * X - B ||

using a complete orthogonal factorization 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 routine first computes a QR factorization with column pivoting: A * P = Q * [ R11 R12 ]

                [  0  R22 ]

with R11 defined as the largest leading submatrix whose estimated condition number is less than 1/RCOND. The order of R11, RANK, is the effective rank of A.

Then, R22 is considered to be negligible, and R12 is annihilated by orthogonal transformations from the right, arriving at the complete orthogonal factorization:

   A * P = Q * [ T11 0 ] * Z
               [  0  0 ]

The minimum-norm solution is then

   X = P * Z' [ inv(T11)*Q1'*B ]
              [        0       ]

where Q1 consists of the first RANK columns of Q.

This routine is basically identical to the original xGELSX except three differences:

  o The call to the subroutine xGEQPF has been substituted by the
    the call to the subroutine xGEQP3. This subroutine is a Blas-3
    version of the QR factorization with column pivoting.
  o Matrix B (the right hand side) is updated with Blas-3.
  o The permutation of matrix B (the right hand side) is faster and
    more simple.


ARGUMENTS


FURTHER DETAILS

Based on contributions by

  A. Petitet, Computer Science Dept., Univ. of Tenn., Knoxville, USA
  E. Quintana-Orti, Depto. de Informatica, Universidad Jaime I, Spain
  G. Quintana-Orti, Depto. de Informatica, Universidad Jaime I, Spain