cgelsy - compute the minimum-norm solution to a complex linear least squares problem
SUBROUTINE CGELSY( M, N, NRHS, A, LDA, B, LDB, JPVT, RCOND, RANK, * WORK, LWORK, RWORK, INFO) COMPLEX A(LDA,*), B(LDB,*), WORK(*) INTEGER M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER JPVT(*) REAL RCOND REAL RWORK(*)
SUBROUTINE CGELSY_64( M, N, NRHS, A, LDA, B, LDB, JPVT, RCOND, RANK, * WORK, LWORK, RWORK, INFO) COMPLEX A(LDA,*), B(LDB,*), WORK(*) INTEGER*8 M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER*8 JPVT(*) REAL RCOND REAL RWORK(*)
SUBROUTINE GELSY( [M], [N], [NRHS], A, [LDA], B, [LDB], JPVT, RCOND, * RANK, [WORK], [LWORK], [RWORK], [INFO]) COMPLEX, DIMENSION(:) :: WORK COMPLEX, DIMENSION(:,:) :: A, B INTEGER :: M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER, DIMENSION(:) :: JPVT REAL :: RCOND REAL, DIMENSION(:) :: RWORK
SUBROUTINE GELSY_64( [M], [N], [NRHS], A, [LDA], B, [LDB], JPVT, * RCOND, RANK, [WORK], [LWORK], [RWORK], [INFO]) COMPLEX, DIMENSION(:) :: WORK COMPLEX, DIMENSION(:,:) :: A, B INTEGER(8) :: M, N, NRHS, LDA, LDB, RANK, LWORK, INFO INTEGER(8), DIMENSION(:) :: JPVT REAL :: RCOND REAL, DIMENSION(:) :: RWORK
#include <sunperf.h>
void cgelsy(int m, int n, int nrhs, complex *a, int lda, complex *b, int ldb, int *jpvt, float rcond, int *rank, int *info);
void cgelsy_64(long m, long n, long nrhs, complex *a, long lda, complex *b, long ldb, long *jpvt, float rcond, long *rank, long *info);
cgelsy computes the minimum-norm solution to a complex 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 unitary 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 permutation of matrix B (the right hand side) is faster and
more simple.
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.
JPVT(i) .ne. 0, the i-th column of A is permuted
to the front of AP, otherwise column i is a free column.
On exit, if JPVT(i) = k, then the i-th column of A*P
was the k-th column of A.
WORK(1) returns the optimal LWORK.
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.
dimension(2*N)
= 0: successful exit
< 0: if INFO = -i, the i-th argument had an illegal value
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