dporfs - improve the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definite,
SUBROUTINE DPORFS( UPLO, N, NRHS, A, LDA, AF, LDAF, B, LDB, X, LDX, * FERR, BERR, WORK, WORK2, INFO) CHARACTER * 1 UPLO INTEGER N, NRHS, LDA, LDAF, LDB, LDX, INFO INTEGER WORK2(*) DOUBLE PRECISION A(LDA,*), AF(LDAF,*), B(LDB,*), X(LDX,*), FERR(*), BERR(*), WORK(*)
SUBROUTINE DPORFS_64( UPLO, N, NRHS, A, LDA, AF, LDAF, B, LDB, X, * LDX, FERR, BERR, WORK, WORK2, INFO) CHARACTER * 1 UPLO INTEGER*8 N, NRHS, LDA, LDAF, LDB, LDX, INFO INTEGER*8 WORK2(*) DOUBLE PRECISION A(LDA,*), AF(LDAF,*), B(LDB,*), X(LDX,*), FERR(*), BERR(*), WORK(*)
SUBROUTINE PORFS( UPLO, [N], [NRHS], A, [LDA], AF, [LDAF], B, [LDB], * X, [LDX], FERR, BERR, [WORK], [WORK2], [INFO]) CHARACTER(LEN=1) :: UPLO INTEGER :: N, NRHS, LDA, LDAF, LDB, LDX, INFO INTEGER, DIMENSION(:) :: WORK2 REAL(8), DIMENSION(:) :: FERR, BERR, WORK REAL(8), DIMENSION(:,:) :: A, AF, B, X
SUBROUTINE PORFS_64( UPLO, [N], [NRHS], A, [LDA], AF, [LDAF], B, * [LDB], X, [LDX], FERR, BERR, [WORK], [WORK2], [INFO]) CHARACTER(LEN=1) :: UPLO INTEGER(8) :: N, NRHS, LDA, LDAF, LDB, LDX, INFO INTEGER(8), DIMENSION(:) :: WORK2 REAL(8), DIMENSION(:) :: FERR, BERR, WORK REAL(8), DIMENSION(:,:) :: A, AF, B, X
#include <sunperf.h>
void dporfs(char uplo, int n, int nrhs, double *a, int lda, double *af, int ldaf, double *b, int ldb, double *x, int ldx, double *ferr, double *berr, int *info);
void dporfs_64(char uplo, long n, long nrhs, double *a, long lda, double *af, long ldaf, double *b, long ldb, double *x, long ldx, double *ferr, double *berr, long *info);
dporfs improves the computed solution to a system of linear equations when the coefficient matrix is symmetric positive definite, and provides error bounds and backward error estimates for the solution.
= 'U': Upper triangle of A is stored;
= 'L': Lower triangle of A is stored.
X(j) (the j-th column of the solution matrix X).
If XTRUE is the true solution corresponding to X(j), FERR(j)
is an estimated upper bound for the magnitude of the largest
element in (X(j) - XTRUE) divided by the magnitude of the
largest element in X(j). The estimate is as reliable as
the estimate for RCOND, and is almost always a slight
overestimate of the true error.
X(j) (i.e., the smallest relative change in
any element of A or B that makes X(j) an exact solution).
dimension(3*N)
dimension(N)
= 0: successful exit
< 0: if INFO = -i, the i-th argument had an illegal value