LAPACK 3.12.1
LAPACK: Linear Algebra PACKage
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◆ sla_gerfsx_extended()

subroutine sla_gerfsx_extended ( integer prec_type,
integer trans_type,
integer n,
integer nrhs,
real, dimension( lda, * ) a,
integer lda,
real, dimension( ldaf, * ) af,
integer ldaf,
integer, dimension( * ) ipiv,
logical colequ,
real, dimension( * ) c,
real, dimension( ldb, * ) b,
integer ldb,
real, dimension( ldy, * ) y,
integer ldy,
real, dimension( * ) berr_out,
integer n_norms,
real, dimension( nrhs, * ) errs_n,
real, dimension( nrhs, * ) errs_c,
real, dimension( * ) res,
real, dimension( * ) ayb,
real, dimension( * ) dy,
real, dimension( * ) y_tail,
real rcond,
integer ithresh,
real rthresh,
real dz_ub,
logical ignore_cwise,
integer info )

SLA_GERFSX_EXTENDED improves the computed solution to a system of linear equations for general matrices by performing extra-precise iterative refinement and provides error bounds and backward error estimates for the solution.

Download SLA_GERFSX_EXTENDED + dependencies [TGZ] [ZIP] [TXT]

Purpose:
!>
!> SLA_GERFSX_EXTENDED improves the computed solution to a system of
!> linear equations by performing extra-precise iterative refinement
!> and provides error bounds and backward error estimates for the solution.
!> This subroutine is called by SGERFSX to perform iterative refinement.
!> In addition to normwise error bound, the code provides maximum
!> componentwise error bound if possible. See comments for ERRS_N
!> and ERRS_C for details of the error bounds. Note that this
!> subroutine is only responsible for setting the second fields of
!> ERRS_N and ERRS_C.
!> 
Parameters
[in]PREC_TYPE
!>          PREC_TYPE is INTEGER
!>     Specifies the intermediate precision to be used in refinement.
!>     The value is defined by ILAPREC(P) where P is a CHARACTER and P
!>          = 'S':  Single
!>          = 'D':  Double
!>          = 'I':  Indigenous
!>          = 'X' or 'E':  Extra
!> 
[in]TRANS_TYPE
!>          TRANS_TYPE is INTEGER
!>     Specifies the transposition operation on A.
!>     The value is defined by ILATRANS(T) where T is a CHARACTER and T
!>          = 'N':  No transpose
!>          = 'T':  Transpose
!>          = 'C':  Conjugate transpose
!> 
[in]N
!>          N is INTEGER
!>     The number of linear equations, i.e., the order of the
!>     matrix A.  N >= 0.
!> 
[in]NRHS
!>          NRHS is INTEGER
!>     The number of right-hand-sides, i.e., the number of columns of the
!>     matrix B.
!> 
[in]A
!>          A is REAL array, dimension (LDA,N)
!>     On entry, the N-by-N matrix A.
!> 
[in]LDA
!>          LDA is INTEGER
!>     The leading dimension of the array A.  LDA >= max(1,N).
!> 
[in]AF
!>          AF is REAL array, dimension (LDAF,N)
!>     The factors L and U from the factorization
!>     A = P*L*U as computed by SGETRF.
!> 
[in]LDAF
!>          LDAF is INTEGER
!>     The leading dimension of the array AF.  LDAF >= max(1,N).
!> 
[in]IPIV
!>          IPIV is INTEGER array, dimension (N)
!>     The pivot indices from the factorization A = P*L*U
!>     as computed by SGETRF; row i of the matrix was interchanged
!>     with row IPIV(i).
!> 
[in]COLEQU
!>          COLEQU is LOGICAL
!>     If .TRUE. then column equilibration was done to A before calling
!>     this routine. This is needed to compute the solution and error
!>     bounds correctly.
!> 
[in]C
!>          C is REAL array, dimension (N)
!>     The column scale factors for A. If COLEQU = .FALSE., C
!>     is not accessed. If C is input, each element of C should be a power
!>     of the radix to ensure a reliable solution and error estimates.
!>     Scaling by powers of the radix does not cause rounding errors unless
!>     the result underflows or overflows. Rounding errors during scaling
!>     lead to refining with a matrix that is not equivalent to the
!>     input matrix, producing error estimates that may not be
!>     reliable.
!> 
[in]B
!>          B is REAL array, dimension (LDB,NRHS)
!>     The right-hand-side matrix B.
!> 
[in]LDB
!>          LDB is INTEGER
!>     The leading dimension of the array B.  LDB >= max(1,N).
!> 
[in,out]Y
!>          Y is REAL array, dimension (LDY,NRHS)
!>     On entry, the solution matrix X, as computed by SGETRS.
!>     On exit, the improved solution matrix Y.
!> 
[in]LDY
!>          LDY is INTEGER
!>     The leading dimension of the array Y.  LDY >= max(1,N).
!> 
[out]BERR_OUT
!>          BERR_OUT is REAL array, dimension (NRHS)
!>     On exit, BERR_OUT(j) contains the componentwise relative backward
!>     error for right-hand-side j from the formula
!>         max(i) ( abs(RES(i)) / ( abs(op(A_s))*abs(Y) + abs(B_s) )(i) )
!>     where abs(Z) is the componentwise absolute value of the matrix
!>     or vector Z. This is computed by SLA_LIN_BERR.
!> 
[in]N_NORMS
!>          N_NORMS is INTEGER
!>     Determines which error bounds to return (see ERRS_N
!>     and ERRS_C).
!>     If N_NORMS >= 1 return normwise error bounds.
!>     If N_NORMS >= 2 return componentwise error bounds.
!> 
[in,out]ERRS_N
!>          ERRS_N is REAL array, dimension (NRHS, N_ERR_BNDS)
!>     For each right-hand side, this array contains information about
!>     various error bounds and condition numbers corresponding to the
!>     normwise relative error, which is defined as follows:
!>
!>     Normwise relative error in the ith solution vector:
!>             max_j (abs(XTRUE(j,i) - X(j,i)))
!>            ------------------------------
!>                  max_j abs(X(j,i))
!>
!>     The array is indexed by the type of error information as described
!>     below. There currently are up to three pieces of information
!>     returned.
!>
!>     The first index in ERRS_N(i,:) corresponds to the ith
!>     right-hand side.
!>
!>     The second index in ERRS_N(:,err) contains the following
!>     three fields:
!>     err = 1  boolean. Trust the answer if the
!>              reciprocal condition number is less than the threshold
!>              sqrt(n) * slamch('Epsilon').
!>
!>     err = 2  error bound: The estimated forward error,
!>              almost certainly within a factor of 10 of the true error
!>              so long as the next entry is greater than the threshold
!>              sqrt(n) * slamch('Epsilon'). This error bound should only
!>              be trusted if the previous boolean is true.
!>
!>     err = 3  Reciprocal condition number: Estimated normwise
!>              reciprocal condition number.  Compared with the threshold
!>              sqrt(n) * slamch('Epsilon') to determine if the error
!>              estimate is . These reciprocal condition
!>              numbers are 1 / (norm(Z^{-1},inf) * norm(Z,inf)) for some
!>              appropriately scaled matrix Z.
!>              Let Z = S*A, where S scales each row by a power of the
!>              radix so all absolute row sums of Z are approximately 1.
!>
!>     This subroutine is only responsible for setting the second field
!>     above.
!>     See Lapack Working Note 165 for further details and extra
!>     cautions.
!> 
[in,out]ERRS_C
!>          ERRS_C is REAL array, dimension (NRHS, N_ERR_BNDS)
!>     For each right-hand side, this array contains information about
!>     various error bounds and condition numbers corresponding to the
!>     componentwise relative error, which is defined as follows:
!>
!>     Componentwise relative error in the ith solution vector:
!>                    abs(XTRUE(j,i) - X(j,i))
!>             max_j ----------------------
!>                         abs(X(j,i))
!>
!>     The array is indexed by the right-hand side i (on which the
!>     componentwise relative error depends), and the type of error
!>     information as described below. There currently are up to three
!>     pieces of information returned for each right-hand side. If
!>     componentwise accuracy is not requested (PARAMS(3) = 0.0), then
!>     ERRS_C is not accessed.  If N_ERR_BNDS < 3, then at most
!>     the first (:,N_ERR_BNDS) entries are returned.
!>
!>     The first index in ERRS_C(i,:) corresponds to the ith
!>     right-hand side.
!>
!>     The second index in ERRS_C(:,err) contains the following
!>     three fields:
!>     err = 1  boolean. Trust the answer if the
!>              reciprocal condition number is less than the threshold
!>              sqrt(n) * slamch('Epsilon').
!>
!>     err = 2  error bound: The estimated forward error,
!>              almost certainly within a factor of 10 of the true error
!>              so long as the next entry is greater than the threshold
!>              sqrt(n) * slamch('Epsilon'). This error bound should only
!>              be trusted if the previous boolean is true.
!>
!>     err = 3  Reciprocal condition number: Estimated componentwise
!>              reciprocal condition number.  Compared with the threshold
!>              sqrt(n) * slamch('Epsilon') to determine if the error
!>              estimate is . These reciprocal condition
!>              numbers are 1 / (norm(Z^{-1},inf) * norm(Z,inf)) for some
!>              appropriately scaled matrix Z.
!>              Let Z = S*(A*diag(x)), where x is the solution for the
!>              current right-hand side and S scales each row of
!>              A*diag(x) by a power of the radix so all absolute row
!>              sums of Z are approximately 1.
!>
!>     This subroutine is only responsible for setting the second field
!>     above.
!>     See Lapack Working Note 165 for further details and extra
!>     cautions.
!> 
[in]RES
!>          RES is REAL array, dimension (N)
!>     Workspace to hold the intermediate residual.
!> 
[in]AYB
!>          AYB is REAL array, dimension (N)
!>     Workspace. This can be the same workspace passed for Y_TAIL.
!> 
[in]DY
!>          DY is REAL array, dimension (N)
!>     Workspace to hold the intermediate solution.
!> 
[in]Y_TAIL
!>          Y_TAIL is REAL array, dimension (N)
!>     Workspace to hold the trailing bits of the intermediate solution.
!> 
[in]RCOND
!>          RCOND is REAL
!>     Reciprocal scaled condition number.  This is an estimate of the
!>     reciprocal Skeel condition number of the matrix A after
!>     equilibration (if done).  If this is less than the machine
!>     precision (in particular, if it is zero), the matrix is singular
!>     to working precision.  Note that the error may still be small even
!>     if this number is very small and the matrix appears ill-
!>     conditioned.
!> 
[in]ITHRESH
!>          ITHRESH is INTEGER
!>     The maximum number of residual computations allowed for
!>     refinement. The default is 10. For 'aggressive' set to 100 to
!>     permit convergence using approximate factorizations or
!>     factorizations other than LU. If the factorization uses a
!>     technique other than Gaussian elimination, the guarantees in
!>     ERRS_N and ERRS_C may no longer be trustworthy.
!> 
[in]RTHRESH
!>          RTHRESH is REAL
!>     Determines when to stop refinement if the error estimate stops
!>     decreasing. Refinement will stop when the next solution no longer
!>     satisfies norm(dx_{i+1}) < RTHRESH * norm(dx_i) where norm(Z) is
!>     the infinity norm of Z. RTHRESH satisfies 0 < RTHRESH <= 1. The
!>     default value is 0.5. For 'aggressive' set to 0.9 to permit
!>     convergence on extremely ill-conditioned matrices. See LAWN 165
!>     for more details.
!> 
[in]DZ_UB
!>          DZ_UB is REAL
!>     Determines when to start considering componentwise convergence.
!>     Componentwise convergence is only considered after each component
!>     of the solution Y is stable, which we define as the relative
!>     change in each component being less than DZ_UB. The default value
!>     is 0.25, requiring the first bit to be stable. See LAWN 165 for
!>     more details.
!> 
[in]IGNORE_CWISE
!>          IGNORE_CWISE is LOGICAL
!>     If .TRUE. then ignore componentwise convergence. Default value
!>     is .FALSE..
!> 
[out]INFO
!>          INFO is INTEGER
!>       = 0:  Successful exit.
!>       < 0:  if INFO = -i, the ith argument to SGETRS had an illegal
!>             value
!> 
Author
Univ. of Tennessee
Univ. of California Berkeley
Univ. of Colorado Denver
NAG Ltd.

Definition at line 389 of file sla_gerfsx_extended.f.

397*
398* -- LAPACK computational routine --
399* -- LAPACK is a software package provided by Univ. of Tennessee, --
400* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
401*
402* .. Scalar Arguments ..
403 INTEGER INFO, LDA, LDAF, LDB, LDY, N, NRHS, PREC_TYPE,
404 $ TRANS_TYPE, N_NORMS, ITHRESH
405 LOGICAL COLEQU, IGNORE_CWISE
406 REAL RTHRESH, DZ_UB
407* ..
408* .. Array Arguments ..
409 INTEGER IPIV( * )
410 REAL A( LDA, * ), AF( LDAF, * ), B( LDB, * ),
411 $ Y( LDY, * ), RES( * ), DY( * ), Y_TAIL( * )
412 REAL C( * ), AYB( * ), RCOND, BERR_OUT( * ),
413 $ ERRS_N( NRHS, * ),
414 $ ERRS_C( NRHS, * )
415* ..
416*
417* =====================================================================
418*
419* .. Local Scalars ..
420 CHARACTER TRANS
421 INTEGER CNT, I, J, X_STATE, Z_STATE, Y_PREC_STATE
422 REAL YK, DYK, YMIN, NORMY, NORMX, NORMDX, DXRAT,
423 $ DZRAT, PREVNORMDX, PREV_DZ_Z, DXRATMAX,
424 $ DZRATMAX, DX_X, DZ_Z, FINAL_DX_X, FINAL_DZ_Z,
425 $ EPS, HUGEVAL, INCR_THRESH
426 LOGICAL INCR_PREC
427* ..
428* .. Parameters ..
429 INTEGER UNSTABLE_STATE, WORKING_STATE, CONV_STATE,
430 $ NOPROG_STATE, BASE_RESIDUAL, EXTRA_RESIDUAL,
431 $ EXTRA_Y
432 parameter( unstable_state = 0, working_state = 1,
433 $ conv_state = 2, noprog_state = 3 )
434 parameter( base_residual = 0, extra_residual = 1,
435 $ extra_y = 2 )
436 INTEGER FINAL_NRM_ERR_I, FINAL_CMP_ERR_I, BERR_I
437 INTEGER RCOND_I, NRM_RCOND_I, NRM_ERR_I, CMP_RCOND_I
438 INTEGER CMP_ERR_I, PIV_GROWTH_I
439 parameter( final_nrm_err_i = 1, final_cmp_err_i = 2,
440 $ berr_i = 3 )
441 parameter( rcond_i = 4, nrm_rcond_i = 5, nrm_err_i = 6 )
442 parameter( cmp_rcond_i = 7, cmp_err_i = 8,
443 $ piv_growth_i = 9 )
444 INTEGER LA_LINRX_ITREF_I, LA_LINRX_ITHRESH_I,
445 $ LA_LINRX_CWISE_I
446 parameter( la_linrx_itref_i = 1,
447 $ la_linrx_ithresh_i = 2 )
448 parameter( la_linrx_cwise_i = 3 )
449 INTEGER LA_LINRX_TRUST_I, LA_LINRX_ERR_I,
450 $ LA_LINRX_RCOND_I
451 parameter( la_linrx_trust_i = 1, la_linrx_err_i = 2 )
452 parameter( la_linrx_rcond_i = 3 )
453* ..
454* .. External Subroutines ..
455 EXTERNAL saxpy, scopy, sgetrs, sgemv,
456 $ blas_sgemv_x,
457 $ blas_sgemv2_x, sla_geamv, sla_wwaddw, slamch,
459 REAL SLAMCH
460 CHARACTER CHLA_TRANSTYPE
461* ..
462* .. Intrinsic Functions ..
463 INTRINSIC abs, max, min
464* ..
465* .. Executable Statements ..
466*
467 IF ( info.NE.0 ) RETURN
468 trans = chla_transtype(trans_type)
469 eps = slamch( 'Epsilon' )
470 hugeval = slamch( 'Overflow' )
471* Force HUGEVAL to Inf
472 hugeval = hugeval * hugeval
473* Using HUGEVAL may lead to spurious underflows.
474 incr_thresh = real( n ) * eps
475*
476 DO j = 1, nrhs
477 y_prec_state = extra_residual
478 IF ( y_prec_state .EQ. extra_y ) THEN
479 DO i = 1, n
480 y_tail( i ) = 0.0
481 END DO
482 END IF
483
484 dxrat = 0.0
485 dxratmax = 0.0
486 dzrat = 0.0
487 dzratmax = 0.0
488 final_dx_x = hugeval
489 final_dz_z = hugeval
490 prevnormdx = hugeval
491 prev_dz_z = hugeval
492 dz_z = hugeval
493 dx_x = hugeval
494
495 x_state = working_state
496 z_state = unstable_state
497 incr_prec = .false.
498
499 DO cnt = 1, ithresh
500*
501* Compute residual RES = B_s - op(A_s) * Y,
502* op(A) = A, A**T, or A**H depending on TRANS (and type).
503*
504 CALL scopy( n, b( 1, j ), 1, res, 1 )
505 IF ( y_prec_state .EQ. base_residual ) THEN
506 CALL sgemv( trans, n, n, -1.0, a, lda, y( 1, j ), 1,
507 $ 1.0, res, 1 )
508 ELSE IF ( y_prec_state .EQ. extra_residual ) THEN
509 CALL blas_sgemv_x( trans_type, n, n, -1.0, a, lda,
510 $ y( 1, j ), 1, 1.0, res, 1, prec_type )
511 ELSE
512 CALL blas_sgemv2_x( trans_type, n, n, -1.0, a, lda,
513 $ y( 1, j ), y_tail, 1, 1.0, res, 1, prec_type )
514 END IF
515
516! XXX: RES is no longer needed.
517 CALL scopy( n, res, 1, dy, 1 )
518 CALL sgetrs( trans, n, 1, af, ldaf, ipiv, dy, n, info )
519*
520* Calculate relative changes DX_X, DZ_Z and ratios DXRAT, DZRAT.
521*
522 normx = 0.0
523 normy = 0.0
524 normdx = 0.0
525 dz_z = 0.0
526 ymin = hugeval
527*
528 DO i = 1, n
529 yk = abs( y( i, j ) )
530 dyk = abs( dy( i ) )
531
532 IF ( yk .NE. 0.0 ) THEN
533 dz_z = max( dz_z, dyk / yk )
534 ELSE IF ( dyk .NE. 0.0 ) THEN
535 dz_z = hugeval
536 END IF
537
538 ymin = min( ymin, yk )
539
540 normy = max( normy, yk )
541
542 IF ( colequ ) THEN
543 normx = max( normx, yk * c( i ) )
544 normdx = max( normdx, dyk * c( i ) )
545 ELSE
546 normx = normy
547 normdx = max( normdx, dyk )
548 END IF
549 END DO
550
551 IF ( normx .NE. 0.0 ) THEN
552 dx_x = normdx / normx
553 ELSE IF ( normdx .EQ. 0.0 ) THEN
554 dx_x = 0.0
555 ELSE
556 dx_x = hugeval
557 END IF
558
559 dxrat = normdx / prevnormdx
560 dzrat = dz_z / prev_dz_z
561*
562* Check termination criteria
563*
564 IF (.NOT.ignore_cwise
565 $ .AND. ymin*rcond .LT. incr_thresh*normy
566 $ .AND. y_prec_state .LT. extra_y)
567 $ incr_prec = .true.
568
569 IF ( x_state .EQ. noprog_state .AND. dxrat .LE. rthresh )
570 $ x_state = working_state
571 IF ( x_state .EQ. working_state ) THEN
572 IF ( dx_x .LE. eps ) THEN
573 x_state = conv_state
574 ELSE IF ( dxrat .GT. rthresh ) THEN
575 IF ( y_prec_state .NE. extra_y ) THEN
576 incr_prec = .true.
577 ELSE
578 x_state = noprog_state
579 END IF
580 ELSE
581 IF ( dxrat .GT. dxratmax ) dxratmax = dxrat
582 END IF
583 IF ( x_state .GT. working_state ) final_dx_x = dx_x
584 END IF
585
586 IF ( z_state .EQ. unstable_state .AND. dz_z .LE. dz_ub )
587 $ z_state = working_state
588 IF ( z_state .EQ. noprog_state .AND. dzrat .LE. rthresh )
589 $ z_state = working_state
590 IF ( z_state .EQ. working_state ) THEN
591 IF ( dz_z .LE. eps ) THEN
592 z_state = conv_state
593 ELSE IF ( dz_z .GT. dz_ub ) THEN
594 z_state = unstable_state
595 dzratmax = 0.0
596 final_dz_z = hugeval
597 ELSE IF ( dzrat .GT. rthresh ) THEN
598 IF ( y_prec_state .NE. extra_y ) THEN
599 incr_prec = .true.
600 ELSE
601 z_state = noprog_state
602 END IF
603 ELSE
604 IF ( dzrat .GT. dzratmax ) dzratmax = dzrat
605 END IF
606 IF ( z_state .GT. working_state ) final_dz_z = dz_z
607 END IF
608*
609* Exit if both normwise and componentwise stopped working,
610* but if componentwise is unstable, let it go at least two
611* iterations.
612*
613 IF ( x_state.NE.working_state ) THEN
614 IF ( ignore_cwise) GOTO 666
615 IF ( z_state.EQ.noprog_state .OR. z_state.EQ.conv_state )
616 $ GOTO 666
617 IF ( z_state.EQ.unstable_state .AND. cnt.GT.1 ) GOTO 666
618 END IF
619
620 IF ( incr_prec ) THEN
621 incr_prec = .false.
622 y_prec_state = y_prec_state + 1
623 DO i = 1, n
624 y_tail( i ) = 0.0
625 END DO
626 END IF
627
628 prevnormdx = normdx
629 prev_dz_z = dz_z
630*
631* Update solution.
632*
633 IF ( y_prec_state .LT. extra_y ) THEN
634 CALL saxpy( n, 1.0, dy, 1, y( 1, j ), 1 )
635 ELSE
636 CALL sla_wwaddw( n, y( 1, j ), y_tail, dy )
637 END IF
638
639 END DO
640* Target of "IF (Z_STOP .AND. X_STOP)". Sun's f77 won't EXIT.
641 666 CONTINUE
642*
643* Set final_* when cnt hits ithresh.
644*
645 IF ( x_state .EQ. working_state ) final_dx_x = dx_x
646 IF ( z_state .EQ. working_state ) final_dz_z = dz_z
647*
648* Compute error bounds
649*
650 IF (n_norms .GE. 1) THEN
651 errs_n( j, la_linrx_err_i ) =
652 $ final_dx_x / (1 - dxratmax)
653 END IF
654 IF ( n_norms .GE. 2 ) THEN
655 errs_c( j, la_linrx_err_i ) =
656 $ final_dz_z / (1 - dzratmax)
657 END IF
658*
659* Compute componentwise relative backward error from formula
660* max(i) ( abs(R(i)) / ( abs(op(A_s))*abs(Y) + abs(B_s) )(i) )
661* where abs(Z) is the componentwise absolute value of the matrix
662* or vector Z.
663*
664* Compute residual RES = B_s - op(A_s) * Y,
665* op(A) = A, A**T, or A**H depending on TRANS (and type).
666*
667 CALL scopy( n, b( 1, j ), 1, res, 1 )
668 CALL sgemv( trans, n, n, -1.0, a, lda, y(1,j), 1, 1.0, res,
669 $ 1 )
670
671 DO i = 1, n
672 ayb( i ) = abs( b( i, j ) )
673 END DO
674*
675* Compute abs(op(A_s))*abs(Y) + abs(B_s).
676*
677 CALL sla_geamv ( trans_type, n, n, 1.0,
678 $ a, lda, y(1, j), 1, 1.0, ayb, 1 )
679
680 CALL sla_lin_berr ( n, n, 1, res, ayb, berr_out( j ) )
681*
682* End of loop for each RHS.
683*
684 END DO
685*
686 RETURN
687*
688* End of SLA_GERFSX_EXTENDED
689*
subroutine saxpy(n, sa, sx, incx, sy, incy)
SAXPY
Definition saxpy.f:89
subroutine scopy(n, sx, incx, sy, incy)
SCOPY
Definition scopy.f:82
subroutine sgemv(trans, m, n, alpha, a, lda, x, incx, beta, y, incy)
SGEMV
Definition sgemv.f:158
subroutine sgetrs(trans, n, nrhs, a, lda, ipiv, b, ldb, info)
SGETRS
Definition sgetrs.f:119
subroutine sla_geamv(trans, m, n, alpha, a, lda, x, incx, beta, y, incy)
SLA_GEAMV computes a matrix-vector product using a general matrix to calculate error bounds.
Definition sla_geamv.f:175
subroutine sla_lin_berr(n, nz, nrhs, res, ayb, berr)
SLA_LIN_BERR computes a component-wise relative backward error.
character *1 function chla_transtype(trans)
CHLA_TRANSTYPE
subroutine sla_wwaddw(n, x, y, w)
SLA_WWADDW adds a vector into a doubled-single vector.
Definition sla_wwaddw.f:79
real function slamch(cmach)
SLAMCH
Definition slamch.f:68
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