LAPACK 3.12.0
LAPACK: Linear Algebra PACKage
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◆ sla_gbrfsx_extended()

subroutine sla_gbrfsx_extended ( integer  prec_type,
integer  trans_type,
integer  n,
integer  kl,
integer  ku,
integer  nrhs,
real, dimension( ldab, * )  ab,
integer  ldab,
real, dimension( ldafb, * )  afb,
integer  ldafb,
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, * )  err_bnds_norm,
real, dimension( nrhs, * )  err_bnds_comp,
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_GBRFSX_EXTENDED improves the computed solution to a system of linear equations for general banded matrices by performing extra-precise iterative refinement and provides error bounds and backward error estimates for the solution.

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

Purpose:
 SLA_GBRFSX_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 SGBRFSX to perform iterative refinement.
 In addition to normwise error bound, the code provides maximum
 componentwise error bound if possible. See comments for ERR_BNDS_NORM
 and ERR_BNDS_COMP for details of the error bounds. Note that this
 subroutine is only responsible for setting the second fields of
 ERR_BNDS_NORM and ERR_BNDS_COMP.
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]KL
          KL is INTEGER
     The number of subdiagonals within the band of A.  KL >= 0.
[in]KU
          KU is INTEGER
     The number of superdiagonals within the band of A.  KU >= 0
[in]NRHS
          NRHS is INTEGER
     The number of right-hand-sides, i.e., the number of columns of the
     matrix B.
[in]AB
          AB is REAL array, dimension (LDAB,N)
     On entry, the N-by-N matrix AB.
[in]LDAB
          LDAB is INTEGER
     The leading dimension of the array AB.  LDAB >= max(1,N).
[in]AFB
          AFB is REAL array, dimension (LDAFB,N)
     The factors L and U from the factorization
     A = P*L*U as computed by SGBTRF.
[in]LDAFB
          LDAFB is INTEGER
     The leading dimension of the array AF.  LDAFB >= max(1,N).
[in]IPIV
          IPIV is INTEGER array, dimension (N)
     The pivot indices from the factorization A = P*L*U
     as computed by SGBTRF; 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 SGBTRS.
     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 ERR_BNDS_NORM
     and ERR_BNDS_COMP).
     If N_NORMS >= 1 return normwise error bounds.
     If N_NORMS >= 2 return componentwise error bounds.
[in,out]ERR_BNDS_NORM
          ERR_BNDS_NORM 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 ERR_BNDS_NORM(i,:) corresponds to the ith
     right-hand side.

     The second index in ERR_BNDS_NORM(:,err) contains the following
     three fields:
     err = 1 "Trust/don't trust" boolean. Trust the answer if the
              reciprocal condition number is less than the threshold
              sqrt(n) * slamch('Epsilon').

     err = 2 "Guaranteed" 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 "guaranteed". 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]ERR_BNDS_COMP
          ERR_BNDS_COMP 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
     ERR_BNDS_COMP is not accessed.  If N_ERR_BNDS < 3, then at most
     the first (:,N_ERR_BNDS) entries are returned.

     The first index in ERR_BNDS_COMP(i,:) corresponds to the ith
     right-hand side.

     The second index in ERR_BNDS_COMP(:,err) contains the following
     three fields:
     err = 1 "Trust/don't trust" boolean. Trust the answer if the
              reciprocal condition number is less than the threshold
              sqrt(n) * slamch('Epsilon').

     err = 2 "Guaranteed" 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 "guaranteed". 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
     ERR_BNDS_NORM and ERR_BNDS_COMP 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 SGBTRS had an illegal
             value
Author
Univ. of Tennessee
Univ. of California Berkeley
Univ. of Colorado Denver
NAG Ltd.

Definition at line 403 of file sla_gbrfsx_extended.f.

410*
411* -- LAPACK computational routine --
412* -- LAPACK is a software package provided by Univ. of Tennessee, --
413* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
414*
415* .. Scalar Arguments ..
416 INTEGER INFO, LDAB, LDAFB, LDB, LDY, N, KL, KU, NRHS,
417 $ PREC_TYPE, TRANS_TYPE, N_NORMS, ITHRESH
418 LOGICAL COLEQU, IGNORE_CWISE
419 REAL RTHRESH, DZ_UB
420* ..
421* .. Array Arguments ..
422 INTEGER IPIV( * )
423 REAL AB( LDAB, * ), AFB( LDAFB, * ), B( LDB, * ),
424 $ Y( LDY, * ), RES(*), DY(*), Y_TAIL(*)
425 REAL C( * ), AYB(*), RCOND, BERR_OUT(*),
426 $ ERR_BNDS_NORM( NRHS, * ),
427 $ ERR_BNDS_COMP( NRHS, * )
428* ..
429*
430* =====================================================================
431*
432* .. Local Scalars ..
433 CHARACTER TRANS
434 INTEGER CNT, I, J, M, X_STATE, Z_STATE, Y_PREC_STATE
435 REAL YK, DYK, YMIN, NORMY, NORMX, NORMDX, DXRAT,
436 $ DZRAT, PREVNORMDX, PREV_DZ_Z, DXRATMAX,
437 $ DZRATMAX, DX_X, DZ_Z, FINAL_DX_X, FINAL_DZ_Z,
438 $ EPS, HUGEVAL, INCR_THRESH
439 LOGICAL INCR_PREC
440* ..
441* .. Parameters ..
442 INTEGER UNSTABLE_STATE, WORKING_STATE, CONV_STATE,
443 $ NOPROG_STATE, BASE_RESIDUAL, EXTRA_RESIDUAL,
444 $ EXTRA_Y
445 parameter( unstable_state = 0, working_state = 1,
446 $ conv_state = 2, noprog_state = 3 )
447 parameter( base_residual = 0, extra_residual = 1,
448 $ extra_y = 2 )
449 INTEGER FINAL_NRM_ERR_I, FINAL_CMP_ERR_I, BERR_I
450 INTEGER RCOND_I, NRM_RCOND_I, NRM_ERR_I, CMP_RCOND_I
451 INTEGER CMP_ERR_I, PIV_GROWTH_I
452 parameter( final_nrm_err_i = 1, final_cmp_err_i = 2,
453 $ berr_i = 3 )
454 parameter( rcond_i = 4, nrm_rcond_i = 5, nrm_err_i = 6 )
455 parameter( cmp_rcond_i = 7, cmp_err_i = 8,
456 $ piv_growth_i = 9 )
457 INTEGER LA_LINRX_ITREF_I, LA_LINRX_ITHRESH_I,
458 $ LA_LINRX_CWISE_I
459 parameter( la_linrx_itref_i = 1,
460 $ la_linrx_ithresh_i = 2 )
461 parameter( la_linrx_cwise_i = 3 )
462 INTEGER LA_LINRX_TRUST_I, LA_LINRX_ERR_I,
463 $ LA_LINRX_RCOND_I
464 parameter( la_linrx_trust_i = 1, la_linrx_err_i = 2 )
465 parameter( la_linrx_rcond_i = 3 )
466* ..
467* .. External Subroutines ..
468 EXTERNAL saxpy, scopy, sgbtrs, sgbmv, blas_sgbmv_x,
469 $ blas_sgbmv2_x, sla_gbamv, sla_wwaddw, slamch,
471 REAL SLAMCH
472 CHARACTER CHLA_TRANSTYPE
473* ..
474* .. Intrinsic Functions ..
475 INTRINSIC abs, max, min
476* ..
477* .. Executable Statements ..
478*
479 IF (info.NE.0) RETURN
480 trans = chla_transtype(trans_type)
481 eps = slamch( 'Epsilon' )
482 hugeval = slamch( 'Overflow' )
483* Force HUGEVAL to Inf
484 hugeval = hugeval * hugeval
485* Using HUGEVAL may lead to spurious underflows.
486 incr_thresh = real( n ) * eps
487 m = kl+ku+1
488
489 DO j = 1, nrhs
490 y_prec_state = extra_residual
491 IF ( y_prec_state .EQ. extra_y ) THEN
492 DO i = 1, n
493 y_tail( i ) = 0.0
494 END DO
495 END IF
496
497 dxrat = 0.0
498 dxratmax = 0.0
499 dzrat = 0.0
500 dzratmax = 0.0
501 final_dx_x = hugeval
502 final_dz_z = hugeval
503 prevnormdx = hugeval
504 prev_dz_z = hugeval
505 dz_z = hugeval
506 dx_x = hugeval
507
508 x_state = working_state
509 z_state = unstable_state
510 incr_prec = .false.
511
512 DO cnt = 1, ithresh
513*
514* Compute residual RES = B_s - op(A_s) * Y,
515* op(A) = A, A**T, or A**H depending on TRANS (and type).
516*
517 CALL scopy( n, b( 1, j ), 1, res, 1 )
518 IF ( y_prec_state .EQ. base_residual ) THEN
519 CALL sgbmv( trans, m, n, kl, ku, -1.0, ab, ldab,
520 $ y( 1, j ), 1, 1.0, res, 1 )
521 ELSE IF ( y_prec_state .EQ. extra_residual ) THEN
522 CALL blas_sgbmv_x( trans_type, n, n, kl, ku,
523 $ -1.0, ab, ldab, y( 1, j ), 1, 1.0, res, 1,
524 $ prec_type )
525 ELSE
526 CALL blas_sgbmv2_x( trans_type, n, n, kl, ku, -1.0,
527 $ ab, ldab, y( 1, j ), y_tail, 1, 1.0, res, 1,
528 $ prec_type )
529 END IF
530
531! XXX: RES is no longer needed.
532 CALL scopy( n, res, 1, dy, 1 )
533 CALL sgbtrs( trans, n, kl, ku, 1, afb, ldafb, ipiv, dy, n,
534 $ info )
535*
536* Calculate relative changes DX_X, DZ_Z and ratios DXRAT, DZRAT.
537*
538 normx = 0.0
539 normy = 0.0
540 normdx = 0.0
541 dz_z = 0.0
542 ymin = hugeval
543
544 DO i = 1, n
545 yk = abs( y( i, j ) )
546 dyk = abs( dy( i ) )
547
548 IF ( yk .NE. 0.0 ) THEN
549 dz_z = max( dz_z, dyk / yk )
550 ELSE IF ( dyk .NE. 0.0 ) THEN
551 dz_z = hugeval
552 END IF
553
554 ymin = min( ymin, yk )
555
556 normy = max( normy, yk )
557
558 IF ( colequ ) THEN
559 normx = max( normx, yk * c( i ) )
560 normdx = max( normdx, dyk * c( i ) )
561 ELSE
562 normx = normy
563 normdx = max( normdx, dyk )
564 END IF
565 END DO
566
567 IF ( normx .NE. 0.0 ) THEN
568 dx_x = normdx / normx
569 ELSE IF ( normdx .EQ. 0.0 ) THEN
570 dx_x = 0.0
571 ELSE
572 dx_x = hugeval
573 END IF
574
575 dxrat = normdx / prevnormdx
576 dzrat = dz_z / prev_dz_z
577*
578* Check termination criteria.
579*
580 IF ( .NOT.ignore_cwise
581 $ .AND. ymin*rcond .LT. incr_thresh*normy
582 $ .AND. y_prec_state .LT. extra_y )
583 $ incr_prec = .true.
584
585 IF ( x_state .EQ. noprog_state .AND. dxrat .LE. rthresh )
586 $ x_state = working_state
587 IF ( x_state .EQ. working_state ) THEN
588 IF ( dx_x .LE. eps ) THEN
589 x_state = conv_state
590 ELSE IF ( dxrat .GT. rthresh ) THEN
591 IF ( y_prec_state .NE. extra_y ) THEN
592 incr_prec = .true.
593 ELSE
594 x_state = noprog_state
595 END IF
596 ELSE
597 IF ( dxrat .GT. dxratmax ) dxratmax = dxrat
598 END IF
599 IF ( x_state .GT. working_state ) final_dx_x = dx_x
600 END IF
601
602 IF ( z_state .EQ. unstable_state .AND. dz_z .LE. dz_ub )
603 $ z_state = working_state
604 IF ( z_state .EQ. noprog_state .AND. dzrat .LE. rthresh )
605 $ z_state = working_state
606 IF ( z_state .EQ. working_state ) THEN
607 IF ( dz_z .LE. eps ) THEN
608 z_state = conv_state
609 ELSE IF ( dz_z .GT. dz_ub ) THEN
610 z_state = unstable_state
611 dzratmax = 0.0
612 final_dz_z = hugeval
613 ELSE IF ( dzrat .GT. rthresh ) THEN
614 IF ( y_prec_state .NE. extra_y ) THEN
615 incr_prec = .true.
616 ELSE
617 z_state = noprog_state
618 END IF
619 ELSE
620 IF ( dzrat .GT. dzratmax ) dzratmax = dzrat
621 END IF
622 IF ( z_state .GT. working_state ) final_dz_z = dz_z
623 END IF
624*
625* Exit if both normwise and componentwise stopped working,
626* but if componentwise is unstable, let it go at least two
627* iterations.
628*
629 IF ( x_state.NE.working_state ) THEN
630 IF ( ignore_cwise ) GOTO 666
631 IF ( z_state.EQ.noprog_state .OR. z_state.EQ.conv_state )
632 $ GOTO 666
633 IF ( z_state.EQ.unstable_state .AND. cnt.GT.1 ) GOTO 666
634 END IF
635
636 IF ( incr_prec ) THEN
637 incr_prec = .false.
638 y_prec_state = y_prec_state + 1
639 DO i = 1, n
640 y_tail( i ) = 0.0
641 END DO
642 END IF
643
644 prevnormdx = normdx
645 prev_dz_z = dz_z
646*
647* Update solution.
648*
649 IF (y_prec_state .LT. extra_y) THEN
650 CALL saxpy( n, 1.0, dy, 1, y(1,j), 1 )
651 ELSE
652 CALL sla_wwaddw( n, y(1,j), y_tail, dy )
653 END IF
654
655 END DO
656* Target of "IF (Z_STOP .AND. X_STOP)". Sun's f77 won't EXIT.
657 666 CONTINUE
658*
659* Set final_* when cnt hits ithresh.
660*
661 IF ( x_state .EQ. working_state ) final_dx_x = dx_x
662 IF ( z_state .EQ. working_state ) final_dz_z = dz_z
663*
664* Compute error bounds.
665*
666 IF ( n_norms .GE. 1 ) THEN
667 err_bnds_norm( j, la_linrx_err_i ) =
668 $ final_dx_x / (1 - dxratmax)
669 END IF
670 IF (n_norms .GE. 2) THEN
671 err_bnds_comp( j, la_linrx_err_i ) =
672 $ final_dz_z / (1 - dzratmax)
673 END IF
674*
675* Compute componentwise relative backward error from formula
676* max(i) ( abs(R(i)) / ( abs(op(A_s))*abs(Y) + abs(B_s) )(i) )
677* where abs(Z) is the componentwise absolute value of the matrix
678* or vector Z.
679*
680* Compute residual RES = B_s - op(A_s) * Y,
681* op(A) = A, A**T, or A**H depending on TRANS (and type).
682*
683 CALL scopy( n, b( 1, j ), 1, res, 1 )
684 CALL sgbmv(trans, n, n, kl, ku, -1.0, ab, ldab, y(1,j),
685 $ 1, 1.0, res, 1 )
686
687 DO i = 1, n
688 ayb( i ) = abs( b( i, j ) )
689 END DO
690*
691* Compute abs(op(A_s))*abs(Y) + abs(B_s).
692*
693 CALL sla_gbamv( trans_type, n, n, kl, ku, 1.0,
694 $ ab, ldab, y(1, j), 1, 1.0, ayb, 1 )
695
696 CALL sla_lin_berr( n, n, 1, res, ayb, berr_out( j ) )
697*
698* End of loop for each RHS
699*
700 END DO
701*
702 RETURN
703*
704* End of SLA_GBRFSX_EXTENDED
705*
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 sgbmv(trans, m, n, kl, ku, alpha, a, lda, x, incx, beta, y, incy)
SGBMV
Definition sgbmv.f:188
subroutine sgbtrs(trans, n, kl, ku, nrhs, ab, ldab, ipiv, b, ldb, info)
SGBTRS
Definition sgbtrs.f:138
subroutine sla_gbamv(trans, m, n, kl, ku, alpha, ab, ldab, x, incx, beta, y, incy)
SLA_GBAMV performs a matrix-vector operation to calculate error bounds.
Definition sla_gbamv.f:187
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:81
real function slamch(cmach)
SLAMCH
Definition slamch.f:68
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