LAPACK  3.6.1
LAPACK: Linear Algebra PACKage
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.

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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 resonsible 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', '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 .LT. 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 definte 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.
Date
September 2012

Definition at line 414 of file sla_gbrfsx_extended.f.

414 *
415 * -- LAPACK computational routine (version 3.4.2) --
416 * -- LAPACK is a software package provided by Univ. of Tennessee, --
417 * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
418 * September 2012
419 *
420 * .. Scalar Arguments ..
421  INTEGER info, ldab, ldafb, ldb, ldy, n, kl, ku, nrhs,
422  $ prec_type, trans_type, n_norms, ithresh
423  LOGICAL colequ, ignore_cwise
424  REAL rthresh, dz_ub
425 * ..
426 * .. Array Arguments ..
427  INTEGER ipiv( * )
428  REAL ab( ldab, * ), afb( ldafb, * ), b( ldb, * ),
429  $ y( ldy, * ), res(*), dy(*), y_tail(*)
430  REAL c( * ), ayb(*), rcond, berr_out(*),
431  $ err_bnds_norm( nrhs, * ),
432  $ err_bnds_comp( nrhs, * )
433 * ..
434 *
435 * =====================================================================
436 *
437 * .. Local Scalars ..
438  CHARACTER trans
439  INTEGER cnt, i, j, m, x_state, z_state, y_prec_state
440  REAL yk, dyk, ymin, normy, normx, normdx, dxrat,
441  $ dzrat, prevnormdx, prev_dz_z, dxratmax,
442  $ dzratmax, dx_x, dz_z, final_dx_x, final_dz_z,
443  $ eps, hugeval, incr_thresh
444  LOGICAL incr_prec
445 * ..
446 * .. Parameters ..
447  INTEGER unstable_state, working_state, conv_state,
448  $ noprog_state, base_residual, extra_residual,
449  $ extra_y
450  parameter ( unstable_state = 0, working_state = 1,
451  $ conv_state = 2, noprog_state = 3 )
452  parameter ( base_residual = 0, extra_residual = 1,
453  $ extra_y = 2 )
454  INTEGER final_nrm_err_i, final_cmp_err_i, berr_i
455  INTEGER rcond_i, nrm_rcond_i, nrm_err_i, cmp_rcond_i
456  INTEGER cmp_err_i, piv_growth_i
457  parameter ( final_nrm_err_i = 1, final_cmp_err_i = 2,
458  $ berr_i = 3 )
459  parameter ( rcond_i = 4, nrm_rcond_i = 5, nrm_err_i = 6 )
460  parameter ( cmp_rcond_i = 7, cmp_err_i = 8,
461  $ piv_growth_i = 9 )
462  INTEGER la_linrx_itref_i, la_linrx_ithresh_i,
463  $ la_linrx_cwise_i
464  parameter ( la_linrx_itref_i = 1,
465  $ la_linrx_ithresh_i = 2 )
466  parameter ( la_linrx_cwise_i = 3 )
467  INTEGER la_linrx_trust_i, la_linrx_err_i,
468  $ la_linrx_rcond_i
469  parameter ( la_linrx_trust_i = 1, la_linrx_err_i = 2 )
470  parameter ( la_linrx_rcond_i = 3 )
471 * ..
472 * .. External Subroutines ..
473  EXTERNAL saxpy, scopy, sgbtrs, sgbmv, blas_sgbmv_x,
474  $ blas_sgbmv2_x, sla_gbamv, sla_wwaddw, slamch,
476  REAL slamch
477  CHARACTER chla_transtype
478 * ..
479 * .. Intrinsic Functions ..
480  INTRINSIC abs, max, min
481 * ..
482 * .. Executable Statements ..
483 *
484  IF (info.NE.0) RETURN
485  trans = chla_transtype(trans_type)
486  eps = slamch( 'Epsilon' )
487  hugeval = slamch( 'Overflow' )
488 * Force HUGEVAL to Inf
489  hugeval = hugeval * hugeval
490 * Using HUGEVAL may lead to spurious underflows.
491  incr_thresh = REAL( N ) * eps
492  m = kl+ku+1
493 
494  DO j = 1, nrhs
495  y_prec_state = extra_residual
496  IF ( y_prec_state .EQ. extra_y ) THEN
497  DO i = 1, n
498  y_tail( i ) = 0.0
499  END DO
500  END IF
501 
502  dxrat = 0.0
503  dxratmax = 0.0
504  dzrat = 0.0
505  dzratmax = 0.0
506  final_dx_x = hugeval
507  final_dz_z = hugeval
508  prevnormdx = hugeval
509  prev_dz_z = hugeval
510  dz_z = hugeval
511  dx_x = hugeval
512 
513  x_state = working_state
514  z_state = unstable_state
515  incr_prec = .false.
516 
517  DO cnt = 1, ithresh
518 *
519 * Compute residual RES = B_s - op(A_s) * Y,
520 * op(A) = A, A**T, or A**H depending on TRANS (and type).
521 *
522  CALL scopy( n, b( 1, j ), 1, res, 1 )
523  IF ( y_prec_state .EQ. base_residual ) THEN
524  CALL sgbmv( trans, m, n, kl, ku, -1.0, ab, ldab,
525  $ y( 1, j ), 1, 1.0, res, 1 )
526  ELSE IF ( y_prec_state .EQ. extra_residual ) THEN
527  CALL blas_sgbmv_x( trans_type, n, n, kl, ku,
528  $ -1.0, ab, ldab, y( 1, j ), 1, 1.0, res, 1,
529  $ prec_type )
530  ELSE
531  CALL blas_sgbmv2_x( trans_type, n, n, kl, ku, -1.0,
532  $ ab, ldab, y( 1, j ), y_tail, 1, 1.0, res, 1,
533  $ prec_type )
534  END IF
535 
536 ! XXX: RES is no longer needed.
537  CALL scopy( n, res, 1, dy, 1 )
538  CALL sgbtrs( trans, n, kl, ku, 1, afb, ldafb, ipiv, dy, n,
539  $ info )
540 *
541 * Calculate relative changes DX_X, DZ_Z and ratios DXRAT, DZRAT.
542 *
543  normx = 0.0
544  normy = 0.0
545  normdx = 0.0
546  dz_z = 0.0
547  ymin = hugeval
548 
549  DO i = 1, n
550  yk = abs( y( i, j ) )
551  dyk = abs( dy( i ) )
552 
553  IF ( yk .NE. 0.0 ) THEN
554  dz_z = max( dz_z, dyk / yk )
555  ELSE IF ( dyk .NE. 0.0 ) THEN
556  dz_z = hugeval
557  END IF
558 
559  ymin = min( ymin, yk )
560 
561  normy = max( normy, yk )
562 
563  IF ( colequ ) THEN
564  normx = max( normx, yk * c( i ) )
565  normdx = max( normdx, dyk * c( i ) )
566  ELSE
567  normx = normy
568  normdx = max( normdx, dyk )
569  END IF
570  END DO
571 
572  IF ( normx .NE. 0.0 ) THEN
573  dx_x = normdx / normx
574  ELSE IF ( normdx .EQ. 0.0 ) THEN
575  dx_x = 0.0
576  ELSE
577  dx_x = hugeval
578  END IF
579 
580  dxrat = normdx / prevnormdx
581  dzrat = dz_z / prev_dz_z
582 *
583 * Check termination criteria.
584 *
585  IF ( .NOT.ignore_cwise
586  $ .AND. ymin*rcond .LT. incr_thresh*normy
587  $ .AND. y_prec_state .LT. extra_y )
588  $ incr_prec = .true.
589 
590  IF ( x_state .EQ. noprog_state .AND. dxrat .LE. rthresh )
591  $ x_state = working_state
592  IF ( x_state .EQ. working_state ) THEN
593  IF ( dx_x .LE. eps ) THEN
594  x_state = conv_state
595  ELSE IF ( dxrat .GT. rthresh ) THEN
596  IF ( y_prec_state .NE. extra_y ) THEN
597  incr_prec = .true.
598  ELSE
599  x_state = noprog_state
600  END IF
601  ELSE
602  IF ( dxrat .GT. dxratmax ) dxratmax = dxrat
603  END IF
604  IF ( x_state .GT. working_state ) final_dx_x = dx_x
605  END IF
606 
607  IF ( z_state .EQ. unstable_state .AND. dz_z .LE. dz_ub )
608  $ z_state = working_state
609  IF ( z_state .EQ. noprog_state .AND. dzrat .LE. rthresh )
610  $ z_state = working_state
611  IF ( z_state .EQ. working_state ) THEN
612  IF ( dz_z .LE. eps ) THEN
613  z_state = conv_state
614  ELSE IF ( dz_z .GT. dz_ub ) THEN
615  z_state = unstable_state
616  dzratmax = 0.0
617  final_dz_z = hugeval
618  ELSE IF ( dzrat .GT. rthresh ) THEN
619  IF ( y_prec_state .NE. extra_y ) THEN
620  incr_prec = .true.
621  ELSE
622  z_state = noprog_state
623  END IF
624  ELSE
625  IF ( dzrat .GT. dzratmax ) dzratmax = dzrat
626  END IF
627  IF ( z_state .GT. working_state ) final_dz_z = dz_z
628  END IF
629 *
630 * Exit if both normwise and componentwise stopped working,
631 * but if componentwise is unstable, let it go at least two
632 * iterations.
633 *
634  IF ( x_state.NE.working_state ) THEN
635  IF ( ignore_cwise ) GOTO 666
636  IF ( z_state.EQ.noprog_state .OR. z_state.EQ.conv_state )
637  $ GOTO 666
638  IF ( z_state.EQ.unstable_state .AND. cnt.GT.1 ) GOTO 666
639  END IF
640 
641  IF ( incr_prec ) THEN
642  incr_prec = .false.
643  y_prec_state = y_prec_state + 1
644  DO i = 1, n
645  y_tail( i ) = 0.0
646  END DO
647  END IF
648 
649  prevnormdx = normdx
650  prev_dz_z = dz_z
651 *
652 * Update soluton.
653 *
654  IF (y_prec_state .LT. extra_y) THEN
655  CALL saxpy( n, 1.0, dy, 1, y(1,j), 1 )
656  ELSE
657  CALL sla_wwaddw( n, y(1,j), y_tail, dy )
658  END IF
659 
660  END DO
661 * Target of "IF (Z_STOP .AND. X_STOP)". Sun's f77 won't EXIT.
662  666 CONTINUE
663 *
664 * Set final_* when cnt hits ithresh.
665 *
666  IF ( x_state .EQ. working_state ) final_dx_x = dx_x
667  IF ( z_state .EQ. working_state ) final_dz_z = dz_z
668 *
669 * Compute error bounds.
670 *
671  IF ( n_norms .GE. 1 ) THEN
672  err_bnds_norm( j, la_linrx_err_i ) =
673  $ final_dx_x / (1 - dxratmax)
674  END IF
675  IF (n_norms .GE. 2) THEN
676  err_bnds_comp( j, la_linrx_err_i ) =
677  $ final_dz_z / (1 - dzratmax)
678  END IF
679 *
680 * Compute componentwise relative backward error from formula
681 * max(i) ( abs(R(i)) / ( abs(op(A_s))*abs(Y) + abs(B_s) )(i) )
682 * where abs(Z) is the componentwise absolute value of the matrix
683 * or vector Z.
684 *
685 * Compute residual RES = B_s - op(A_s) * Y,
686 * op(A) = A, A**T, or A**H depending on TRANS (and type).
687 *
688  CALL scopy( n, b( 1, j ), 1, res, 1 )
689  CALL sgbmv(trans, n, n, kl, ku, -1.0, ab, ldab, y(1,j),
690  $ 1, 1.0, res, 1 )
691 
692  DO i = 1, n
693  ayb( i ) = abs( b( i, j ) )
694  END DO
695 *
696 * Compute abs(op(A_s))*abs(Y) + abs(B_s).
697 *
698  CALL sla_gbamv( trans_type, n, n, kl, ku, 1.0,
699  $ ab, ldab, y(1, j), 1, 1.0, ayb, 1 )
700 
701  CALL sla_lin_berr( n, n, 1, res, ayb, berr_out( j ) )
702 *
703 * End of loop for each RHS
704 *
705  END DO
706 *
707  RETURN
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 sgbmv(TRANS, M, N, KL, KU, ALPHA, A, LDA, X, INCX, BETA, Y, INCY)
SGBMV
Definition: sgbmv.f:187
character *1 function chla_transtype(TRANS)
CHLA_TRANSTYPE
subroutine saxpy(N, SA, SX, INCX, SY, INCY)
SAXPY
Definition: saxpy.f:54
subroutine sgbtrs(TRANS, N, KL, KU, NRHS, AB, LDAB, IPIV, B, LDB, INFO)
SGBTRS
Definition: sgbtrs.f:140
real function slamch(CMACH)
SLAMCH
Definition: slamch.f:69
subroutine sla_lin_berr(N, NZ, NRHS, RES, AYB, BERR)
SLA_LIN_BERR computes a component-wise relative backward error.
Definition: sla_lin_berr.f:103
subroutine sla_wwaddw(N, X, Y, W)
SLA_WWADDW adds a vector into a doubled-single vector.
Definition: sla_wwaddw.f:83
subroutine scopy(N, SX, INCX, SY, INCY)
SCOPY
Definition: scopy.f:53

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