slasd2.f

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*> \brief \b SLASD2 merges the two sets of singular values together into a single sorted set. Used by sbdsdc.
*
*  =========== DOCUMENTATION ===========
*
* Online html documentation available at 
*            http://www.netlib.org/lapack/explore-html/ 
*
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*> \endhtmlonly 
*
*  Definition:
*  ===========
*
*       SUBROUTINE SLASD2( NL, NR, SQRE, K, D, Z, ALPHA, BETA, U, LDU, VT,
*                          LDVT, DSIGMA, U2, LDU2, VT2, LDVT2, IDXP, IDX,
*                          IDXC, IDXQ, COLTYP, INFO )
* 
*       .. Scalar Arguments ..
*       INTEGER            INFO, K, LDU, LDU2, LDVT, LDVT2, NL, NR, SQRE
*       REAL               ALPHA, BETA
*       ..
*       .. Array Arguments ..
*       INTEGER            COLTYP( * ), IDX( * ), IDXC( * ), IDXP( * ),
*      $                   IDXQ( * )
*       REAL               D( * ), DSIGMA( * ), U( LDU, * ),
*      $                   U2( LDU2, * ), VT( LDVT, * ), VT2( LDVT2, * ),
*      $                   Z( * )
*       ..
*  
*
*> \par Purpose:
*  =============
*>
*> \verbatim
*>
*> SLASD2 merges the two sets of singular values together into a single
*> sorted set.  Then it tries to deflate the size of the problem.
*> There are two ways in which deflation can occur:  when two or more
*> singular values are close together or if there is a tiny entry in the
*> Z vector.  For each such occurrence the order of the related secular
*> equation problem is reduced by one.
*>
*> SLASD2 is called from SLASD1.
*> \endverbatim
*
*  Arguments:
*  ==========
*
*> \param[in] NL
*> \verbatim
*>          NL is INTEGER
*>         The row dimension of the upper block.  NL >= 1.
*> \endverbatim
*>
*> \param[in] NR
*> \verbatim
*>          NR is INTEGER
*>         The row dimension of the lower block.  NR >= 1.
*> \endverbatim
*>
*> \param[in] SQRE
*> \verbatim
*>          SQRE is INTEGER
*>         = 0: the lower block is an NR-by-NR square matrix.
*>         = 1: the lower block is an NR-by-(NR+1) rectangular matrix.
*>
*>         The bidiagonal matrix has N = NL + NR + 1 rows and
*>         M = N + SQRE >= N columns.
*> \endverbatim
*>
*> \param[out] K
*> \verbatim
*>          K is INTEGER
*>         Contains the dimension of the non-deflated matrix,
*>         This is the order of the related secular equation. 1 <= K <=N.
*> \endverbatim
*>
*> \param[in,out] D
*> \verbatim
*>          D is REAL array, dimension (N)
*>         On entry D contains the singular values of the two submatrices
*>         to be combined.  On exit D contains the trailing (N-K) updated
*>         singular values (those which were deflated) sorted into
*>         increasing order.
*> \endverbatim
*>
*> \param[out] Z
*> \verbatim
*>          Z is REAL array, dimension (N)
*>         On exit Z contains the updating row vector in the secular
*>         equation.
*> \endverbatim
*>
*> \param[in] ALPHA
*> \verbatim
*>          ALPHA is REAL
*>         Contains the diagonal element associated with the added row.
*> \endverbatim
*>
*> \param[in] BETA
*> \verbatim
*>          BETA is REAL
*>         Contains the off-diagonal element associated with the added
*>         row.
*> \endverbatim
*>
*> \param[in,out] U
*> \verbatim
*>          U is REAL array, dimension (LDU,N)
*>         On entry U contains the left singular vectors of two
*>         submatrices in the two square blocks with corners at (1,1),
*>         (NL, NL), and (NL+2, NL+2), (N,N).
*>         On exit U contains the trailing (N-K) updated left singular
*>         vectors (those which were deflated) in its last N-K columns.
*> \endverbatim
*>
*> \param[in] LDU
*> \verbatim
*>          LDU is INTEGER
*>         The leading dimension of the array U.  LDU >= N.
*> \endverbatim
*>
*> \param[in,out] VT
*> \verbatim
*>          VT is REAL array, dimension (LDVT,M)
*>         On entry VT**T contains the right singular vectors of two
*>         submatrices in the two square blocks with corners at (1,1),
*>         (NL+1, NL+1), and (NL+2, NL+2), (M,M).
*>         On exit VT**T contains the trailing (N-K) updated right singular
*>         vectors (those which were deflated) in its last N-K columns.
*>         In case SQRE =1, the last row of VT spans the right null
*>         space.
*> \endverbatim
*>
*> \param[in] LDVT
*> \verbatim
*>          LDVT is INTEGER
*>         The leading dimension of the array VT.  LDVT >= M.
*> \endverbatim
*>
*> \param[out] DSIGMA
*> \verbatim
*>          DSIGMA is REAL array, dimension (N)
*>         Contains a copy of the diagonal elements (K-1 singular values
*>         and one zero) in the secular equation.
*> \endverbatim
*>
*> \param[out] U2
*> \verbatim
*>          U2 is REAL array, dimension (LDU2,N)
*>         Contains a copy of the first K-1 left singular vectors which
*>         will be used by SLASD3 in a matrix multiply (SGEMM) to solve
*>         for the new left singular vectors. U2 is arranged into four
*>         blocks. The first block contains a column with 1 at NL+1 and
*>         zero everywhere else; the second block contains non-zero
*>         entries only at and above NL; the third contains non-zero
*>         entries only below NL+1; and the fourth is dense.
*> \endverbatim
*>
*> \param[in] LDU2
*> \verbatim
*>          LDU2 is INTEGER
*>         The leading dimension of the array U2.  LDU2 >= N.
*> \endverbatim
*>
*> \param[out] VT2
*> \verbatim
*>          VT2 is REAL array, dimension (LDVT2,N)
*>         VT2**T contains a copy of the first K right singular vectors
*>         which will be used by SLASD3 in a matrix multiply (SGEMM) to
*>         solve for the new right singular vectors. VT2 is arranged into
*>         three blocks. The first block contains a row that corresponds
*>         to the special 0 diagonal element in SIGMA; the second block
*>         contains non-zeros only at and before NL +1; the third block
*>         contains non-zeros only at and after  NL +2.
*> \endverbatim
*>
*> \param[in] LDVT2
*> \verbatim
*>          LDVT2 is INTEGER
*>         The leading dimension of the array VT2.  LDVT2 >= M.
*> \endverbatim
*>
*> \param[out] IDXP
*> \verbatim
*>          IDXP is INTEGER array, dimension (N)
*>         This will contain the permutation used to place deflated
*>         values of D at the end of the array. On output IDXP(2:K)
*>         points to the nondeflated D-values and IDXP(K+1:N)
*>         points to the deflated singular values.
*> \endverbatim
*>
*> \param[out] IDX
*> \verbatim
*>          IDX is INTEGER array, dimension (N)
*>         This will contain the permutation used to sort the contents of
*>         D into ascending order.
*> \endverbatim
*>
*> \param[out] IDXC
*> \verbatim
*>          IDXC is INTEGER array, dimension (N)
*>         This will contain the permutation used to arrange the columns
*>         of the deflated U matrix into three groups:  the first group
*>         contains non-zero entries only at and above NL, the second
*>         contains non-zero entries only below NL+2, and the third is
*>         dense.
*> \endverbatim
*>
*> \param[in,out] IDXQ
*> \verbatim
*>          IDXQ is INTEGER array, dimension (N)
*>         This contains the permutation which separately sorts the two
*>         sub-problems in D into ascending order.  Note that entries in
*>         the first hlaf of this permutation must first be moved one
*>         position backward; and entries in the second half
*>         must first have NL+1 added to their values.
*> \endverbatim
*>
*> \param[out] COLTYP
*> \verbatim
*>          COLTYP is INTEGER array, dimension (N)
*>         As workspace, this will contain a label which will indicate
*>         which of the following types a column in the U2 matrix or a
*>         row in the VT2 matrix is:
*>         1 : non-zero in the upper half only
*>         2 : non-zero in the lower half only
*>         3 : dense
*>         4 : deflated
*>
*>         On exit, it is an array of dimension 4, with COLTYP(I) being
*>         the dimension of the I-th type columns.
*> \endverbatim
*>
*> \param[out] INFO
*> \verbatim
*>          INFO is INTEGER
*>          = 0:  successful exit.
*>          < 0:  if INFO = -i, the i-th argument had an illegal value.
*> \endverbatim
*
*  Authors:
*  ========
*
*> \author Univ. of Tennessee 
*> \author Univ. of California Berkeley 
*> \author Univ. of Colorado Denver 
*> \author NAG Ltd. 
*
*> \date September 2012
*
*> \ingroup auxOTHERauxiliary
*
*> \par Contributors:
*  ==================
*>
*>     Ming Gu and Huan Ren, Computer Science Division, University of
*>     California at Berkeley, USA
*>
*  =====================================================================
      SUBROUTINE slasd2( NL, NR, SQRE, K, D, Z, ALPHA, BETA, U, LDU, VT,
     $                   ldvt, dsigma, u2, ldu2, vt2, ldvt2, idxp, idx,
     $                   idxc, idxq, coltyp, info )
*
*  -- LAPACK auxiliary routine (version 3.4.2) --
*  -- LAPACK is a software package provided by Univ. of Tennessee,    --
*  -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
*     September 2012
*
*     .. Scalar Arguments ..
      INTEGER            info, k, ldu, ldu2, ldvt, ldvt2, nl, nr, sqre
      REAL               alpha, beta
*     ..
*     .. Array Arguments ..
      INTEGER            coltyp( * ), idx( * ), idxc( * ), idxp( * ),
     $                   idxq( * )
      REAL               d( * ), dsigma( * ), u( ldu, * ),
     $                   u2( ldu2, * ), vt( ldvt, * ), vt2( ldvt2, * ),
     $                   z( * )
*     ..
*
*  =====================================================================
*
*     .. Parameters ..
      REAL               zero, one, two, eight
      parameter( zero = 0.0e+0, one = 1.0e+0, two = 2.0e+0,
     $                   eight = 8.0e+0 )
*     ..
*     .. Local Arrays ..
      INTEGER            ctot( 4 ), psm( 4 )
*     ..
*     .. Local Scalars ..
      INTEGER            ct, i, idxi, idxj, idxjp, j, jp, jprev, k2, m,
     $                   n, nlp1, nlp2
      REAL               c, eps, hlftol, s, tau, tol, z1
*     ..
*     .. External Functions ..
      REAL               slamch, slapy2
      EXTERNAL           slamch, slapy2
*     ..
*     .. External Subroutines ..
      EXTERNAL           scopy, slacpy, slamrg, slaset, srot, xerbla
*     ..
*     .. Intrinsic Functions ..
      INTRINSIC          abs, max
*     ..
*     .. Executable Statements ..
*
*     Test the input parameters.
*
      info = 0
*
      IF( nl.LT.1 ) THEN
         info = -1
      ELSE IF( nr.LT.1 ) THEN
         info = -2
      ELSE IF( ( sqre.NE.1 ) .AND. ( sqre.NE.0 ) ) THEN
         info = -3
      END IF
*
      n = nl + nr + 1
      m = n + sqre
*
      IF( ldu.LT.n ) THEN
         info = -10
      ELSE IF( ldvt.LT.m ) THEN
         info = -12
      ELSE IF( ldu2.LT.n ) THEN
         info = -15
      ELSE IF( ldvt2.LT.m ) THEN
         info = -17
      END IF
      IF( info.NE.0 ) THEN
         CALL xerbla( 'SLASD2', -info )
         return
      END IF
*
      nlp1 = nl + 1
      nlp2 = nl + 2
*
*     Generate the first part of the vector Z; and move the singular
*     values in the first part of D one position backward.
*
      z1 = alpha*vt( nlp1, nlp1 )
      z( 1 ) = z1
      DO 10 i = nl, 1, -1
         z( i+1 ) = alpha*vt( i, nlp1 )
         d( i+1 ) = d( i )
         idxq( i+1 ) = idxq( i ) + 1
   10 continue
*
*     Generate the second part of the vector Z.
*
      DO 20 i = nlp2, m
         z( i ) = beta*vt( i, nlp2 )
   20 continue
*
*     Initialize some reference arrays.
*
      DO 30 i = 2, nlp1
         coltyp( i ) = 1
   30 continue
      DO 40 i = nlp2, n
         coltyp( i ) = 2
   40 continue
*
*     Sort the singular values into increasing order
*
      DO 50 i = nlp2, n
         idxq( i ) = idxq( i ) + nlp1
   50 continue
*
*     DSIGMA, IDXC, IDXC, and the first column of U2
*     are used as storage space.
*
      DO 60 i = 2, n
         dsigma( i ) = d( idxq( i ) )
         u2( i, 1 ) = z( idxq( i ) )
         idxc( i ) = coltyp( idxq( i ) )
   60 continue
*
      CALL slamrg( nl, nr, dsigma( 2 ), 1, 1, idx( 2 ) )
*
      DO 70 i = 2, n
         idxi = 1 + idx( i )
         d( i ) = dsigma( idxi )
         z( i ) = u2( idxi, 1 )
         coltyp( i ) = idxc( idxi )
   70 continue
*
*     Calculate the allowable deflation tolerance
*
      eps = slamch( 'Epsilon' )
      tol = max( abs( alpha ), abs( beta ) )
      tol = eight*eps*max( abs( d( n ) ), tol )
*
*     There are 2 kinds of deflation -- first a value in the z-vector
*     is small, second two (or more) singular values are very close
*     together (their difference is small).
*
*     If the value in the z-vector is small, we simply permute the
*     array so that the corresponding singular value is moved to the
*     end.
*
*     If two values in the D-vector are close, we perform a two-sided
*     rotation designed to make one of the corresponding z-vector
*     entries zero, and then permute the array so that the deflated
*     singular value is moved to the end.
*
*     If there are multiple singular values then the problem deflates.
*     Here the number of equal singular values are found.  As each equal
*     singular value is found, an elementary reflector is computed to
*     rotate the corresponding singular subspace so that the
*     corresponding components of Z are zero in this new basis.
*
      k = 1
      k2 = n + 1
      DO 80 j = 2, n
         IF( abs( z( j ) ).LE.tol ) THEN
*
*           Deflate due to small z component.
*
            k2 = k2 - 1
            idxp( k2 ) = j
            coltyp( j ) = 4
            IF( j.EQ.n )
     $         go to 120
         ELSE
            jprev = j
            go to 90
         END IF
   80 continue
   90 continue
      j = jprev
  100 continue
      j = j + 1
      IF( j.GT.n )
     $   go to 110
      IF( abs( z( j ) ).LE.tol ) THEN
*
*        Deflate due to small z component.
*
         k2 = k2 - 1
         idxp( k2 ) = j
         coltyp( j ) = 4
      ELSE
*
*        Check if singular values are close enough to allow deflation.
*
         IF( abs( d( j )-d( jprev ) ).LE.tol ) THEN
*
*           Deflation is possible.
*
            s = z( jprev )
            c = z( j )
*
*           Find sqrt(a**2+b**2) without overflow or
*           destructive underflow.
*
            tau = slapy2( c, s )
            c = c / tau
            s = -s / tau
            z( j ) = tau
            z( jprev ) = zero
*
*           Apply back the Givens rotation to the left and right
*           singular vector matrices.
*
            idxjp = idxq( idx( jprev )+1 )
            idxj = idxq( idx( j )+1 )
            IF( idxjp.LE.nlp1 ) THEN
               idxjp = idxjp - 1
            END IF
            IF( idxj.LE.nlp1 ) THEN
               idxj = idxj - 1
            END IF
            CALL srot( n, u( 1, idxjp ), 1, u( 1, idxj ), 1, c, s )
            CALL srot( m, vt( idxjp, 1 ), ldvt, vt( idxj, 1 ), ldvt, c,
     $                 s )
            IF( coltyp( j ).NE.coltyp( jprev ) ) THEN
               coltyp( j ) = 3
            END IF
            coltyp( jprev ) = 4
            k2 = k2 - 1
            idxp( k2 ) = jprev
            jprev = j
         ELSE
            k = k + 1
            u2( k, 1 ) = z( jprev )
            dsigma( k ) = d( jprev )
            idxp( k ) = jprev
            jprev = j
         END IF
      END IF
      go to 100
  110 continue
*
*     Record the last singular value.
*
      k = k + 1
      u2( k, 1 ) = z( jprev )
      dsigma( k ) = d( jprev )
      idxp( k ) = jprev
*
  120 continue
*
*     Count up the total number of the various types of columns, then
*     form a permutation which positions the four column types into
*     four groups of uniform structure (although one or more of these
*     groups may be empty).
*
      DO 130 j = 1, 4
         ctot( j ) = 0
  130 continue
      DO 140 j = 2, n
         ct = coltyp( j )
         ctot( ct ) = ctot( ct ) + 1
  140 continue
*
*     PSM(*) = Position in SubMatrix (of types 1 through 4)
*
      psm( 1 ) = 2
      psm( 2 ) = 2 + ctot( 1 )
      psm( 3 ) = psm( 2 ) + ctot( 2 )
      psm( 4 ) = psm( 3 ) + ctot( 3 )
*
*     Fill out the IDXC array so that the permutation which it induces
*     will place all type-1 columns first, all type-2 columns next,
*     then all type-3's, and finally all type-4's, starting from the
*     second column. This applies similarly to the rows of VT.
*
      DO 150 j = 2, n
         jp = idxp( j )
         ct = coltyp( jp )
         idxc( psm( ct ) ) = j
         psm( ct ) = psm( ct ) + 1
  150 continue
*
*     Sort the singular values and corresponding singular vectors into
*     DSIGMA, U2, and VT2 respectively.  The singular values/vectors
*     which were not deflated go into the first K slots of DSIGMA, U2,
*     and VT2 respectively, while those which were deflated go into the
*     last N - K slots, except that the first column/row will be treated
*     separately.
*
      DO 160 j = 2, n
         jp = idxp( j )
         dsigma( j ) = d( jp )
         idxj = idxq( idx( idxp( idxc( j ) ) )+1 )
         IF( idxj.LE.nlp1 ) THEN
            idxj = idxj - 1
         END IF
         CALL scopy( n, u( 1, idxj ), 1, u2( 1, j ), 1 )
         CALL scopy( m, vt( idxj, 1 ), ldvt, vt2( j, 1 ), ldvt2 )
  160 continue
*
*     Determine DSIGMA(1), DSIGMA(2) and Z(1)
*
      dsigma( 1 ) = zero
      hlftol = tol / two
      IF( abs( dsigma( 2 ) ).LE.hlftol )
     $   dsigma( 2 ) = hlftol
      IF( m.GT.n ) THEN
         z( 1 ) = slapy2( z1, z( m ) )
         IF( z( 1 ).LE.tol ) THEN
            c = one
            s = zero
            z( 1 ) = tol
         ELSE
            c = z1 / z( 1 )
            s = z( m ) / z( 1 )
         END IF
      ELSE
         IF( abs( z1 ).LE.tol ) THEN
            z( 1 ) = tol
         ELSE
            z( 1 ) = z1
         END IF
      END IF
*
*     Move the rest of the updating row to Z.
*
      CALL scopy( k-1, u2( 2, 1 ), 1, z( 2 ), 1 )
*
*     Determine the first column of U2, the first row of VT2 and the
*     last row of VT.
*
      CALL slaset( 'A', n, 1, zero, zero, u2, ldu2 )
      u2( nlp1, 1 ) = one
      IF( m.GT.n ) THEN
         DO 170 i = 1, nlp1
            vt( m, i ) = -s*vt( nlp1, i )
            vt2( 1, i ) = c*vt( nlp1, i )
  170    continue
         DO 180 i = nlp2, m
            vt2( 1, i ) = s*vt( m, i )
            vt( m, i ) = c*vt( m, i )
  180    continue
      ELSE
         CALL scopy( m, vt( nlp1, 1 ), ldvt, vt2( 1, 1 ), ldvt2 )
      END IF
      IF( m.GT.n ) THEN
         CALL scopy( m, vt( m, 1 ), ldvt, vt2( m, 1 ), ldvt2 )
      END IF
*
*     The deflated singular values and their corresponding vectors go
*     into the back of D, U, and V respectively.
*
      IF( n.GT.k ) THEN
         CALL scopy( n-k, dsigma( k+1 ), 1, d( k+1 ), 1 )
         CALL slacpy( 'A', n, n-k, u2( 1, k+1 ), ldu2, u( 1, k+1 ),
     $                ldu )
         CALL slacpy( 'A', n-k, m, vt2( k+1, 1 ), ldvt2, vt( k+1, 1 ),
     $                ldvt )
      END IF
*
*     Copy CTOT into COLTYP for referencing in SLASD3.
*
      DO 190 j = 1, 4
         coltyp( j ) = ctot( j )
  190 continue
*
      return
*
*     End of SLASD2
*
      END