OpenBLAS/lapack-netlib/SRC/sbdsqr.f

*> \brief \b SBDSQR
*
*  =========== DOCUMENTATION ===========
*
* Online html documentation available at
*            http://www.netlib.org/lapack/explore-html/
*
*> \htmlonly
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*> [TXT]</a>
*> \endhtmlonly
*
*  Definition:
*  ===========
*
*       SUBROUTINE SBDSQR( UPLO, N, NCVT, NRU, NCC, D, E, VT, LDVT, U,
*                          LDU, C, LDC, WORK, INFO )
*
*       .. Scalar Arguments ..
*       CHARACTER          UPLO
*       INTEGER            INFO, LDC, LDU, LDVT, N, NCC, NCVT, NRU
*       ..
*       .. Array Arguments ..
*       REAL               C( LDC, * ), D( * ), E( * ), U( LDU, * ),
*      $                   VT( LDVT, * ), WORK( * )
*       ..
*
*
*> \par Purpose:
*  =============
*>
*> \verbatim
*>
*> SBDSQR computes the singular values and, optionally, the right and/or
*> left singular vectors from the singular value decomposition (SVD) of
*> a real N-by-N (upper or lower) bidiagonal matrix B using the implicit
*> zero-shift QR algorithm.  The SVD of B has the form
*>
*>    B = Q * S * P**T
*>
*> where S is the diagonal matrix of singular values, Q is an orthogonal
*> matrix of left singular vectors, and P is an orthogonal matrix of
*> right singular vectors.  If left singular vectors are requested, this
*> subroutine actually returns U*Q instead of Q, and, if right singular
*> vectors are requested, this subroutine returns P**T*VT instead of
*> P**T, for given real input matrices U and VT.  When U and VT are the
*> orthogonal matrices that reduce a general matrix A to bidiagonal
*> form:  A = U*B*VT, as computed by SGEBRD, then
*>
*>    A = (U*Q) * S * (P**T*VT)
*>
*> is the SVD of A.  Optionally, the subroutine may also compute Q**T*C
*> for a given real input matrix C.
*>
*> See "Computing  Small Singular Values of Bidiagonal Matrices With
*> Guaranteed High Relative Accuracy," by J. Demmel and W. Kahan,
*> LAPACK Working Note #3 (or SIAM J. Sci. Statist. Comput. vol. 11,
*> no. 5, pp. 873-912, Sept 1990) and
*> "Accurate singular values and differential qd algorithms," by
*> B. Parlett and V. Fernando, Technical Report CPAM-554, Mathematics
*> Department, University of California at Berkeley, July 1992
*> for a detailed description of the algorithm.
*> \endverbatim
*
*  Arguments:
*  ==========
*
*> \param[in] UPLO
*> \verbatim
*>          UPLO is CHARACTER*1
*>          = 'U':  B is upper bidiagonal;
*>          = 'L':  B is lower bidiagonal.
*> \endverbatim
*>
*> \param[in] N
*> \verbatim
*>          N is INTEGER
*>          The order of the matrix B.  N >= 0.
*> \endverbatim
*>
*> \param[in] NCVT
*> \verbatim
*>          NCVT is INTEGER
*>          The number of columns of the matrix VT. NCVT >= 0.
*> \endverbatim
*>
*> \param[in] NRU
*> \verbatim
*>          NRU is INTEGER
*>          The number of rows of the matrix U. NRU >= 0.
*> \endverbatim
*>
*> \param[in] NCC
*> \verbatim
*>          NCC is INTEGER
*>          The number of columns of the matrix C. NCC >= 0.
*> \endverbatim
*>
*> \param[in,out] D
*> \verbatim
*>          D is REAL array, dimension (N)
*>          On entry, the n diagonal elements of the bidiagonal matrix B.
*>          On exit, if INFO=0, the singular values of B in decreasing
*>          order.
*> \endverbatim
*>
*> \param[in,out] E
*> \verbatim
*>          E is REAL array, dimension (N-1)
*>          On entry, the N-1 offdiagonal elements of the bidiagonal
*>          matrix B.
*>          On exit, if INFO = 0, E is destroyed; if INFO > 0, D and E
*>          will contain the diagonal and superdiagonal elements of a
*>          bidiagonal matrix orthogonally equivalent to the one given
*>          as input.
*> \endverbatim
*>
*> \param[in,out] VT
*> \verbatim
*>          VT is REAL array, dimension (LDVT, NCVT)
*>          On entry, an N-by-NCVT matrix VT.
*>          On exit, VT is overwritten by P**T * VT.
*>          Not referenced if NCVT = 0.
*> \endverbatim
*>
*> \param[in] LDVT
*> \verbatim
*>          LDVT is INTEGER
*>          The leading dimension of the array VT.
*>          LDVT >= max(1,N) if NCVT > 0; LDVT >= 1 if NCVT = 0.
*> \endverbatim
*>
*> \param[in,out] U
*> \verbatim
*>          U is REAL array, dimension (LDU, N)
*>          On entry, an NRU-by-N matrix U.
*>          On exit, U is overwritten by U * Q.
*>          Not referenced if NRU = 0.
*> \endverbatim
*>
*> \param[in] LDU
*> \verbatim
*>          LDU is INTEGER
*>          The leading dimension of the array U.  LDU >= max(1,NRU).
*> \endverbatim
*>
*> \param[in,out] C
*> \verbatim
*>          C is REAL array, dimension (LDC, NCC)
*>          On entry, an N-by-NCC matrix C.
*>          On exit, C is overwritten by Q**T * C.
*>          Not referenced if NCC = 0.
*> \endverbatim
*>
*> \param[in] LDC
*> \verbatim
*>          LDC is INTEGER
*>          The leading dimension of the array C.
*>          LDC >= max(1,N) if NCC > 0; LDC >=1 if NCC = 0.
*> \endverbatim
*>
*> \param[out] WORK
*> \verbatim
*>          WORK is REAL array, dimension (4*N)
*> \endverbatim
*>
*> \param[out] INFO
*> \verbatim
*>          INFO is INTEGER
*>          = 0:  successful exit
*>          < 0:  If INFO = -i, the i-th argument had an illegal value
*>          > 0:
*>             if NCVT = NRU = NCC = 0,
*>                = 1, a split was marked by a positive value in E
*>                = 2, current block of Z not diagonalized after 30*N
*>                     iterations (in inner while loop)
*>                = 3, termination criterion of outer while loop not met
*>                     (program created more than N unreduced blocks)
*>             else NCVT = NRU = NCC = 0,
*>                   the algorithm did not converge; D and E contain the
*>                   elements of a bidiagonal matrix which is orthogonally
*>                   similar to the input matrix B;  if INFO = i, i
*>                   elements of E have not converged to zero.
*> \endverbatim
*
*> \par Internal Parameters:
*  =========================
*>
*> \verbatim
*>  TOLMUL  REAL, default = max(10,min(100,EPS**(-1/8)))
*>          TOLMUL controls the convergence criterion of the QR loop.
*>          If it is positive, TOLMUL*EPS is the desired relative
*>             precision in the computed singular values.
*>          If it is negative, abs(TOLMUL*EPS*sigma_max) is the
*>             desired absolute accuracy in the computed singular
*>             values (corresponds to relative accuracy
*>             abs(TOLMUL*EPS) in the largest singular value.
*>          abs(TOLMUL) should be between 1 and 1/EPS, and preferably
*>             between 10 (for fast convergence) and .1/EPS
*>             (for there to be some accuracy in the results).
*>          Default is to lose at either one eighth or 2 of the
*>             available decimal digits in each computed singular value
*>             (whichever is smaller).
*>
*>  MAXITR  INTEGER, default = 6
*>          MAXITR controls the maximum number of passes of the
*>          algorithm through its inner loop. The algorithms stops
*>          (and so fails to converge) if the number of passes
*>          through the inner loop exceeds MAXITR*N**2.
*> \endverbatim
*
*> \par Note:
*  ===========
*>
*> \verbatim
*>  Bug report from Cezary Dendek.
*>  On March 23rd 2017, the INTEGER variable MAXIT = MAXITR*N**2 is
*>  removed since it can overflow pretty easily (for N larger or equal
*>  than 18,919). We instead use MAXITDIVN = MAXITR*N.
*> \endverbatim
*
*  Authors:
*  ========
*
*> \author Univ. of Tennessee
*> \author Univ. of California Berkeley
*> \author Univ. of Colorado Denver
*> \author NAG Ltd.
*
*> \date June 2017
*
*> \ingroup auxOTHERcomputational
*
*  =====================================================================
      SUBROUTINE SBDSQR( UPLO, N, NCVT, NRU, NCC, D, E, VT, LDVT, U,
     $                   LDU, C, LDC, WORK, INFO )
*
*  -- LAPACK computational routine (version 3.7.1) --
*  -- LAPACK is a software package provided by Univ. of Tennessee,    --
*  -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
*     June 2017
*
*     .. Scalar Arguments ..
      CHARACTER          UPLO
      INTEGER            INFO, LDC, LDU, LDVT, N, NCC, NCVT, NRU
*     ..
*     .. Array Arguments ..
      REAL               C( LDC, * ), D( * ), E( * ), U( LDU, * ),
     $                   VT( LDVT, * ), WORK( * )
*     ..
*
*  =====================================================================
*
*     .. Parameters ..
      REAL               ZERO
      PARAMETER          ( ZERO = 0.0E0 )
      REAL               ONE
      PARAMETER          ( ONE = 1.0E0 )
      REAL               NEGONE
      PARAMETER          ( NEGONE = -1.0E0 )
      REAL               HNDRTH
      PARAMETER          ( HNDRTH = 0.01E0 )
      REAL               TEN
      PARAMETER          ( TEN = 10.0E0 )
      REAL               HNDRD
      PARAMETER          ( HNDRD = 100.0E0 )
      REAL               MEIGTH
      PARAMETER          ( MEIGTH = -0.125E0 )
      INTEGER            MAXITR
      PARAMETER          ( MAXITR = 6 )
*     ..
*     .. Local Scalars ..
      LOGICAL            LOWER, ROTATE
      INTEGER            I, IDIR, ISUB, ITER, ITERDIVN, J, LL, LLL, M,
     $                   MAXITDIVN, NM1, NM12, NM13, OLDLL, OLDM
      REAL               ABSE, ABSS, COSL, COSR, CS, EPS, F, G, H, MU,
     $                   OLDCS, OLDSN, R, SHIFT, SIGMN, SIGMX, SINL,
     $                   SINR, SLL, SMAX, SMIN, SMINL,  SMINOA,
     $                   SN, THRESH, TOL, TOLMUL, UNFL
*     ..
*     .. External Functions ..
      LOGICAL            LSAME
      REAL               SLAMCH
      EXTERNAL           LSAME, SLAMCH
*     ..
*     .. External Subroutines ..
      EXTERNAL           SLARTG, SLAS2, SLASQ1, SLASR, SLASV2, SROT,
     $                   SSCAL, SSWAP, XERBLA
*     ..
*     .. Intrinsic Functions ..
      INTRINSIC          ABS, MAX, MIN, REAL, SIGN, SQRT
*     ..
*     .. Executable Statements ..
*
*     Test the input parameters.
*
      INFO = 0
      LOWER = LSAME( UPLO, 'L' )
      IF( .NOT.LSAME( UPLO, 'U' ) .AND. .NOT.LOWER ) THEN
         INFO = -1
      ELSE IF( N.LT.0 ) THEN
         INFO = -2
      ELSE IF( NCVT.LT.0 ) THEN
         INFO = -3
      ELSE IF( NRU.LT.0 ) THEN
         INFO = -4
      ELSE IF( NCC.LT.0 ) THEN
         INFO = -5
      ELSE IF( ( NCVT.EQ.0 .AND. LDVT.LT.1 ) .OR.
     $         ( NCVT.GT.0 .AND. LDVT.LT.MAX( 1, N ) ) ) THEN
         INFO = -9
      ELSE IF( LDU.LT.MAX( 1, NRU ) ) THEN
         INFO = -11
      ELSE IF( ( NCC.EQ.0 .AND. LDC.LT.1 ) .OR.
     $         ( NCC.GT.0 .AND. LDC.LT.MAX( 1, N ) ) ) THEN
         INFO = -13
      END IF
      IF( INFO.NE.0 ) THEN
         CALL XERBLA( 'SBDSQR', -INFO )
         RETURN
      END IF
      IF( N.EQ.0 )
     $   RETURN
      IF( N.EQ.1 )
     $   GO TO 160
*
*     ROTATE is true if any singular vectors desired, false otherwise
*
      ROTATE = ( NCVT.GT.0 ) .OR. ( NRU.GT.0 ) .OR. ( NCC.GT.0 )
*
*     If no singular vectors desired, use qd algorithm
*
      IF( .NOT.ROTATE ) THEN
         CALL SLASQ1( N, D, E, WORK, INFO )
*
*     If INFO equals 2, dqds didn't finish, try to finish
*
         IF( INFO .NE. 2 ) RETURN
         INFO = 0
      END IF
*
      NM1 = N - 1
      NM12 = NM1 + NM1
      NM13 = NM12 + NM1
      IDIR = 0
*
*     Get machine constants
*
      EPS = SLAMCH( 'Epsilon' )
      UNFL = SLAMCH( 'Safe minimum' )
*
*     If matrix lower bidiagonal, rotate to be upper bidiagonal
*     by applying Givens rotations on the left
*
      IF( LOWER ) THEN
         DO 10 I = 1, N - 1
            CALL SLARTG( D( I ), E( I ), CS, SN, R )
            D( I ) = R
            E( I ) = SN*D( I+1 )
            D( I+1 ) = CS*D( I+1 )
            WORK( I ) = CS
            WORK( NM1+I ) = SN
   10    CONTINUE
*
*        Update singular vectors if desired
*
         IF( NRU.GT.0 )
     $      CALL SLASR( 'R', 'V', 'F', NRU, N, WORK( 1 ), WORK( N ), U,
     $                  LDU )
         IF( NCC.GT.0 )
     $      CALL SLASR( 'L', 'V', 'F', N, NCC, WORK( 1 ), WORK( N ), C,
     $                  LDC )
      END IF
*
*     Compute singular values to relative accuracy TOL
*     (By setting TOL to be negative, algorithm will compute
*     singular values to absolute accuracy ABS(TOL)*norm(input matrix))
*
      TOLMUL = MAX( TEN, MIN( HNDRD, EPS**MEIGTH ) )
      TOL = TOLMUL*EPS
*
*     Compute approximate maximum, minimum singular values
*
      SMAX = ZERO
      DO 20 I = 1, N
         SMAX = MAX( SMAX, ABS( D( I ) ) )
   20 CONTINUE
      DO 30 I = 1, N - 1
         SMAX = MAX( SMAX, ABS( E( I ) ) )
   30 CONTINUE
      SMINL = ZERO
      IF( TOL.GE.ZERO ) THEN
*
*        Relative accuracy desired
*
         SMINOA = ABS( D( 1 ) )
         IF( SMINOA.EQ.ZERO )
     $      GO TO 50
         MU = SMINOA
         DO 40 I = 2, N
            MU = ABS( D( I ) )*( MU / ( MU+ABS( E( I-1 ) ) ) )
            SMINOA = MIN( SMINOA, MU )
            IF( SMINOA.EQ.ZERO )
     $         GO TO 50
   40    CONTINUE
   50    CONTINUE
         SMINOA = SMINOA / SQRT( REAL( N ) )
         THRESH = MAX( TOL*SMINOA, MAXITR*(N*(N*UNFL)) )
      ELSE
*
*        Absolute accuracy desired
*
         THRESH = MAX( ABS( TOL )*SMAX, MAXITR*(N*(N*UNFL)) )
      END IF
*
*     Prepare for main iteration loop for the singular values
*     (MAXIT is the maximum number of passes through the inner
*     loop permitted before nonconvergence signalled.)
*
      MAXITDIVN = MAXITR*N
      ITERDIVN = 0
      ITER = -1
      OLDLL = -1
      OLDM = -1
*
*     M points to last element of unconverged part of matrix
*
      M = N
*
*     Begin main iteration loop
*
   60 CONTINUE
*
*     Check for convergence or exceeding iteration count
*
      IF( M.LE.1 )
     $   GO TO 160
*
      IF( ITER.GE.N ) THEN
         ITER = ITER - N
         ITERDIVN = ITERDIVN + 1
         IF( ITERDIVN.GE.MAXITDIVN )
     $      GO TO 200
      END IF
*
*     Find diagonal block of matrix to work on
*
      IF( TOL.LT.ZERO .AND. ABS( D( M ) ).LE.THRESH )
     $   D( M ) = ZERO
      SMAX = ABS( D( M ) )
      SMIN = SMAX
      DO 70 LLL = 1, M - 1
         LL = M - LLL
         ABSS = ABS( D( LL ) )
         ABSE = ABS( E( LL ) )
         IF( TOL.LT.ZERO .AND. ABSS.LE.THRESH )
     $      D( LL ) = ZERO
         IF( ABSE.LE.THRESH )
     $      GO TO 80
         SMIN = MIN( SMIN, ABSS )
         SMAX = MAX( SMAX, ABSS, ABSE )
   70 CONTINUE
      LL = 0
      GO TO 90
   80 CONTINUE
      E( LL ) = ZERO
*
*     Matrix splits since E(LL) = 0
*
      IF( LL.EQ.M-1 ) THEN
*
*        Convergence of bottom singular value, return to top of loop
*
         M = M - 1
         GO TO 60
      END IF
   90 CONTINUE
      LL = LL + 1
*
*     E(LL) through E(M-1) are nonzero, E(LL-1) is zero
*
      IF( LL.EQ.M-1 ) THEN
*
*        2 by 2 block, handle separately
*
         CALL SLASV2( D( M-1 ), E( M-1 ), D( M ), SIGMN, SIGMX, SINR,
     $                COSR, SINL, COSL )
         D( M-1 ) = SIGMX
         E( M-1 ) = ZERO
         D( M ) = SIGMN
*
*        Compute singular vectors, if desired
*
         IF( NCVT.GT.0 )
     $      CALL SROT( NCVT, VT( M-1, 1 ), LDVT, VT( M, 1 ), LDVT, COSR,
     $                 SINR )
         IF( NRU.GT.0 )
     $      CALL SROT( NRU, U( 1, M-1 ), 1, U( 1, M ), 1, COSL, SINL )
         IF( NCC.GT.0 )
     $      CALL SROT( NCC, C( M-1, 1 ), LDC, C( M, 1 ), LDC, COSL,
     $                 SINL )
         M = M - 2
         GO TO 60
      END IF
*
*     If working on new submatrix, choose shift direction
*     (from larger end diagonal element towards smaller)
*
      IF( LL.GT.OLDM .OR. M.LT.OLDLL ) THEN
         IF( ABS( D( LL ) ).GE.ABS( D( M ) ) ) THEN
*
*           Chase bulge from top (big end) to bottom (small end)
*
            IDIR = 1
         ELSE
*
*           Chase bulge from bottom (big end) to top (small end)
*
            IDIR = 2
         END IF
      END IF
*
*     Apply convergence tests
*
      IF( IDIR.EQ.1 ) THEN
*
*        Run convergence test in forward direction
*        First apply standard test to bottom of matrix
*
         IF( ABS( E( M-1 ) ).LE.ABS( TOL )*ABS( D( M ) ) .OR.
     $       ( TOL.LT.ZERO .AND. ABS( E( M-1 ) ).LE.THRESH ) ) THEN
            E( M-1 ) = ZERO
            GO TO 60
         END IF
*
         IF( TOL.GE.ZERO ) THEN
*
*           If relative accuracy desired,
*           apply convergence criterion forward
*
            MU = ABS( D( LL ) )
            SMINL = MU
            DO 100 LLL = LL, M - 1
               IF( ABS( E( LLL ) ).LE.TOL*MU ) THEN
                  E( LLL ) = ZERO
                  GO TO 60
               END IF
               MU = ABS( D( LLL+1 ) )*( MU / ( MU+ABS( E( LLL ) ) ) )
               SMINL = MIN( SMINL, MU )
  100       CONTINUE
         END IF
*
      ELSE
*
*        Run convergence test in backward direction
*        First apply standard test to top of matrix
*
         IF( ABS( E( LL ) ).LE.ABS( TOL )*ABS( D( LL ) ) .OR.
     $       ( TOL.LT.ZERO .AND. ABS( E( LL ) ).LE.THRESH ) ) THEN
            E( LL ) = ZERO
            GO TO 60
         END IF
*
         IF( TOL.GE.ZERO ) THEN
*
*           If relative accuracy desired,
*           apply convergence criterion backward
*
            MU = ABS( D( M ) )
            SMINL = MU
            DO 110 LLL = M - 1, LL, -1
               IF( ABS( E( LLL ) ).LE.TOL*MU ) THEN
                  E( LLL ) = ZERO
                  GO TO 60
               END IF
               MU = ABS( D( LLL ) )*( MU / ( MU+ABS( E( LLL ) ) ) )
               SMINL = MIN( SMINL, MU )
  110       CONTINUE
         END IF
      END IF
      OLDLL = LL
      OLDM = M
*
*     Compute shift.  First, test if shifting would ruin relative
*     accuracy, and if so set the shift to zero.
*
      IF( TOL.GE.ZERO .AND. N*TOL*( SMINL / SMAX ).LE.
     $    MAX( EPS, HNDRTH*TOL ) ) THEN
*
*        Use a zero shift to avoid loss of relative accuracy
*
         SHIFT = ZERO
      ELSE
*
*        Compute the shift from 2-by-2 block at end of matrix
*
         IF( IDIR.EQ.1 ) THEN
            SLL = ABS( D( LL ) )
            CALL SLAS2( D( M-1 ), E( M-1 ), D( M ), SHIFT, R )
         ELSE
            SLL = ABS( D( M ) )
            CALL SLAS2( D( LL ), E( LL ), D( LL+1 ), SHIFT, R )
         END IF
*
*        Test if shift negligible, and if so set to zero
*
         IF( SLL.GT.ZERO ) THEN
            IF( ( SHIFT / SLL )**2.LT.EPS )
     $         SHIFT = ZERO
         END IF
      END IF
*
*     Increment iteration count
*
      ITER = ITER + M - LL
*
*     If SHIFT = 0, do simplified QR iteration
*
      IF( SHIFT.EQ.ZERO ) THEN
         IF( IDIR.EQ.1 ) THEN
*
*           Chase bulge from top to bottom
*           Save cosines and sines for later singular vector updates
*
            CS = ONE
            OLDCS = ONE
            DO 120 I = LL, M - 1
               CALL SLARTG( D( I )*CS, E( I ), CS, SN, R )
               IF( I.GT.LL )
     $            E( I-1 ) = OLDSN*R
               CALL SLARTG( OLDCS*R, D( I+1 )*SN, OLDCS, OLDSN, D( I ) )
               WORK( I-LL+1 ) = CS
               WORK( I-LL+1+NM1 ) = SN
               WORK( I-LL+1+NM12 ) = OLDCS
               WORK( I-LL+1+NM13 ) = OLDSN
  120       CONTINUE
            H = D( M )*CS
            D( M ) = H*OLDCS
            E( M-1 ) = H*OLDSN
*
*           Update singular vectors
*
            IF( NCVT.GT.0 )
     $         CALL SLASR( 'L', 'V', 'F', M-LL+1, NCVT, WORK( 1 ),
     $                     WORK( N ), VT( LL, 1 ), LDVT )
            IF( NRU.GT.0 )
     $         CALL SLASR( 'R', 'V', 'F', NRU, M-LL+1, WORK( NM12+1 ),
     $                     WORK( NM13+1 ), U( 1, LL ), LDU )
            IF( NCC.GT.0 )
     $         CALL SLASR( 'L', 'V', 'F', M-LL+1, NCC, WORK( NM12+1 ),
     $                     WORK( NM13+1 ), C( LL, 1 ), LDC )
*
*           Test convergence
*
            IF( ABS( E( M-1 ) ).LE.THRESH )
     $         E( M-1 ) = ZERO
*
         ELSE
*
*           Chase bulge from bottom to top
*           Save cosines and sines for later singular vector updates
*
            CS = ONE
            OLDCS = ONE
            DO 130 I = M, LL + 1, -1
               CALL SLARTG( D( I )*CS, E( I-1 ), CS, SN, R )
               IF( I.LT.M )
     $            E( I ) = OLDSN*R
               CALL SLARTG( OLDCS*R, D( I-1 )*SN, OLDCS, OLDSN, D( I ) )
               WORK( I-LL ) = CS
               WORK( I-LL+NM1 ) = -SN
               WORK( I-LL+NM12 ) = OLDCS
               WORK( I-LL+NM13 ) = -OLDSN
  130       CONTINUE
            H = D( LL )*CS
            D( LL ) = H*OLDCS
            E( LL ) = H*OLDSN
*
*           Update singular vectors
*
            IF( NCVT.GT.0 )
     $         CALL SLASR( 'L', 'V', 'B', M-LL+1, NCVT, WORK( NM12+1 ),
     $                     WORK( NM13+1 ), VT( LL, 1 ), LDVT )
            IF( NRU.GT.0 )
     $         CALL SLASR( 'R', 'V', 'B', NRU, M-LL+1, WORK( 1 ),
     $                     WORK( N ), U( 1, LL ), LDU )
            IF( NCC.GT.0 )
     $         CALL SLASR( 'L', 'V', 'B', M-LL+1, NCC, WORK( 1 ),
     $                     WORK( N ), C( LL, 1 ), LDC )
*
*           Test convergence
*
            IF( ABS( E( LL ) ).LE.THRESH )
     $         E( LL ) = ZERO
         END IF
      ELSE
*
*        Use nonzero shift
*
         IF( IDIR.EQ.1 ) THEN
*
*           Chase bulge from top to bottom
*           Save cosines and sines for later singular vector updates
*
            F = ( ABS( D( LL ) )-SHIFT )*
     $          ( SIGN( ONE, D( LL ) )+SHIFT / D( LL ) )
            G = E( LL )
            DO 140 I = LL, M - 1
               CALL SLARTG( F, G, COSR, SINR, R )
               IF( I.GT.LL )
     $            E( I-1 ) = R
               F = COSR*D( I ) + SINR*E( I )
               E( I ) = COSR*E( I ) - SINR*D( I )
               G = SINR*D( I+1 )
               D( I+1 ) = COSR*D( I+1 )
               CALL SLARTG( F, G, COSL, SINL, R )
               D( I ) = R
               F = COSL*E( I ) + SINL*D( I+1 )
               D( I+1 ) = COSL*D( I+1 ) - SINL*E( I )
               IF( I.LT.M-1 ) THEN
                  G = SINL*E( I+1 )
                  E( I+1 ) = COSL*E( I+1 )
               END IF
               WORK( I-LL+1 ) = COSR
               WORK( I-LL+1+NM1 ) = SINR
               WORK( I-LL+1+NM12 ) = COSL
               WORK( I-LL+1+NM13 ) = SINL
  140       CONTINUE
            E( M-1 ) = F
*
*           Update singular vectors
*
            IF( NCVT.GT.0 )
     $         CALL SLASR( 'L', 'V', 'F', M-LL+1, NCVT, WORK( 1 ),
     $                     WORK( N ), VT( LL, 1 ), LDVT )
            IF( NRU.GT.0 )
     $         CALL SLASR( 'R', 'V', 'F', NRU, M-LL+1, WORK( NM12+1 ),
     $                     WORK( NM13+1 ), U( 1, LL ), LDU )
            IF( NCC.GT.0 )
     $         CALL SLASR( 'L', 'V', 'F', M-LL+1, NCC, WORK( NM12+1 ),
     $                     WORK( NM13+1 ), C( LL, 1 ), LDC )
*
*           Test convergence
*
            IF( ABS( E( M-1 ) ).LE.THRESH )
     $         E( M-1 ) = ZERO
*
         ELSE
*
*           Chase bulge from bottom to top
*           Save cosines and sines for later singular vector updates
*
            F = ( ABS( D( M ) )-SHIFT )*( SIGN( ONE, D( M ) )+SHIFT /
     $          D( M ) )
            G = E( M-1 )
            DO 150 I = M, LL + 1, -1
               CALL SLARTG( F, G, COSR, SINR, R )
               IF( I.LT.M )
     $            E( I ) = R
               F = COSR*D( I ) + SINR*E( I-1 )
               E( I-1 ) = COSR*E( I-1 ) - SINR*D( I )
               G = SINR*D( I-1 )
               D( I-1 ) = COSR*D( I-1 )
               CALL SLARTG( F, G, COSL, SINL, R )
               D( I ) = R
               F = COSL*E( I-1 ) + SINL*D( I-1 )
               D( I-1 ) = COSL*D( I-1 ) - SINL*E( I-1 )
               IF( I.GT.LL+1 ) THEN
                  G = SINL*E( I-2 )
                  E( I-2 ) = COSL*E( I-2 )
               END IF
               WORK( I-LL ) = COSR
               WORK( I-LL+NM1 ) = -SINR
               WORK( I-LL+NM12 ) = COSL
               WORK( I-LL+NM13 ) = -SINL
  150       CONTINUE
            E( LL ) = F
*
*           Test convergence
*
            IF( ABS( E( LL ) ).LE.THRESH )
     $         E( LL ) = ZERO
*
*           Update singular vectors if desired
*
            IF( NCVT.GT.0 )
     $         CALL SLASR( 'L', 'V', 'B', M-LL+1, NCVT, WORK( NM12+1 ),
     $                     WORK( NM13+1 ), VT( LL, 1 ), LDVT )
            IF( NRU.GT.0 )
     $         CALL SLASR( 'R', 'V', 'B', NRU, M-LL+1, WORK( 1 ),
     $                     WORK( N ), U( 1, LL ), LDU )
            IF( NCC.GT.0 )
     $         CALL SLASR( 'L', 'V', 'B', M-LL+1, NCC, WORK( 1 ),
     $                     WORK( N ), C( LL, 1 ), LDC )
         END IF
      END IF
*
*     QR iteration finished, go back and check convergence
*
      GO TO 60
*
*     All singular values converged, so make them positive
*
  160 CONTINUE
      DO 170 I = 1, N
         IF( D( I ).LT.ZERO ) THEN
            D( I ) = -D( I )
*
*           Change sign of singular vectors, if desired
*
            IF( NCVT.GT.0 )
     $         CALL SSCAL( NCVT, NEGONE, VT( I, 1 ), LDVT )
         END IF
  170 CONTINUE
*
*     Sort the singular values into decreasing order (insertion sort on
*     singular values, but only one transposition per singular vector)
*
      DO 190 I = 1, N - 1
*
*        Scan for smallest D(I)
*
         ISUB = 1
         SMIN = D( 1 )
         DO 180 J = 2, N + 1 - I
            IF( D( J ).LE.SMIN ) THEN
               ISUB = J
               SMIN = D( J )
            END IF
  180    CONTINUE
         IF( ISUB.NE.N+1-I ) THEN
*
*           Swap singular values and vectors
*
            D( ISUB ) = D( N+1-I )
            D( N+1-I ) = SMIN
            IF( NCVT.GT.0 )
     $         CALL SSWAP( NCVT, VT( ISUB, 1 ), LDVT, VT( N+1-I, 1 ),
     $                     LDVT )
            IF( NRU.GT.0 )
     $         CALL SSWAP( NRU, U( 1, ISUB ), 1, U( 1, N+1-I ), 1 )
            IF( NCC.GT.0 )
     $         CALL SSWAP( NCC, C( ISUB, 1 ), LDC, C( N+1-I, 1 ), LDC )
         END IF
  190 CONTINUE
      GO TO 220
*
*     Maximum number of iterations exceeded, failure to converge
*
  200 CONTINUE
      INFO = 0
      DO 210 I = 1, N - 1
         IF( E( I ).NE.ZERO )
     $      INFO = INFO + 1
  210 CONTINUE
  220 CONTINUE
      RETURN
*
*     End of SBDSQR
*
      END