          SUBROUTINE CBDSQR( UPLO, N, NCVT, NRU, NCC, D, E, VT, LDVT, U,
         $                   LDU, C, LDC, RWORK, INFO )
    *
    *  -- LAPACK routine (version 3.1) --
    *     Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd..
    *     November 2006
    *
    *     .. Scalar Arguments ..
          CHARACTER          UPLO
          INTEGER            INFO, LDC, LDU, LDVT, N, NCC, NCVT, NRU
    *     ..
    *     .. Array Arguments ..
          REAL               D( * ), E( * ), RWORK( * )
          COMPLEX            C( LDC, * ), U( LDU, * ), VT( LDVT, * )
    *     ..
    *
    *  Purpose
    *  =======
    *
    *  CBDSQR 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**H
    *
    *  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**H*VT instead of
    *  P**H, for given complex input matrices U and VT.  When U and VT are
    *  the unitary matrices that reduce a general matrix A to bidiagonal
    *  form: A = U*B*VT, as computed by CGEBRD, then
    *
    *     A = (U*Q) * S * (P**H*VT)
    *
    *  is the SVD of A.  Optionally, the subroutine may also compute Q**H*C
    *  for a given complex 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.
    *
    *  Arguments
    *  =========
    *
    *  UPLO    (input) CHARACTER*1
    *          = 'U':  B is upper bidiagonal;
    *          = 'L':  B is lower bidiagonal.
    *
    *  N       (input) INTEGER
    *          The order of the matrix B.  N >= 0.
    *
    *  NCVT    (input) INTEGER
    *          The number of columns of the matrix VT. NCVT >= 0.
    *
    *  NRU     (input) INTEGER
    *          The number of rows of the matrix U. NRU >= 0.
    *
    *  NCC     (input) INTEGER
    *          The number of columns of the matrix C. NCC >= 0.
    *
    *  D       (input/output) 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.
    *
    *  E       (input/output) 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.
    *
    *  VT      (input/output) COMPLEX array, dimension (LDVT, NCVT)
    *          On entry, an N-by-NCVT matrix VT.
    *          On exit, VT is overwritten by P**H * VT.
    *          Not referenced if NCVT = 0.
    *
    *  LDVT    (input) INTEGER
    *          The leading dimension of the array VT.
    *          LDVT >= max(1,N) if NCVT > 0; LDVT >= 1 if NCVT = 0.
    *
    *  U       (input/output) COMPLEX 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.
    *
    *  LDU     (input) INTEGER
    *          The leading dimension of the array U.  LDU >= max(1,NRU).
    *
    *  C       (input/output) COMPLEX array, dimension (LDC, NCC)
    *          On entry, an N-by-NCC matrix C.
    *          On exit, C is overwritten by Q**H * C.
    *          Not referenced if NCC = 0.
    *
    *  LDC     (input) INTEGER
    *          The leading dimension of the array C.
    *          LDC >= max(1,N) if NCC > 0; LDC >=1 if NCC = 0.
    *
    *  RWORK   (workspace) REAL array, dimension (2*N)
    *          if NCVT = NRU = NCC = 0, (max(1, 4*N-4)) otherwise
    *
    *  INFO    (output) INTEGER
    *          = 0:  successful exit
    *          < 0:  If INFO = -i, the i-th argument had an illegal value
    *          > 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.
    *
    *  Internal Parameters
    *  ===================
    *
    *  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.
    *
    *  =====================================================================
    *
    *     .. 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, J, LL, LLL, M, MAXIT, 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           CLASR, CSROT, CSSCAL, CSWAP, SLARTG, SLAS2,
         $                   SLASQ1, SLASV2, 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( 'CBDSQR', -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, RWORK, INFO )
             RETURN
          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 )
                RWORK( I ) = CS
                RWORK( NM1+I ) = SN
       10    CONTINUE
    *
    *        Update singular vectors if desired
    *
             IF( NRU.GT.0 )
         $      CALL CLASR( 'R', 'V', 'F', NRU, N, RWORK( 1 ), RWORK( N ),
         $                  U, LDU )
             IF( NCC.GT.0 )
         $      CALL CLASR( 'L', 'V', 'F', N, NCC, RWORK( 1 ), RWORK( 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.)
    *
          MAXIT = MAXITR*N*N
          ITER = 0
          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.GT.MAXIT )
         $   GO TO 200
    *
    *     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 CSROT( NCVT, VT( M-1, 1 ), LDVT, VT( M, 1 ), LDVT,
         $                  COSR, SINR )
             IF( NRU.GT.0 )
         $      CALL CSROT( NRU, U( 1, M-1 ), 1, U( 1, M ), 1, COSL, SINL )
             IF( NCC.GT.0 )
         $      CALL CSROT( 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 ) )
                   RWORK( I-LL+1 ) = CS
                   RWORK( I-LL+1+NM1 ) = SN
                   RWORK( I-LL+1+NM12 ) = OLDCS
                   RWORK( 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 CLASR( 'L', 'V', 'F', M-LL+1, NCVT, RWORK( 1 ),
         $                     RWORK( N ), VT( LL, 1 ), LDVT )
                IF( NRU.GT.0 )
         $         CALL CLASR( 'R', 'V', 'F', NRU, M-LL+1, RWORK( NM12+1 ),
         $                     RWORK( NM13+1 ), U( 1, LL ), LDU )
                IF( NCC.GT.0 )
         $         CALL CLASR( 'L', 'V', 'F', M-LL+1, NCC, RWORK( NM12+1 ),
         $                     RWORK( 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 ) )
                   RWORK( I-LL ) = CS
                   RWORK( I-LL+NM1 ) = -SN
                   RWORK( I-LL+NM12 ) = OLDCS
                   RWORK( 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 CLASR( 'L', 'V', 'B', M-LL+1, NCVT, RWORK( NM12+1 ),
         $                     RWORK( NM13+1 ), VT( LL, 1 ), LDVT )
                IF( NRU.GT.0 )
         $         CALL CLASR( 'R', 'V', 'B', NRU, M-LL+1, RWORK( 1 ),
         $                     RWORK( N ), U( 1, LL ), LDU )
                IF( NCC.GT.0 )
         $         CALL CLASR( 'L', 'V', 'B', M-LL+1, NCC, RWORK( 1 ),
         $                     RWORK( 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
                   RWORK( I-LL+1 ) = COSR
                   RWORK( I-LL+1+NM1 ) = SINR
                   RWORK( I-LL+1+NM12 ) = COSL
                   RWORK( I-LL+1+NM13 ) = SINL
      140       CONTINUE
                E( M-1 ) = F
    *
    *           Update singular vectors
    *
                IF( NCVT.GT.0 )
         $         CALL CLASR( 'L', 'V', 'F', M-LL+1, NCVT, RWORK( 1 ),
         $                     RWORK( N ), VT( LL, 1 ), LDVT )
                IF( NRU.GT.0 )
         $         CALL CLASR( 'R', 'V', 'F', NRU, M-LL+1, RWORK( NM12+1 ),
         $                     RWORK( NM13+1 ), U( 1, LL ), LDU )
                IF( NCC.GT.0 )
         $         CALL CLASR( 'L', 'V', 'F', M-LL+1, NCC, RWORK( NM12+1 ),
         $                     RWORK( 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
                   RWORK( I-LL ) = COSR
                   RWORK( I-LL+NM1 ) = -SINR
                   RWORK( I-LL+NM12 ) = COSL
                   RWORK( 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 CLASR( 'L', 'V', 'B', M-LL+1, NCVT, RWORK( NM12+1 ),
         $                     RWORK( NM13+1 ), VT( LL, 1 ), LDVT )
                IF( NRU.GT.0 )
         $         CALL CLASR( 'R', 'V', 'B', NRU, M-LL+1, RWORK( 1 ),
         $                     RWORK( N ), U( 1, LL ), LDU )
                IF( NCC.GT.0 )
         $         CALL CLASR( 'L', 'V', 'B', M-LL+1, NCC, RWORK( 1 ),
         $                     RWORK( 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 CSSCAL( 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 CSWAP( NCVT, VT( ISUB, 1 ), LDVT, VT( N+1-I, 1 ),
         $                     LDVT )
                IF( NRU.GT.0 )
         $         CALL CSWAP( NRU, U( 1, ISUB ), 1, U( 1, N+1-I ), 1 )
                IF( NCC.GT.0 )
         $         CALL CSWAP( 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 CBDSQR
    *
          END