SUBROUTINE CBDSQR( UPLO, N, NCVT, NRU, NCC, D, E, VT, LDVT, U,
$ LDU, C, LDC, RWORK, INFO )
*
* -- LAPACK routine (version 3.0) --
* Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd.,
* Courant Institute, Argonne National Lab, and Rice University
* October 31, 1999
*
* .. 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 value decomposition (SVD) of a real
* N-by-N (upper or lower) bidiagonal matrix B: B = Q * S * P' (P'
* denotes the transpose of P), where S is a diagonal matrix with
* non-negative diagonal elements (the singular values of B), and Q
* and P are orthogonal matrices.
*
* The routine computes S, and optionally computes U * Q, P' * VT,
* or Q' * C, for given complex input matrices U, VT, and 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)
* On entry, the elements of E contain the
* offdiagonal elements of of the bidiagonal matrix whose SVD
* is desired. On normal exit (INFO = 0), E is destroyed.
* If the algorithm does not converge (INFO > 0), D and E
* will contain the diagonal and superdiagonal elements of a
* bidiagonal matrix orthogonally equivalent to the one given
* as input. E(N) is used for workspace.
*
* VT (input/output) COMPLEX array, dimension (LDVT, NCVT)
* On entry, an N-by-NCVT matrix VT.
* On exit, VT is overwritten by P' * VT.
* VT is 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.
* U is 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' * C.
* C is 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 (4*N)
*
* 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, SMINLO, 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
SMINLO = SMINL
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
SMINLO = SMINL
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
