SUBROUTINE SBDSQR( UPLO, N, NCVT, NRU, NCC, D, E, VT, LDVT, U, \$ LDU, C, LDC, WORK, INFO ) * * -- LAPACK routine (version 3.1.1) -- * Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. * January 2007 * * .. 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( * ) * .. * * Purpose * ======= * * 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. * * 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) 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. * * 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) 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. * * LDU (input) INTEGER * The leading dimension of the array U. LDU >= max(1,NRU). * * C (input/output) 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. * * LDC (input) INTEGER * The leading dimension of the array C. * LDC >= max(1,N) if NCC > 0; LDC >=1 if NCC = 0. * * WORK (workspace) REAL array, dimension (2*N) * if NCVT = NRU = NCC = 0, (max(1, 4*N)) 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 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 ) 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 ) 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.) * 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 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