*> \brief \b SLASDQ computes the SVD of a real bidiagonal matrix with diagonal d and off-diagonal e. Used by sbdsdc. * * =========== DOCUMENTATION =========== * * Online html documentation available at * http://www.netlib.org/lapack/explore-html/ * *> \htmlonly *> Download SLASDQ + dependencies *> *> [TGZ] *> *> [ZIP] *> *> [TXT] *> \endhtmlonly * * Definition: * =========== * * SUBROUTINE SLASDQ( UPLO, SQRE, 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, SQRE * .. * .. Array Arguments .. * REAL C( LDC, * ), D( * ), E( * ), U( LDU, * ), * $ VT( LDVT, * ), WORK( * ) * .. * * *> \par Purpose: * ============= *> *> \verbatim *> *> SLASDQ computes the singular value decomposition (SVD) of a real *> (upper or lower) bidiagonal matrix with diagonal D and offdiagonal *> E, accumulating the transformations if desired. Letting B denote *> the input bidiagonal matrix, the algorithm computes orthogonal *> matrices Q and P such that B = Q * S * P**T (P**T denotes the transpose *> of P). The singular values S are overwritten on D. *> *> The input matrix U is changed to U * Q if desired. *> The input matrix VT is changed to P**T * VT if desired. *> The input matrix C is changed to Q**T * C if desired. *> *> See "Computing Small Singular Values of Bidiagonal Matrices With *> Guaranteed High Relative Accuracy," by J. Demmel and W. Kahan, *> LAPACK Working Note #3, for a detailed description of the algorithm. *> \endverbatim * * Arguments: * ========== * *> \param[in] UPLO *> \verbatim *> UPLO is CHARACTER*1 *> On entry, UPLO specifies whether the input bidiagonal matrix *> is upper or lower bidiagonal, and whether it is square are *> not. *> UPLO = 'U' or 'u' B is upper bidiagonal. *> UPLO = 'L' or 'l' B is lower bidiagonal. *> \endverbatim *> *> \param[in] SQRE *> \verbatim *> SQRE is INTEGER *> = 0: then the input matrix is N-by-N. *> = 1: then the input matrix is N-by-(N+1) if UPLU = 'U' and *> (N+1)-by-N if UPLU = 'L'. *> *> The bidiagonal matrix has *> N = NL + NR + 1 rows and *> M = N + SQRE >= N columns. *> \endverbatim *> *> \param[in] N *> \verbatim *> N is INTEGER *> On entry, N specifies the number of rows and columns *> in the matrix. N must be at least 0. *> \endverbatim *> *> \param[in] NCVT *> \verbatim *> NCVT is INTEGER *> On entry, NCVT specifies the number of columns of *> the matrix VT. NCVT must be at least 0. *> \endverbatim *> *> \param[in] NRU *> \verbatim *> NRU is INTEGER *> On entry, NRU specifies the number of rows of *> the matrix U. NRU must be at least 0. *> \endverbatim *> *> \param[in] NCC *> \verbatim *> NCC is INTEGER *> On entry, NCC specifies the number of columns of *> the matrix C. NCC must be at least 0. *> \endverbatim *> *> \param[in,out] D *> \verbatim *> D is REAL array, dimension (N) *> On entry, D contains the diagonal entries of the *> bidiagonal matrix whose SVD is desired. On normal exit, *> D contains the singular values in ascending order. *> \endverbatim *> *> \param[in,out] E *> \verbatim *> E is REAL array. *> dimension is (N-1) if SQRE = 0 and N if SQRE = 1. *> On entry, the entries of E contain the offdiagonal entries *> of the bidiagonal matrix whose SVD is desired. On normal *> exit, E will contain 0. If the algorithm does not converge, *> D and E will contain the diagonal and superdiagonal entries *> 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, contains a matrix which on exit has been *> premultiplied by P**T, dimension N-by-NCVT if SQRE = 0 *> and (N+1)-by-NCVT if SQRE = 1 (not referenced if NCVT=0). *> \endverbatim *> *> \param[in] LDVT *> \verbatim *> LDVT is INTEGER *> On entry, LDVT specifies the leading dimension of VT as *> declared in the calling (sub) program. LDVT must be at *> least 1. If NCVT is nonzero LDVT must also be at least N. *> \endverbatim *> *> \param[in,out] U *> \verbatim *> U is REAL array, dimension (LDU, N) *> On entry, contains a matrix which on exit has been *> postmultiplied by Q, dimension NRU-by-N if SQRE = 0 *> and NRU-by-(N+1) if SQRE = 1 (not referenced if NRU=0). *> \endverbatim *> *> \param[in] LDU *> \verbatim *> LDU is INTEGER *> On entry, LDU specifies the leading dimension of U as *> declared in the calling (sub) program. LDU must be at *> least max( 1, NRU ) . *> \endverbatim *> *> \param[in,out] C *> \verbatim *> C is REAL array, dimension (LDC, NCC) *> On entry, contains an N-by-NCC matrix which on exit *> has been premultiplied by Q**T dimension N-by-NCC if SQRE = 0 *> and (N+1)-by-NCC if SQRE = 1 (not referenced if NCC=0). *> \endverbatim *> *> \param[in] LDC *> \verbatim *> LDC is INTEGER *> On entry, LDC specifies the leading dimension of C as *> declared in the calling (sub) program. LDC must be at *> least 1. If NCC is nonzero, LDC must also be at least N. *> \endverbatim *> *> \param[out] WORK *> \verbatim *> WORK is REAL array, dimension (4*N) *> Workspace. Only referenced if one of NCVT, NRU, or NCC is *> nonzero, and if N is at least 2. *> \endverbatim *> *> \param[out] INFO *> \verbatim *> INFO is INTEGER *> On exit, a value of 0 indicates a successful exit. *> If INFO < 0, argument number -INFO is illegal. *> If INFO > 0, the algorithm did not converge, and INFO *> specifies how many superdiagonals did not converge. *> \endverbatim * * Authors: * ======== * *> \author Univ. of Tennessee *> \author Univ. of California Berkeley *> \author Univ. of Colorado Denver *> \author NAG Ltd. * *> \ingroup OTHERauxiliary * *> \par Contributors: * ================== *> *> Ming Gu and Huan Ren, Computer Science Division, University of *> California at Berkeley, USA *> * ===================================================================== SUBROUTINE SLASDQ( UPLO, SQRE, N, NCVT, NRU, NCC, D, E, VT, LDVT, $ U, LDU, C, LDC, WORK, INFO ) * * -- LAPACK auxiliary routine -- * -- LAPACK is a software package provided by Univ. of Tennessee, -- * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- * * .. Scalar Arguments .. CHARACTER UPLO INTEGER INFO, LDC, LDU, LDVT, N, NCC, NCVT, NRU, SQRE * .. * .. Array Arguments .. REAL C( LDC, * ), D( * ), E( * ), U( LDU, * ), $ VT( LDVT, * ), WORK( * ) * .. * * ===================================================================== * * .. Parameters .. REAL ZERO PARAMETER ( ZERO = 0.0E+0 ) * .. * .. Local Scalars .. LOGICAL ROTATE INTEGER I, ISUB, IUPLO, J, NP1, SQRE1 REAL CS, R, SMIN, SN * .. * .. External Subroutines .. EXTERNAL SBDSQR, SLARTG, SLASR, SSWAP, XERBLA * .. * .. External Functions .. LOGICAL LSAME EXTERNAL LSAME * .. * .. Intrinsic Functions .. INTRINSIC MAX * .. * .. Executable Statements .. * * Test the input parameters. * INFO = 0 IUPLO = 0 IF( LSAME( UPLO, 'U' ) ) $ IUPLO = 1 IF( LSAME( UPLO, 'L' ) ) $ IUPLO = 2 IF( IUPLO.EQ.0 ) THEN INFO = -1 ELSE IF( ( SQRE.LT.0 ) .OR. ( SQRE.GT.1 ) ) THEN INFO = -2 ELSE IF( N.LT.0 ) THEN INFO = -3 ELSE IF( NCVT.LT.0 ) THEN INFO = -4 ELSE IF( NRU.LT.0 ) THEN INFO = -5 ELSE IF( NCC.LT.0 ) THEN INFO = -6 ELSE IF( ( NCVT.EQ.0 .AND. LDVT.LT.1 ) .OR. $ ( NCVT.GT.0 .AND. LDVT.LT.MAX( 1, N ) ) ) THEN INFO = -10 ELSE IF( LDU.LT.MAX( 1, NRU ) ) THEN INFO = -12 ELSE IF( ( NCC.EQ.0 .AND. LDC.LT.1 ) .OR. $ ( NCC.GT.0 .AND. LDC.LT.MAX( 1, N ) ) ) THEN INFO = -14 END IF IF( INFO.NE.0 ) THEN CALL XERBLA( 'SLASDQ', -INFO ) RETURN END IF IF( N.EQ.0 ) $ RETURN * * ROTATE is true if any singular vectors desired, false otherwise * ROTATE = ( NCVT.GT.0 ) .OR. ( NRU.GT.0 ) .OR. ( NCC.GT.0 ) NP1 = N + 1 SQRE1 = SQRE * * If matrix non-square upper bidiagonal, rotate to be lower * bidiagonal. The rotations are on the right. * IF( ( IUPLO.EQ.1 ) .AND. ( SQRE1.EQ.1 ) ) 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 ) IF( ROTATE ) THEN WORK( I ) = CS WORK( N+I ) = SN END IF 10 CONTINUE CALL SLARTG( D( N ), E( N ), CS, SN, R ) D( N ) = R E( N ) = ZERO IF( ROTATE ) THEN WORK( N ) = CS WORK( N+N ) = SN END IF IUPLO = 2 SQRE1 = 0 * * Update singular vectors if desired. * IF( NCVT.GT.0 ) $ CALL SLASR( 'L', 'V', 'F', NP1, NCVT, WORK( 1 ), $ WORK( NP1 ), VT, LDVT ) END IF * * If matrix lower bidiagonal, rotate to be upper bidiagonal * by applying Givens rotations on the left. * IF( IUPLO.EQ.2 ) THEN DO 20 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 ) IF( ROTATE ) THEN WORK( I ) = CS WORK( N+I ) = SN END IF 20 CONTINUE * * If matrix (N+1)-by-N lower bidiagonal, one additional * rotation is needed. * IF( SQRE1.EQ.1 ) THEN CALL SLARTG( D( N ), E( N ), CS, SN, R ) D( N ) = R IF( ROTATE ) THEN WORK( N ) = CS WORK( N+N ) = SN END IF END IF * * Update singular vectors if desired. * IF( NRU.GT.0 ) THEN IF( SQRE1.EQ.0 ) THEN CALL SLASR( 'R', 'V', 'F', NRU, N, WORK( 1 ), $ WORK( NP1 ), U, LDU ) ELSE CALL SLASR( 'R', 'V', 'F', NRU, NP1, WORK( 1 ), $ WORK( NP1 ), U, LDU ) END IF END IF IF( NCC.GT.0 ) THEN IF( SQRE1.EQ.0 ) THEN CALL SLASR( 'L', 'V', 'F', N, NCC, WORK( 1 ), $ WORK( NP1 ), C, LDC ) ELSE CALL SLASR( 'L', 'V', 'F', NP1, NCC, WORK( 1 ), $ WORK( NP1 ), C, LDC ) END IF END IF END IF * * Call SBDSQR to compute the SVD of the reduced real * N-by-N upper bidiagonal matrix. * CALL SBDSQR( 'U', N, NCVT, NRU, NCC, D, E, VT, LDVT, U, LDU, C, $ LDC, WORK, INFO ) * * Sort the singular values into ascending order (insertion sort on * singular values, but only one transposition per singular vector) * DO 40 I = 1, N * * Scan for smallest D(I). * ISUB = I SMIN = D( I ) DO 30 J = I + 1, N IF( D( J ).LT.SMIN ) THEN ISUB = J SMIN = D( J ) END IF 30 CONTINUE IF( ISUB.NE.I ) THEN * * Swap singular values and vectors. * D( ISUB ) = D( I ) D( I ) = SMIN IF( NCVT.GT.0 ) $ CALL SSWAP( NCVT, VT( ISUB, 1 ), LDVT, VT( I, 1 ), LDVT ) IF( NRU.GT.0 ) $ CALL SSWAP( NRU, U( 1, ISUB ), 1, U( 1, I ), 1 ) IF( NCC.GT.0 ) $ CALL SSWAP( NCC, C( ISUB, 1 ), LDC, C( I, 1 ), LDC ) END IF 40 CONTINUE * RETURN * * End of SLASDQ * END