*> \brief ZGESVJ * * =========== DOCUMENTATION =========== * * Online html documentation available at * http://www.netlib.org/lapack/explore-html/ * *> \htmlonly *> Download ZGESVJ + dependencies *> *> [TGZ] *> *> [ZIP] *> *> [TXT] *> \endhtmlonly * * Definition: * =========== * * SUBROUTINE ZGESVJ( JOBA, JOBU, JOBV, M, N, A, LDA, SVA, MV, V, * LDV, CWORK, LWORK, RWORK, LRWORK, INFO ) * * .. Scalar Arguments .. * INTEGER INFO, LDA, LDV, LWORK, LRWORK, M, MV, N * CHARACTER*1 JOBA, JOBU, JOBV * .. * .. Array Arguments .. * COMPLEX*16 A( LDA, * ), V( LDV, * ), CWORK( LWORK ) * DOUBLE PRECISION RWORK( LRWORK ), SVA( N ) * .. * * *> \par Purpose: * ============= *> *> \verbatim *> *> ZGESVJ computes the singular value decomposition (SVD) of a complex *> M-by-N matrix A, where M >= N. The SVD of A is written as *> [++] [xx] [x0] [xx] *> A = U * SIGMA * V^*, [++] = [xx] * [ox] * [xx] *> [++] [xx] *> where SIGMA is an N-by-N diagonal matrix, U is an M-by-N orthonormal *> matrix, and V is an N-by-N unitary matrix. The diagonal elements *> of SIGMA are the singular values of A. The columns of U and V are the *> left and the right singular vectors of A, respectively. *> \endverbatim * * Arguments: * ========== * *> \param[in] JOBA *> \verbatim *> JOBA is CHARACTER*1 *> Specifies the structure of A. *> = 'L': The input matrix A is lower triangular; *> = 'U': The input matrix A is upper triangular; *> = 'G': The input matrix A is general M-by-N matrix, M >= N. *> \endverbatim *> *> \param[in] JOBU *> \verbatim *> JOBU is CHARACTER*1 *> Specifies whether to compute the left singular vectors *> (columns of U): *> = 'U' or 'F': The left singular vectors corresponding to the nonzero *> singular values are computed and returned in the leading *> columns of A. See more details in the description of A. *> The default numerical orthogonality threshold is set to *> approximately TOL=CTOL*EPS, CTOL=SQRT(M), EPS=DLAMCH('E'). *> = 'C': Analogous to JOBU='U', except that user can control the *> level of numerical orthogonality of the computed left *> singular vectors. TOL can be set to TOL = CTOL*EPS, where *> CTOL is given on input in the array WORK. *> No CTOL smaller than ONE is allowed. CTOL greater *> than 1 / EPS is meaningless. The option 'C' *> can be used if M*EPS is satisfactory orthogonality *> of the computed left singular vectors, so CTOL=M could *> save few sweeps of Jacobi rotations. *> See the descriptions of A and WORK(1). *> = 'N': The matrix U is not computed. However, see the *> description of A. *> \endverbatim *> *> \param[in] JOBV *> \verbatim *> JOBV is CHARACTER*1 *> Specifies whether to compute the right singular vectors, that *> is, the matrix V: *> = 'V' or 'J': the matrix V is computed and returned in the array V *> = 'A' : the Jacobi rotations are applied to the MV-by-N *> array V. In other words, the right singular vector *> matrix V is not computed explicitly; instead it is *> applied to an MV-by-N matrix initially stored in the *> first MV rows of V. *> = 'N' : the matrix V is not computed and the array V is not *> referenced *> \endverbatim *> *> \param[in] M *> \verbatim *> M is INTEGER *> The number of rows of the input matrix A. 1/DLAMCH('E') > M >= 0. *> \endverbatim *> *> \param[in] N *> \verbatim *> N is INTEGER *> The number of columns of the input matrix A. *> M >= N >= 0. *> \endverbatim *> *> \param[in,out] A *> \verbatim *> A is COMPLEX*16 array, dimension (LDA,N) *> On entry, the M-by-N matrix A. *> On exit, *> If JOBU .EQ. 'U' .OR. JOBU .EQ. 'C': *> If INFO .EQ. 0 : *> RANKA orthonormal columns of U are returned in the *> leading RANKA columns of the array A. Here RANKA <= N *> is the number of computed singular values of A that are *> above the underflow threshold DLAMCH('S'). The singular *> vectors corresponding to underflowed or zero singular *> values are not computed. The value of RANKA is returned *> in the array RWORK as RANKA=NINT(RWORK(2)). Also see the *> descriptions of SVA and RWORK. The computed columns of U *> are mutually numerically orthogonal up to approximately *> TOL=SQRT(M)*EPS (default); or TOL=CTOL*EPS (JOBU.EQ.'C'), *> see the description of JOBU. *> If INFO .GT. 0, *> the procedure ZGESVJ did not converge in the given number *> of iterations (sweeps). In that case, the computed *> columns of U may not be orthogonal up to TOL. The output *> U (stored in A), SIGMA (given by the computed singular *> values in SVA(1:N)) and V is still a decomposition of the *> input matrix A in the sense that the residual *> || A - SCALE * U * SIGMA * V^* ||_2 / ||A||_2 is small. *> If JOBU .EQ. 'N': *> If INFO .EQ. 0 : *> Note that the left singular vectors are 'for free' in the *> one-sided Jacobi SVD algorithm. However, if only the *> singular values are needed, the level of numerical *> orthogonality of U is not an issue and iterations are *> stopped when the columns of the iterated matrix are *> numerically orthogonal up to approximately M*EPS. Thus, *> on exit, A contains the columns of U scaled with the *> corresponding singular values. *> If INFO .GT. 0 : *> the procedure ZGESVJ did not converge in the given number *> of iterations (sweeps). *> \endverbatim *> *> \param[in] LDA *> \verbatim *> LDA is INTEGER *> The leading dimension of the array A. LDA >= max(1,M). *> \endverbatim *> *> \param[out] SVA *> \verbatim *> SVA is DOUBLE PRECISION array, dimension (N) *> On exit, *> If INFO .EQ. 0 : *> depending on the value SCALE = RWORK(1), we have: *> If SCALE .EQ. ONE: *> SVA(1:N) contains the computed singular values of A. *> During the computation SVA contains the Euclidean column *> norms of the iterated matrices in the array A. *> If SCALE .NE. ONE: *> The singular values of A are SCALE*SVA(1:N), and this *> factored representation is due to the fact that some of the *> singular values of A might underflow or overflow. *> *> If INFO .GT. 0 : *> the procedure ZGESVJ did not converge in the given number of *> iterations (sweeps) and SCALE*SVA(1:N) may not be accurate. *> \endverbatim *> *> \param[in] MV *> \verbatim *> MV is INTEGER *> If JOBV .EQ. 'A', then the product of Jacobi rotations in ZGESVJ *> is applied to the first MV rows of V. See the description of JOBV. *> \endverbatim *> *> \param[in,out] V *> \verbatim *> V is COMPLEX*16 array, dimension (LDV,N) *> If JOBV = 'V', then V contains on exit the N-by-N matrix of *> the right singular vectors; *> If JOBV = 'A', then V contains the product of the computed right *> singular vector matrix and the initial matrix in *> the array V. *> If JOBV = 'N', then V is not referenced. *> \endverbatim *> *> \param[in] LDV *> \verbatim *> LDV is INTEGER *> The leading dimension of the array V, LDV .GE. 1. *> If JOBV .EQ. 'V', then LDV .GE. max(1,N). *> If JOBV .EQ. 'A', then LDV .GE. max(1,MV) . *> \endverbatim *> *> \param[in,out] CWORK *> \verbatim *> CWORK is COMPLEX*16 array, dimension (max(1,LWORK)) *> Used as workspace. *> If on entry LWORK .EQ. -1, then a workspace query is assumed and *> no computation is done; CWORK(1) is set to the minial (and optimal) *> length of CWORK. *> \endverbatim *> *> \param[in] LWORK *> \verbatim *> LWORK is INTEGER. *> Length of CWORK, LWORK >= M+N. *> \endverbatim *> *> \param[in,out] RWORK *> \verbatim *> RWORK is DOUBLE PRECISION array, dimension (max(6,LRWORK)) *> On entry, *> If JOBU .EQ. 'C' : *> RWORK(1) = CTOL, where CTOL defines the threshold for convergence. *> The process stops if all columns of A are mutually *> orthogonal up to CTOL*EPS, EPS=DLAMCH('E'). *> It is required that CTOL >= ONE, i.e. it is not *> allowed to force the routine to obtain orthogonality *> below EPSILON. *> On exit, *> RWORK(1) = SCALE is the scaling factor such that SCALE*SVA(1:N) *> are the computed singular values of A. *> (See description of SVA().) *> RWORK(2) = NINT(RWORK(2)) is the number of the computed nonzero *> singular values. *> RWORK(3) = NINT(RWORK(3)) is the number of the computed singular *> values that are larger than the underflow threshold. *> RWORK(4) = NINT(RWORK(4)) is the number of sweeps of Jacobi *> rotations needed for numerical convergence. *> RWORK(5) = max_{i.NE.j} |COS(A(:,i),A(:,j))| in the last sweep. *> This is useful information in cases when ZGESVJ did *> not converge, as it can be used to estimate whether *> the output is stil useful and for post festum analysis. *> RWORK(6) = the largest absolute value over all sines of the *> Jacobi rotation angles in the last sweep. It can be *> useful for a post festum analysis. *> If on entry LRWORK .EQ. -1, then a workspace query is assumed and *> no computation is done; RWORK(1) is set to the minial (and optimal) *> length of RWORK. *> \endverbatim *> *> \param[in] LRWORK *> \verbatim *> LRWORK is INTEGER *> Length of RWORK, LRWORK >= MAX(6,N). *> \endverbatim *> *> \param[out] INFO *> \verbatim *> INFO is INTEGER *> = 0 : successful exit. *> < 0 : if INFO = -i, then the i-th argument had an illegal value *> > 0 : ZGESVJ did not converge in the maximal allowed number *> (NSWEEP=30) of sweeps. The output may still be useful. *> See the description of RWORK. *> \endverbatim *> * Authors: * ======== * *> \author Univ. of Tennessee *> \author Univ. of California Berkeley *> \author Univ. of Colorado Denver *> \author NAG Ltd. * *> \date June 2016 * *> \ingroup complex16GEcomputational * *> \par Further Details: * ===================== *> *> \verbatim *> *> The orthogonal N-by-N matrix V is obtained as a product of Jacobi plane *> rotations. In the case of underflow of the tangent of the Jacobi angle, a *> modified Jacobi transformation of Drmac  is used. Pivot strategy uses *> column interchanges of de Rijk . The relative accuracy of the computed *> singular values and the accuracy of the computed singular vectors (in *> angle metric) is as guaranteed by the theory of Demmel and Veselic . *> The condition number that determines the accuracy in the full rank case *> is essentially min_{D=diag} kappa(A*D), where kappa(.) is the *> spectral condition number. The best performance of this Jacobi SVD *> procedure is achieved if used in an accelerated version of Drmac and *> Veselic [4,5], and it is the kernel routine in the SIGMA library . *> Some tunning parameters (marked with [TP]) are available for the *> implementer. *> The computational range for the nonzero singular values is the machine *> number interval ( UNDERFLOW , OVERFLOW ). In extreme cases, even *> denormalized singular values can be computed with the corresponding *> gradual loss of accurate digits. *> \endverbatim * *> \par Contributor: * ================== *> *> \verbatim *> *> ============ *> *> Zlatko Drmac (Zagreb, Croatia) *> *> \endverbatim * *> \par References: * ================ *> *> \verbatim *> *>  P. P. M. De Rijk: A one-sided Jacobi algorithm for computing the *> singular value decomposition on a vector computer. *> SIAM J. Sci. Stat. Comp., Vol. 10 (1998), pp. 359-371. *>  J. Demmel and K. Veselic: Jacobi method is more accurate than QR. *>  Z. Drmac: Implementation of Jacobi rotations for accurate singular *> value computation in floating point arithmetic. *> SIAM J. Sci. Comp., Vol. 18 (1997), pp. 1200-1222. *>  Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm I. *> SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1322-1342. *> LAPACK Working note 169. *>  Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm II. *> SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1343-1362. *> LAPACK Working note 170. *>  Z. Drmac: SIGMA - mathematical software library for accurate SVD, PSV, *> QSVD, (H,K)-SVD computations. *> Department of Mathematics, University of Zagreb, 2008, 2015. *> \endverbatim * *> \par Bugs, examples and comments: * ================================= *> *> \verbatim *> =========================== *> Please report all bugs and send interesting test examples and comments to *> drmac@math.hr. Thank you. *> \endverbatim *> * ===================================================================== SUBROUTINE ZGESVJ( JOBA, JOBU, JOBV, M, N, A, LDA, SVA, MV, V, \$ LDV, CWORK, LWORK, RWORK, LRWORK, 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 2016 * IMPLICIT NONE * .. Scalar Arguments .. INTEGER INFO, LDA, LDV, LWORK, LRWORK, M, MV, N CHARACTER*1 JOBA, JOBU, JOBV * .. * .. Array Arguments .. COMPLEX*16 A( LDA, * ), V( LDV, * ), CWORK( LWORK ) DOUBLE PRECISION RWORK( LRWORK ), SVA( N ) * .. * * ===================================================================== * * .. Local Parameters .. DOUBLE PRECISION ZERO, HALF, ONE PARAMETER ( ZERO = 0.0D0, HALF = 0.5D0, ONE = 1.0D0) COMPLEX*16 CZERO, CONE PARAMETER ( CZERO = (0.0D0, 0.0D0), CONE = (1.0D0, 0.0D0) ) INTEGER NSWEEP PARAMETER ( NSWEEP = 30 ) * .. * .. Local Scalars .. COMPLEX*16 AAPQ, OMPQ DOUBLE PRECISION AAPP, AAPP0, AAPQ1, AAQQ, APOAQ, AQOAP, BIG, \$ BIGTHETA, CS, CTOL, EPSLN, MXAAPQ, \$ MXSINJ, ROOTBIG, ROOTEPS, ROOTSFMIN, ROOTTOL, \$ SKL, SFMIN, SMALL, SN, T, TEMP1, THETA, THSIGN, TOL INTEGER BLSKIP, EMPTSW, i, ibr, IERR, igl, IJBLSK, ir1, \$ ISWROT, jbc, jgl, KBL, LKAHEAD, MVL, N2, N34, \$ N4, NBL, NOTROT, p, PSKIPPED, q, ROWSKIP, SWBAND LOGICAL APPLV, GOSCALE, LOWER, LQUERY, LSVEC, NOSCALE, ROTOK, \$ RSVEC, UCTOL, UPPER * .. * .. * .. Intrinsic Functions .. INTRINSIC ABS, MAX, MIN, CONJG, DBLE, SIGN, SQRT * .. * .. External Functions .. * .. * from BLAS DOUBLE PRECISION DZNRM2 COMPLEX*16 ZDOTC EXTERNAL ZDOTC, DZNRM2 INTEGER IDAMAX EXTERNAL IDAMAX * from LAPACK DOUBLE PRECISION DLAMCH EXTERNAL DLAMCH LOGICAL LSAME EXTERNAL LSAME * .. * .. External Subroutines .. * .. * from BLAS EXTERNAL ZCOPY, ZROT, ZDSCAL, ZSWAP * from LAPACK EXTERNAL DLASCL, ZLASCL, ZLASET, ZLASSQ, XERBLA EXTERNAL ZGSVJ0, ZGSVJ1 * .. * .. Executable Statements .. * * Test the input arguments * LSVEC = LSAME( JOBU, 'U' ) .OR. LSAME( JOBU, 'F' ) UCTOL = LSAME( JOBU, 'C' ) RSVEC = LSAME( JOBV, 'V' ) .OR. LSAME( JOBV, 'J' ) APPLV = LSAME( JOBV, 'A' ) UPPER = LSAME( JOBA, 'U' ) LOWER = LSAME( JOBA, 'L' ) * LQUERY = ( LWORK .EQ. -1 ) .OR. ( LRWORK .EQ. -1 ) IF( .NOT.( UPPER .OR. LOWER .OR. LSAME( JOBA, 'G' ) ) ) THEN INFO = -1 ELSE IF( .NOT.( LSVEC .OR. UCTOL .OR. LSAME( JOBU, 'N' ) ) ) THEN INFO = -2 ELSE IF( .NOT.( RSVEC .OR. APPLV .OR. LSAME( JOBV, 'N' ) ) ) THEN INFO = -3 ELSE IF( M.LT.0 ) THEN INFO = -4 ELSE IF( ( N.LT.0 ) .OR. ( N.GT.M ) ) THEN INFO = -5 ELSE IF( LDA.LT.M ) THEN INFO = -7 ELSE IF( MV.LT.0 ) THEN INFO = -9 ELSE IF( ( RSVEC .AND. ( LDV.LT.N ) ) .OR. \$ ( APPLV .AND. ( LDV.LT.MV ) ) ) THEN INFO = -11 ELSE IF( UCTOL .AND. ( RWORK( 1 ).LE.ONE ) ) THEN INFO = -12 ELSE IF( ( LWORK.LT.( M+N ) ) .AND. ( .NOT.LQUERY ) ) THEN INFO = -13 ELSE IF( ( LRWORK.LT.MAX( N, 6 ) ) .AND. ( .NOT.LQUERY ) ) THEN INFO = -15 ELSE INFO = 0 END IF * * #:( IF( INFO.NE.0 ) THEN CALL XERBLA( 'ZGESVJ', -INFO ) RETURN ELSE IF ( LQUERY ) THEN CWORK(1) = M + N RWORK(1) = MAX( N, 6 ) RETURN END IF * * #:) Quick return for void matrix * IF( ( M.EQ.0 ) .OR. ( N.EQ.0 ) )RETURN * * Set numerical parameters * The stopping criterion for Jacobi rotations is * * max_{i<>j}|A(:,i)^* * A(:,j)| / (||A(:,i)||*||A(:,j)||) < CTOL*EPS * * where EPS is the round-off and CTOL is defined as follows: * IF( UCTOL ) THEN * ... user controlled CTOL = RWORK( 1 ) ELSE * ... default IF( LSVEC .OR. RSVEC .OR. APPLV ) THEN CTOL = SQRT( DBLE( M ) ) ELSE CTOL = DBLE( M ) END IF END IF * ... and the machine dependent parameters are *[!] (Make sure that SLAMCH() works properly on the target machine.) * EPSLN = DLAMCH( 'Epsilon' ) ROOTEPS = SQRT( EPSLN ) SFMIN = DLAMCH( 'SafeMinimum' ) ROOTSFMIN = SQRT( SFMIN ) SMALL = SFMIN / EPSLN BIG = DLAMCH( 'Overflow' ) * BIG = ONE / SFMIN ROOTBIG = ONE / ROOTSFMIN * LARGE = BIG / SQRT( DBLE( M*N ) ) BIGTHETA = ONE / ROOTEPS * TOL = CTOL*EPSLN ROOTTOL = SQRT( TOL ) * IF( DBLE( M )*EPSLN.GE.ONE ) THEN INFO = -4 CALL XERBLA( 'ZGESVJ', -INFO ) RETURN END IF * * Initialize the right singular vector matrix. * IF( RSVEC ) THEN MVL = N CALL ZLASET( 'A', MVL, N, CZERO, CONE, V, LDV ) ELSE IF( APPLV ) THEN MVL = MV END IF RSVEC = RSVEC .OR. APPLV * * Initialize SVA( 1:N ) = ( ||A e_i||_2, i = 1:N ) *(!) If necessary, scale A to protect the largest singular value * from overflow. It is possible that saving the largest singular * value destroys the information about the small ones. * This initial scaling is almost minimal in the sense that the * goal is to make sure that no column norm overflows, and that * SQRT(N)*max_i SVA(i) does not overflow. If INFinite entries * in A are detected, the procedure returns with INFO=-6. * SKL = ONE / SQRT( DBLE( M )*DBLE( N ) ) NOSCALE = .TRUE. GOSCALE = .TRUE. * IF( LOWER ) THEN * the input matrix is M-by-N lower triangular (trapezoidal) DO 1874 p = 1, N AAPP = ZERO AAQQ = ONE CALL ZLASSQ( M-p+1, A( p, p ), 1, AAPP, AAQQ ) IF( AAPP.GT.BIG ) THEN INFO = -6 CALL XERBLA( 'ZGESVJ', -INFO ) RETURN END IF AAQQ = SQRT( AAQQ ) IF( ( AAPP.LT.( BIG / AAQQ ) ) .AND. NOSCALE ) THEN SVA( p ) = AAPP*AAQQ ELSE NOSCALE = .FALSE. SVA( p ) = AAPP*( AAQQ*SKL ) IF( GOSCALE ) THEN GOSCALE = .FALSE. DO 1873 q = 1, p - 1 SVA( q ) = SVA( q )*SKL 1873 CONTINUE END IF END IF 1874 CONTINUE ELSE IF( UPPER ) THEN * the input matrix is M-by-N upper triangular (trapezoidal) DO 2874 p = 1, N AAPP = ZERO AAQQ = ONE CALL ZLASSQ( p, A( 1, p ), 1, AAPP, AAQQ ) IF( AAPP.GT.BIG ) THEN INFO = -6 CALL XERBLA( 'ZGESVJ', -INFO ) RETURN END IF AAQQ = SQRT( AAQQ ) IF( ( AAPP.LT.( BIG / AAQQ ) ) .AND. NOSCALE ) THEN SVA( p ) = AAPP*AAQQ ELSE NOSCALE = .FALSE. SVA( p ) = AAPP*( AAQQ*SKL ) IF( GOSCALE ) THEN GOSCALE = .FALSE. DO 2873 q = 1, p - 1 SVA( q ) = SVA( q )*SKL 2873 CONTINUE END IF END IF 2874 CONTINUE ELSE * the input matrix is M-by-N general dense DO 3874 p = 1, N AAPP = ZERO AAQQ = ONE CALL ZLASSQ( M, A( 1, p ), 1, AAPP, AAQQ ) IF( AAPP.GT.BIG ) THEN INFO = -6 CALL XERBLA( 'ZGESVJ', -INFO ) RETURN END IF AAQQ = SQRT( AAQQ ) IF( ( AAPP.LT.( BIG / AAQQ ) ) .AND. NOSCALE ) THEN SVA( p ) = AAPP*AAQQ ELSE NOSCALE = .FALSE. SVA( p ) = AAPP*( AAQQ*SKL ) IF( GOSCALE ) THEN GOSCALE = .FALSE. DO 3873 q = 1, p - 1 SVA( q ) = SVA( q )*SKL 3873 CONTINUE END IF END IF 3874 CONTINUE END IF * IF( NOSCALE )SKL = ONE * * Move the smaller part of the spectrum from the underflow threshold *(!) Start by determining the position of the nonzero entries of the * array SVA() relative to ( SFMIN, BIG ). * AAPP = ZERO AAQQ = BIG DO 4781 p = 1, N IF( SVA( p ).NE.ZERO )AAQQ = MIN( AAQQ, SVA( p ) ) AAPP = MAX( AAPP, SVA( p ) ) 4781 CONTINUE * * #:) Quick return for zero matrix * IF( AAPP.EQ.ZERO ) THEN IF( LSVEC )CALL ZLASET( 'G', M, N, CZERO, CONE, A, LDA ) RWORK( 1 ) = ONE RWORK( 2 ) = ZERO RWORK( 3 ) = ZERO RWORK( 4 ) = ZERO RWORK( 5 ) = ZERO RWORK( 6 ) = ZERO RETURN END IF * * #:) Quick return for one-column matrix * IF( N.EQ.1 ) THEN IF( LSVEC )CALL ZLASCL( 'G', 0, 0, SVA( 1 ), SKL, M, 1, \$ A( 1, 1 ), LDA, IERR ) RWORK( 1 ) = ONE / SKL IF( SVA( 1 ).GE.SFMIN ) THEN RWORK( 2 ) = ONE ELSE RWORK( 2 ) = ZERO END IF RWORK( 3 ) = ZERO RWORK( 4 ) = ZERO RWORK( 5 ) = ZERO RWORK( 6 ) = ZERO RETURN END IF * * Protect small singular values from underflow, and try to * avoid underflows/overflows in computing Jacobi rotations. * SN = SQRT( SFMIN / EPSLN ) TEMP1 = SQRT( BIG / DBLE( N ) ) IF( ( AAPP.LE.SN ) .OR. ( AAQQ.GE.TEMP1 ) .OR. \$ ( ( SN.LE.AAQQ ) .AND. ( AAPP.LE.TEMP1 ) ) ) THEN TEMP1 = MIN( BIG, TEMP1 / AAPP ) * AAQQ = AAQQ*TEMP1 * AAPP = AAPP*TEMP1 ELSE IF( ( AAQQ.LE.SN ) .AND. ( AAPP.LE.TEMP1 ) ) THEN TEMP1 = MIN( SN / AAQQ, BIG / (AAPP*SQRT( DBLE(N)) ) ) * AAQQ = AAQQ*TEMP1 * AAPP = AAPP*TEMP1 ELSE IF( ( AAQQ.GE.SN ) .AND. ( AAPP.GE.TEMP1 ) ) THEN TEMP1 = MAX( SN / AAQQ, TEMP1 / AAPP ) * AAQQ = AAQQ*TEMP1 * AAPP = AAPP*TEMP1 ELSE IF( ( AAQQ.LE.SN ) .AND. ( AAPP.GE.TEMP1 ) ) THEN TEMP1 = MIN( SN / AAQQ, BIG / ( SQRT( DBLE( N ) )*AAPP ) ) * AAQQ = AAQQ*TEMP1 * AAPP = AAPP*TEMP1 ELSE TEMP1 = ONE END IF * * Scale, if necessary * IF( TEMP1.NE.ONE ) THEN CALL DLASCL( 'G', 0, 0, ONE, TEMP1, N, 1, SVA, N, IERR ) END IF SKL = TEMP1*SKL IF( SKL.NE.ONE ) THEN CALL ZLASCL( JOBA, 0, 0, ONE, SKL, M, N, A, LDA, IERR ) SKL = ONE / SKL END IF * * Row-cyclic Jacobi SVD algorithm with column pivoting * EMPTSW = ( N*( N-1 ) ) / 2 NOTROT = 0 DO 1868 q = 1, N CWORK( q ) = CONE 1868 CONTINUE * * * SWBAND = 3 *[TP] SWBAND is a tuning parameter [TP]. It is meaningful and effective * if ZGESVJ is used as a computational routine in the preconditioned * Jacobi SVD algorithm ZGEJSV. For sweeps i=1:SWBAND the procedure * works on pivots inside a band-like region around the diagonal. * The boundaries are determined dynamically, based on the number of * pivots above a threshold. * KBL = MIN( 8, N ) *[TP] KBL is a tuning parameter that defines the tile size in the * tiling of the p-q loops of pivot pairs. In general, an optimal * value of KBL depends on the matrix dimensions and on the * parameters of the computer's memory. * NBL = N / KBL IF( ( NBL*KBL ).NE.N )NBL = NBL + 1 * BLSKIP = KBL**2 *[TP] BLKSKIP is a tuning parameter that depends on SWBAND and KBL. * ROWSKIP = MIN( 5, KBL ) *[TP] ROWSKIP is a tuning parameter. * LKAHEAD = 1 *[TP] LKAHEAD is a tuning parameter. * * Quasi block transformations, using the lower (upper) triangular * structure of the input matrix. The quasi-block-cycling usually * invokes cubic convergence. Big part of this cycle is done inside * canonical subspaces of dimensions less than M. * IF( ( LOWER .OR. UPPER ) .AND. ( N.GT.MAX( 64, 4*KBL ) ) ) THEN *[TP] The number of partition levels and the actual partition are * tuning parameters. N4 = N / 4 N2 = N / 2 N34 = 3*N4 IF( APPLV ) THEN q = 0 ELSE q = 1 END IF * IF( LOWER ) THEN * * This works very well on lower triangular matrices, in particular * in the framework of the preconditioned Jacobi SVD (xGEJSV). * The idea is simple: * [+ 0 0 0] Note that Jacobi transformations of [0 0] * [+ + 0 0] [0 0] * [+ + x 0] actually work on [x 0] [x 0] * [+ + x x] [x x]. [x x] * CALL ZGSVJ0( JOBV, M-N34, N-N34, A( N34+1, N34+1 ), LDA, \$ CWORK( N34+1 ), SVA( N34+1 ), MVL, \$ V( N34*q+1, N34+1 ), LDV, EPSLN, SFMIN, TOL, \$ 2, CWORK( N+1 ), LWORK-N, IERR ) CALL ZGSVJ0( JOBV, M-N2, N34-N2, A( N2+1, N2+1 ), LDA, \$ CWORK( N2+1 ), SVA( N2+1 ), MVL, \$ V( N2*q+1, N2+1 ), LDV, EPSLN, SFMIN, TOL, 2, \$ CWORK( N+1 ), LWORK-N, IERR ) CALL ZGSVJ1( JOBV, M-N2, N-N2, N4, A( N2+1, N2+1 ), LDA, \$ CWORK( N2+1 ), SVA( N2+1 ), MVL, \$ V( N2*q+1, N2+1 ), LDV, EPSLN, SFMIN, TOL, 1, \$ CWORK( N+1 ), LWORK-N, IERR ) CALL ZGSVJ0( JOBV, M-N4, N2-N4, A( N4+1, N4+1 ), LDA, \$ CWORK( N4+1 ), SVA( N4+1 ), MVL, \$ V( N4*q+1, N4+1 ), LDV, EPSLN, SFMIN, TOL, 1, \$ CWORK( N+1 ), LWORK-N, IERR ) * CALL ZGSVJ0( JOBV, M, N4, A, LDA, CWORK, SVA, MVL, V, LDV, \$ EPSLN, SFMIN, TOL, 1, CWORK( N+1 ), LWORK-N, \$ IERR ) * CALL ZGSVJ1( JOBV, M, N2, N4, A, LDA, CWORK, SVA, MVL, V, \$ LDV, EPSLN, SFMIN, TOL, 1, CWORK( N+1 ), \$ LWORK-N, IERR ) * * ELSE IF( UPPER ) THEN * * CALL ZGSVJ0( JOBV, N4, N4, A, LDA, CWORK, SVA, MVL, V, LDV, \$ EPSLN, SFMIN, TOL, 2, CWORK( N+1 ), LWORK-N, \$ IERR ) * CALL ZGSVJ0( JOBV, N2, N4, A( 1, N4+1 ), LDA, CWORK( N4+1 ), \$ SVA( N4+1 ), MVL, V( N4*q+1, N4+1 ), LDV, \$ EPSLN, SFMIN, TOL, 1, CWORK( N+1 ), LWORK-N, \$ IERR ) * CALL ZGSVJ1( JOBV, N2, N2, N4, A, LDA, CWORK, SVA, MVL, V, \$ LDV, EPSLN, SFMIN, TOL, 1, CWORK( N+1 ), \$ LWORK-N, IERR ) * CALL ZGSVJ0( JOBV, N2+N4, N4, A( 1, N2+1 ), LDA, \$ CWORK( N2+1 ), SVA( N2+1 ), MVL, \$ V( N2*q+1, N2+1 ), LDV, EPSLN, SFMIN, TOL, 1, \$ CWORK( N+1 ), LWORK-N, IERR ) END IF * END IF * * .. Row-cyclic pivot strategy with de Rijk's pivoting .. * DO 1993 i = 1, NSWEEP * * .. go go go ... * MXAAPQ = ZERO MXSINJ = ZERO ISWROT = 0 * NOTROT = 0 PSKIPPED = 0 * * Each sweep is unrolled using KBL-by-KBL tiles over the pivot pairs * 1 <= p < q <= N. This is the first step toward a blocked implementation * of the rotations. New implementation, based on block transformations, * is under development. * DO 2000 ibr = 1, NBL * igl = ( ibr-1 )*KBL + 1 * DO 1002 ir1 = 0, MIN( LKAHEAD, NBL-ibr ) * igl = igl + ir1*KBL * DO 2001 p = igl, MIN( igl+KBL-1, N-1 ) * * .. de Rijk's pivoting * q = IDAMAX( N-p+1, SVA( p ), 1 ) + p - 1 IF( p.NE.q ) THEN CALL ZSWAP( M, A( 1, p ), 1, A( 1, q ), 1 ) IF( RSVEC )CALL ZSWAP( MVL, V( 1, p ), 1, \$ V( 1, q ), 1 ) TEMP1 = SVA( p ) SVA( p ) = SVA( q ) SVA( q ) = TEMP1 AAPQ = CWORK(p) CWORK(p) = CWORK(q) CWORK(q) = AAPQ END IF * IF( ir1.EQ.0 ) THEN * * Column norms are periodically updated by explicit * norm computation. *[!] Caveat: * Unfortunately, some BLAS implementations compute DZNRM2(M,A(1,p),1) * as SQRT(S=CDOTC(M,A(1,p),1,A(1,p),1)), which may cause the result to * overflow for ||A(:,p)||_2 > SQRT(overflow_threshold), and to * underflow for ||A(:,p)||_2 < SQRT(underflow_threshold). * Hence, DZNRM2 cannot be trusted, not even in the case when * the true norm is far from the under(over)flow boundaries. * If properly implemented SCNRM2 is available, the IF-THEN-ELSE-END IF * below should be replaced with "AAPP = DZNRM2( M, A(1,p), 1 )". * IF( ( SVA( p ).LT.ROOTBIG ) .AND. \$ ( SVA( p ).GT.ROOTSFMIN ) ) THEN SVA( p ) = DZNRM2( M, A( 1, p ), 1 ) ELSE TEMP1 = ZERO AAPP = ONE CALL ZLASSQ( M, A( 1, p ), 1, TEMP1, AAPP ) SVA( p ) = TEMP1*SQRT( AAPP ) END IF AAPP = SVA( p ) ELSE AAPP = SVA( p ) END IF * IF( AAPP.GT.ZERO ) THEN * PSKIPPED = 0 * DO 2002 q = p + 1, MIN( igl+KBL-1, N ) * AAQQ = SVA( q ) * IF( AAQQ.GT.ZERO ) THEN * AAPP0 = AAPP IF( AAQQ.GE.ONE ) THEN ROTOK = ( SMALL*AAPP ).LE.AAQQ IF( AAPP.LT.( BIG / AAQQ ) ) THEN AAPQ = ( ZDOTC( M, A( 1, p ), 1, \$ A( 1, q ), 1 ) / AAQQ ) / AAPP ELSE CALL ZCOPY( M, A( 1, p ), 1, \$ CWORK(N+1), 1 ) CALL ZLASCL( 'G', 0, 0, AAPP, ONE, \$ M, 1, CWORK(N+1), LDA, IERR ) AAPQ = ZDOTC( M, CWORK(N+1), 1, \$ A( 1, q ), 1 ) / AAQQ END IF ELSE ROTOK = AAPP.LE.( AAQQ / SMALL ) IF( AAPP.GT.( SMALL / AAQQ ) ) THEN AAPQ = ( ZDOTC( M, A( 1, p ), 1, \$ A( 1, q ), 1 ) / AAPP ) / AAQQ ELSE CALL ZCOPY( M, A( 1, q ), 1, \$ CWORK(N+1), 1 ) CALL ZLASCL( 'G', 0, 0, AAQQ, \$ ONE, M, 1, \$ CWORK(N+1), LDA, IERR ) AAPQ = ZDOTC( M, A(1, p ), 1, \$ CWORK(N+1), 1 ) / AAPP END IF END IF * * AAPQ = AAPQ * CONJG( CWORK(p) ) * CWORK(q) AAPQ1 = -ABS(AAPQ) MXAAPQ = MAX( MXAAPQ, -AAPQ1 ) * * TO rotate or NOT to rotate, THAT is the question ... * IF( ABS( AAPQ1 ).GT.TOL ) THEN OMPQ = AAPQ / ABS(AAPQ) * * .. rotate *[RTD] ROTATED = ROTATED + ONE * IF( ir1.EQ.0 ) THEN NOTROT = 0 PSKIPPED = 0 ISWROT = ISWROT + 1 END IF * IF( ROTOK ) THEN * AQOAP = AAQQ / AAPP APOAQ = AAPP / AAQQ THETA = -HALF*ABS( AQOAP-APOAQ )/AAPQ1 * IF( ABS( THETA ).GT.BIGTHETA ) THEN * T = HALF / THETA CS = ONE CALL ZROT( M, A(1,p), 1, A(1,q), 1, \$ CS, CONJG(OMPQ)*T ) IF ( RSVEC ) THEN CALL ZROT( MVL, V(1,p), 1, \$ V(1,q), 1, CS, CONJG(OMPQ)*T ) END IF SVA( q ) = AAQQ*SQRT( MAX( ZERO, \$ ONE+T*APOAQ*AAPQ1 ) ) AAPP = AAPP*SQRT( MAX( ZERO, \$ ONE-T*AQOAP*AAPQ1 ) ) MXSINJ = MAX( MXSINJ, ABS( T ) ) * ELSE * * .. choose correct signum for THETA and rotate * THSIGN = -SIGN( ONE, AAPQ1 ) T = ONE / ( THETA+THSIGN* \$ SQRT( ONE+THETA*THETA ) ) CS = SQRT( ONE / ( ONE+T*T ) ) SN = T*CS * MXSINJ = MAX( MXSINJ, ABS( SN ) ) SVA( q ) = AAQQ*SQRT( MAX( ZERO, \$ ONE+T*APOAQ*AAPQ1 ) ) AAPP = AAPP*SQRT( MAX( ZERO, \$ ONE-T*AQOAP*AAPQ1 ) ) * CALL ZROT( M, A(1,p), 1, A(1,q), 1, \$ CS, CONJG(OMPQ)*SN ) IF ( RSVEC ) THEN CALL ZROT( MVL, V(1,p), 1, \$ V(1,q), 1, CS, CONJG(OMPQ)*SN ) END IF END IF CWORK(p) = -CWORK(q) * OMPQ * ELSE * .. have to use modified Gram-Schmidt like transformation CALL ZCOPY( M, A( 1, p ), 1, \$ CWORK(N+1), 1 ) CALL ZLASCL( 'G', 0, 0, AAPP, ONE, M, \$ 1, CWORK(N+1), LDA, \$ IERR ) CALL ZLASCL( 'G', 0, 0, AAQQ, ONE, M, \$ 1, A( 1, q ), LDA, IERR ) CALL ZAXPY( M, -AAPQ, CWORK(N+1), 1, \$ A( 1, q ), 1 ) CALL ZLASCL( 'G', 0, 0, ONE, AAQQ, M, \$ 1, A( 1, q ), LDA, IERR ) SVA( q ) = AAQQ*SQRT( MAX( ZERO, \$ ONE-AAPQ1*AAPQ1 ) ) MXSINJ = MAX( MXSINJ, SFMIN ) END IF * END IF ROTOK THEN ... ELSE * * In the case of cancellation in updating SVA(q), SVA(p) * recompute SVA(q), SVA(p). * IF( ( SVA( q ) / AAQQ )**2.LE.ROOTEPS ) \$ THEN IF( ( AAQQ.LT.ROOTBIG ) .AND. \$ ( AAQQ.GT.ROOTSFMIN ) ) THEN SVA( q ) = DZNRM2( M, A( 1, q ), 1 ) ELSE T = ZERO AAQQ = ONE CALL ZLASSQ( M, A( 1, q ), 1, T, \$ AAQQ ) SVA( q ) = T*SQRT( AAQQ ) END IF END IF IF( ( AAPP / AAPP0 ).LE.ROOTEPS ) THEN IF( ( AAPP.LT.ROOTBIG ) .AND. \$ ( AAPP.GT.ROOTSFMIN ) ) THEN AAPP = DZNRM2( M, A( 1, p ), 1 ) ELSE T = ZERO AAPP = ONE CALL ZLASSQ( M, A( 1, p ), 1, T, \$ AAPP ) AAPP = T*SQRT( AAPP ) END IF SVA( p ) = AAPP END IF * ELSE * A(:,p) and A(:,q) already numerically orthogonal IF( ir1.EQ.0 )NOTROT = NOTROT + 1 *[RTD] SKIPPED = SKIPPED + 1 PSKIPPED = PSKIPPED + 1 END IF ELSE * A(:,q) is zero column IF( ir1.EQ.0 )NOTROT = NOTROT + 1 PSKIPPED = PSKIPPED + 1 END IF * IF( ( i.LE.SWBAND ) .AND. \$ ( PSKIPPED.GT.ROWSKIP ) ) THEN IF( ir1.EQ.0 )AAPP = -AAPP NOTROT = 0 GO TO 2103 END IF * 2002 CONTINUE * END q-LOOP * 2103 CONTINUE * bailed out of q-loop * SVA( p ) = AAPP * ELSE SVA( p ) = AAPP IF( ( ir1.EQ.0 ) .AND. ( AAPP.EQ.ZERO ) ) \$ NOTROT = NOTROT + MIN( igl+KBL-1, N ) - p END IF * 2001 CONTINUE * end of the p-loop * end of doing the block ( ibr, ibr ) 1002 CONTINUE * end of ir1-loop * * ... go to the off diagonal blocks * igl = ( ibr-1 )*KBL + 1 * DO 2010 jbc = ibr + 1, NBL * jgl = ( jbc-1 )*KBL + 1 * * doing the block at ( ibr, jbc ) * IJBLSK = 0 DO 2100 p = igl, MIN( igl+KBL-1, N ) * AAPP = SVA( p ) IF( AAPP.GT.ZERO ) THEN * PSKIPPED = 0 * DO 2200 q = jgl, MIN( jgl+KBL-1, N ) * AAQQ = SVA( q ) IF( AAQQ.GT.ZERO ) THEN AAPP0 = AAPP * * .. M x 2 Jacobi SVD .. * * Safe Gram matrix computation * IF( AAQQ.GE.ONE ) THEN IF( AAPP.GE.AAQQ ) THEN ROTOK = ( SMALL*AAPP ).LE.AAQQ ELSE ROTOK = ( SMALL*AAQQ ).LE.AAPP END IF IF( AAPP.LT.( BIG / AAQQ ) ) THEN AAPQ = ( ZDOTC( M, A( 1, p ), 1, \$ A( 1, q ), 1 ) / AAQQ ) / AAPP ELSE CALL ZCOPY( M, A( 1, p ), 1, \$ CWORK(N+1), 1 ) CALL ZLASCL( 'G', 0, 0, AAPP, \$ ONE, M, 1, \$ CWORK(N+1), LDA, IERR ) AAPQ = ZDOTC( M, CWORK(N+1), 1, \$ A( 1, q ), 1 ) / AAQQ END IF ELSE IF( AAPP.GE.AAQQ ) THEN ROTOK = AAPP.LE.( AAQQ / SMALL ) ELSE ROTOK = AAQQ.LE.( AAPP / SMALL ) END IF IF( AAPP.GT.( SMALL / AAQQ ) ) THEN AAPQ = ( ZDOTC( M, A( 1, p ), 1, \$ A( 1, q ), 1 ) / MAX(AAQQ,AAPP) ) \$ / MIN(AAQQ,AAPP) ELSE CALL ZCOPY( M, A( 1, q ), 1, \$ CWORK(N+1), 1 ) CALL ZLASCL( 'G', 0, 0, AAQQ, \$ ONE, M, 1, \$ CWORK(N+1), LDA, IERR ) AAPQ = ZDOTC( M, A( 1, p ), 1, \$ CWORK(N+1), 1 ) / AAPP END IF END IF * * AAPQ = AAPQ * CONJG(CWORK(p))*CWORK(q) AAPQ1 = -ABS(AAPQ) MXAAPQ = MAX( MXAAPQ, -AAPQ1 ) * * TO rotate or NOT to rotate, THAT is the question ... * IF( ABS( AAPQ1 ).GT.TOL ) THEN OMPQ = AAPQ / ABS(AAPQ) NOTROT = 0 *[RTD] ROTATED = ROTATED + 1 PSKIPPED = 0 ISWROT = ISWROT + 1 * IF( ROTOK ) THEN * AQOAP = AAQQ / AAPP APOAQ = AAPP / AAQQ THETA = -HALF*ABS( AQOAP-APOAQ )/ AAPQ1 IF( AAQQ.GT.AAPP0 )THETA = -THETA * IF( ABS( THETA ).GT.BIGTHETA ) THEN T = HALF / THETA CS = ONE CALL ZROT( M, A(1,p), 1, A(1,q), 1, \$ CS, CONJG(OMPQ)*T ) IF( RSVEC ) THEN CALL ZROT( MVL, V(1,p), 1, \$ V(1,q), 1, CS, CONJG(OMPQ)*T ) END IF SVA( q ) = AAQQ*SQRT( MAX( ZERO, \$ ONE+T*APOAQ*AAPQ1 ) ) AAPP = AAPP*SQRT( MAX( ZERO, \$ ONE-T*AQOAP*AAPQ1 ) ) MXSINJ = MAX( MXSINJ, ABS( T ) ) ELSE * * .. choose correct signum for THETA and rotate * THSIGN = -SIGN( ONE, AAPQ1 ) IF( AAQQ.GT.AAPP0 )THSIGN = -THSIGN T = ONE / ( THETA+THSIGN* \$ SQRT( ONE+THETA*THETA ) ) CS = SQRT( ONE / ( ONE+T*T ) ) SN = T*CS MXSINJ = MAX( MXSINJ, ABS( SN ) ) SVA( q ) = AAQQ*SQRT( MAX( ZERO, \$ ONE+T*APOAQ*AAPQ1 ) ) AAPP = AAPP*SQRT( MAX( ZERO, \$ ONE-T*AQOAP*AAPQ1 ) ) * CALL ZROT( M, A(1,p), 1, A(1,q), 1, \$ CS, CONJG(OMPQ)*SN ) IF( RSVEC ) THEN CALL ZROT( MVL, V(1,p), 1, \$ V(1,q), 1, CS, CONJG(OMPQ)*SN ) END IF END IF CWORK(p) = -CWORK(q) * OMPQ * ELSE * .. have to use modified Gram-Schmidt like transformation IF( AAPP.GT.AAQQ ) THEN CALL ZCOPY( M, A( 1, p ), 1, \$ CWORK(N+1), 1 ) CALL ZLASCL( 'G', 0, 0, AAPP, ONE, \$ M, 1, CWORK(N+1),LDA, \$ IERR ) CALL ZLASCL( 'G', 0, 0, AAQQ, ONE, \$ M, 1, A( 1, q ), LDA, \$ IERR ) CALL ZAXPY( M, -AAPQ, CWORK(N+1), \$ 1, A( 1, q ), 1 ) CALL ZLASCL( 'G', 0, 0, ONE, AAQQ, \$ M, 1, A( 1, q ), LDA, \$ IERR ) SVA( q ) = AAQQ*SQRT( MAX( ZERO, \$ ONE-AAPQ1*AAPQ1 ) ) MXSINJ = MAX( MXSINJ, SFMIN ) ELSE CALL ZCOPY( M, A( 1, q ), 1, \$ CWORK(N+1), 1 ) CALL ZLASCL( 'G', 0, 0, AAQQ, ONE, \$ M, 1, CWORK(N+1),LDA, \$ IERR ) CALL ZLASCL( 'G', 0, 0, AAPP, ONE, \$ M, 1, A( 1, p ), LDA, \$ IERR ) CALL ZAXPY( M, -CONJG(AAPQ), \$ CWORK(N+1), 1, A( 1, p ), 1 ) CALL ZLASCL( 'G', 0, 0, ONE, AAPP, \$ M, 1, A( 1, p ), LDA, \$ IERR ) SVA( p ) = AAPP*SQRT( MAX( ZERO, \$ ONE-AAPQ1*AAPQ1 ) ) MXSINJ = MAX( MXSINJ, SFMIN ) END IF END IF * END IF ROTOK THEN ... ELSE * * In the case of cancellation in updating SVA(q), SVA(p) * .. recompute SVA(q), SVA(p) IF( ( SVA( q ) / AAQQ )**2.LE.ROOTEPS ) \$ THEN IF( ( AAQQ.LT.ROOTBIG ) .AND. \$ ( AAQQ.GT.ROOTSFMIN ) ) THEN SVA( q ) = DZNRM2( M, A( 1, q ), 1) ELSE T = ZERO AAQQ = ONE CALL ZLASSQ( M, A( 1, q ), 1, T, \$ AAQQ ) SVA( q ) = T*SQRT( AAQQ ) END IF END IF IF( ( AAPP / AAPP0 )**2.LE.ROOTEPS ) THEN IF( ( AAPP.LT.ROOTBIG ) .AND. \$ ( AAPP.GT.ROOTSFMIN ) ) THEN AAPP = DZNRM2( M, A( 1, p ), 1 ) ELSE T = ZERO AAPP = ONE CALL ZLASSQ( M, A( 1, p ), 1, T, \$ AAPP ) AAPP = T*SQRT( AAPP ) END IF SVA( p ) = AAPP END IF * end of OK rotation ELSE NOTROT = NOTROT + 1 *[RTD] SKIPPED = SKIPPED + 1 PSKIPPED = PSKIPPED + 1 IJBLSK = IJBLSK + 1 END IF ELSE NOTROT = NOTROT + 1 PSKIPPED = PSKIPPED + 1 IJBLSK = IJBLSK + 1 END IF * IF( ( i.LE.SWBAND ) .AND. ( IJBLSK.GE.BLSKIP ) ) \$ THEN SVA( p ) = AAPP NOTROT = 0 GO TO 2011 END IF IF( ( i.LE.SWBAND ) .AND. \$ ( PSKIPPED.GT.ROWSKIP ) ) THEN AAPP = -AAPP NOTROT = 0 GO TO 2203 END IF * 2200 CONTINUE * end of the q-loop 2203 CONTINUE * SVA( p ) = AAPP * ELSE * IF( AAPP.EQ.ZERO )NOTROT = NOTROT + \$ MIN( jgl+KBL-1, N ) - jgl + 1 IF( AAPP.LT.ZERO )NOTROT = 0 * END IF * 2100 CONTINUE * end of the p-loop 2010 CONTINUE * end of the jbc-loop 2011 CONTINUE *2011 bailed out of the jbc-loop DO 2012 p = igl, MIN( igl+KBL-1, N ) SVA( p ) = ABS( SVA( p ) ) 2012 CONTINUE *** 2000 CONTINUE *2000 :: end of the ibr-loop * * .. update SVA(N) IF( ( SVA( N ).LT.ROOTBIG ) .AND. ( SVA( N ).GT.ROOTSFMIN ) ) \$ THEN SVA( N ) = DZNRM2( M, A( 1, N ), 1 ) ELSE T = ZERO AAPP = ONE CALL ZLASSQ( M, A( 1, N ), 1, T, AAPP ) SVA( N ) = T*SQRT( AAPP ) END IF * * Additional steering devices * IF( ( i.LT.SWBAND ) .AND. ( ( MXAAPQ.LE.ROOTTOL ) .OR. \$ ( ISWROT.LE.N ) ) )SWBAND = i * IF( ( i.GT.SWBAND+1 ) .AND. ( MXAAPQ.LT.SQRT( DBLE( N ) )* \$ TOL ) .AND. ( DBLE( N )*MXAAPQ*MXSINJ.LT.TOL ) ) THEN GO TO 1994 END IF * IF( NOTROT.GE.EMPTSW )GO TO 1994 * 1993 CONTINUE * end i=1:NSWEEP loop * * #:( Reaching this point means that the procedure has not converged. INFO = NSWEEP - 1 GO TO 1995 * 1994 CONTINUE * #:) Reaching this point means numerical convergence after the i-th * sweep. * INFO = 0 * #:) INFO = 0 confirms successful iterations. 1995 CONTINUE * * Sort the singular values and find how many are above * the underflow threshold. * N2 = 0 N4 = 0 DO 5991 p = 1, N - 1 q = IDAMAX( N-p+1, SVA( p ), 1 ) + p - 1 IF( p.NE.q ) THEN TEMP1 = SVA( p ) SVA( p ) = SVA( q ) SVA( q ) = TEMP1 CALL ZSWAP( M, A( 1, p ), 1, A( 1, q ), 1 ) IF( RSVEC )CALL ZSWAP( MVL, V( 1, p ), 1, V( 1, q ), 1 ) END IF IF( SVA( p ).NE.ZERO ) THEN N4 = N4 + 1 IF( SVA( p )*SKL.GT.SFMIN )N2 = N2 + 1 END IF 5991 CONTINUE IF( SVA( N ).NE.ZERO ) THEN N4 = N4 + 1 IF( SVA( N )*SKL.GT.SFMIN )N2 = N2 + 1 END IF * * Normalize the left singular vectors. * IF( LSVEC .OR. UCTOL ) THEN DO 1998 p = 1, N4 * CALL ZDSCAL( M, ONE / SVA( p ), A( 1, p ), 1 ) CALL ZLASCL( 'G',0,0, SVA(p), ONE, M, 1, A(1,p), M, IERR ) 1998 CONTINUE END IF * * Scale the product of Jacobi rotations. * IF( RSVEC ) THEN DO 2399 p = 1, N TEMP1 = ONE / DZNRM2( MVL, V( 1, p ), 1 ) CALL ZDSCAL( MVL, TEMP1, V( 1, p ), 1 ) 2399 CONTINUE END IF * * Undo scaling, if necessary (and possible). IF( ( ( SKL.GT.ONE ) .AND. ( SVA( 1 ).LT.( BIG / SKL ) ) ) \$ .OR. ( ( SKL.LT.ONE ) .AND. ( SVA( MAX( N2, 1 ) ) .GT. \$ ( SFMIN / SKL ) ) ) ) THEN DO 2400 p = 1, N SVA( p ) = SKL*SVA( p ) 2400 CONTINUE SKL = ONE END IF * RWORK( 1 ) = SKL * The singular values of A are SKL*SVA(1:N). If SKL.NE.ONE * then some of the singular values may overflow or underflow and * the spectrum is given in this factored representation. * RWORK( 2 ) = DBLE( N4 ) * N4 is the number of computed nonzero singular values of A. * RWORK( 3 ) = DBLE( N2 ) * N2 is the number of singular values of A greater than SFMIN. * If N2