|
- *> \brief \b DGESVJ
- *
- * =========== DOCUMENTATION ===========
- *
- * Online html documentation available at
- * http://www.netlib.org/lapack/explore-html/
- *
- *> \htmlonly
- *> Download DGESVJ + dependencies
- *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dgesvj.f">
- *> [TGZ]</a>
- *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dgesvj.f">
- *> [ZIP]</a>
- *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dgesvj.f">
- *> [TXT]</a>
- *> \endhtmlonly
- *
- * Definition:
- * ===========
- *
- * SUBROUTINE DGESVJ( JOBA, JOBU, JOBV, M, N, A, LDA, SVA, MV, V,
- * LDV, WORK, LWORK, INFO )
- *
- * .. Scalar Arguments ..
- * INTEGER INFO, LDA, LDV, LWORK, M, MV, N
- * CHARACTER*1 JOBA, JOBU, JOBV
- * ..
- * .. Array Arguments ..
- * DOUBLE PRECISION A( LDA, * ), SVA( N ), V( LDV, * ),
- * $ WORK( LWORK )
- * ..
- *
- *
- *> \par Purpose:
- * =============
- *>
- *> \verbatim
- *>
- *> DGESVJ computes the singular value decomposition (SVD) of a real
- *> M-by-N matrix A, where M >= N. The SVD of A is written as
- *> [++] [xx] [x0] [xx]
- *> A = U * SIGMA * V^t, [++] = [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 orthogonal 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.
- *> DGESVJ can sometimes compute tiny singular values and their singular vectors much
- *> more accurately than other SVD routines, see below under Further Details.
- *> \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': 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=DSQRT(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': 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 DOUBLE PRECISION array, dimension (LDA,N)
- *> On entry, the M-by-N matrix A.
- *> On exit :
- *> If JOBU = 'U' .OR. JOBU = 'C' :
- *> If INFO = 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 WORK as RANKA=NINT(WORK(2)). Also see the
- *> descriptions of SVA and WORK. The computed columns of U
- *> are mutually numerically orthogonal up to approximately
- *> TOL=DSQRT(M)*EPS (default); or TOL=CTOL*EPS (JOBU = 'C'),
- *> see the description of JOBU.
- *> If INFO > 0 :
- *> the procedure DGESVJ 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^T||_2 / ||A||_2 is small.
- *>
- *> If JOBU = 'N' :
- *> If INFO = 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 > 0 :
- *> the procedure DGESVJ 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 = 0 :
- *> depending on the value SCALE = WORK(1), we have:
- *> If SCALE = 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 > 0 :
- *> the procedure DGESVJ 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 = 'A', then the product of Jacobi rotations in DGESVJ
- *> is applied to the first MV rows of V. See the description of JOBV.
- *> \endverbatim
- *>
- *> \param[in,out] V
- *> \verbatim
- *> V is DOUBLE PRECISION 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 >= 1.
- *> If JOBV = 'V', then LDV >= max(1,N).
- *> If JOBV = 'A', then LDV >= max(1,MV) .
- *> \endverbatim
- *>
- *> \param[in,out] WORK
- *> \verbatim
- *> WORK is DOUBLE PRECISION array, dimension (LWORK)
- *> On entry :
- *> If JOBU = 'C' :
- *> WORK(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 EPS.
- *> On exit :
- *> WORK(1) = SCALE is the scaling factor such that SCALE*SVA(1:N)
- *> are the computed singular values of A.
- *> (See description of SVA().)
- *> WORK(2) = NINT(WORK(2)) is the number of the computed nonzero
- *> singular values.
- *> WORK(3) = NINT(WORK(3)) is the number of the computed singular
- *> values that are larger than the underflow threshold.
- *> WORK(4) = NINT(WORK(4)) is the number of sweeps of Jacobi
- *> rotations needed for numerical convergence.
- *> WORK(5) = max_{i.NE.j} |COS(A(:,i),A(:,j))| in the last sweep.
- *> This is useful information in cases when DGESVJ did
- *> not converge, as it can be used to estimate whether
- *> the output is still useful and for post festum analysis.
- *> WORK(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.
- *> \endverbatim
- *>
- *> \param[in] LWORK
- *> \verbatim
- *> LWORK is INTEGER
- *> length of WORK, WORK >= MAX(6,M+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: DGESVJ did not converge in the maximal allowed number (30)
- *> of sweeps. The output may still be useful. See the
- *> description of WORK.
- *> \endverbatim
- *
- * Authors:
- * ========
- *
- *> \author Univ. of Tennessee
- *> \author Univ. of California Berkeley
- *> \author Univ. of Colorado Denver
- *> \author NAG Ltd.
- *
- *> \date June 2017
- *
- *> \ingroup doubleGEcomputational
- *
- *> \par Further Details:
- * =====================
- *>
- *> \verbatim
- *>
- *> The orthogonal N-by-N matrix V is obtained as a product of Jacobi plane
- *> rotations. The rotations are implemented as fast scaled rotations of
- *> Anda and Park [1]. In the case of underflow of the Jacobi angle, a
- *> modified Jacobi transformation of Drmac [4] is used. Pivot strategy uses
- *> column interchanges of de Rijk [2]. 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 [3].
- *> 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 [5,6], and it is the kernel routine in the SIGMA library [7].
- *> 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 Contributors:
- * ==================
- *>
- *> \verbatim
- *>
- *> ============
- *>
- *> Zlatko Drmac (Zagreb, Croatia) and Kresimir Veselic (Hagen, Germany)
- *> \endverbatim
- *
- *> \par References:
- * ================
- *>
- *> \verbatim
- *>
- *> [1] A. A. Anda and H. Park: Fast plane rotations with dynamic scaling.
- *> SIAM J. matrix Anal. Appl., Vol. 15 (1994), pp. 162-174.
- *> [2] 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.
- *> [3] J. Demmel and K. Veselic: Jacobi method is more accurate than QR.
- *> [4] 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.
- *> [5] 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.
- *> [6] 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.
- *> [7] Z. Drmac: SIGMA - mathematical software library for accurate SVD, PSV,
- *> QSVD, (H,K)-SVD computations.
- *> Department of Mathematics, University of Zagreb, 2008.
- *> \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 DGESVJ( JOBA, JOBU, JOBV, M, N, A, LDA, SVA, MV, V,
- $ LDV, WORK, LWORK, 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 2017
- *
- * .. Scalar Arguments ..
- INTEGER INFO, LDA, LDV, LWORK, M, MV, N
- CHARACTER*1 JOBA, JOBU, JOBV
- * ..
- * .. Array Arguments ..
- DOUBLE PRECISION A( LDA, * ), SVA( N ), V( LDV, * ),
- $ WORK( LWORK )
- * ..
- *
- * =====================================================================
- *
- * .. Local Parameters ..
- DOUBLE PRECISION ZERO, HALF, ONE
- PARAMETER ( ZERO = 0.0D0, HALF = 0.5D0, ONE = 1.0D0)
- INTEGER NSWEEP
- PARAMETER ( NSWEEP = 30 )
- * ..
- * .. Local Scalars ..
- DOUBLE PRECISION AAPP, AAPP0, AAPQ, AAQQ, APOAQ, AQOAP, BIG,
- $ BIGTHETA, CS, CTOL, EPSLN, LARGE, 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, LSVEC, NOSCALE, ROTOK,
- $ RSVEC, UCTOL, UPPER
- * ..
- * .. Local Arrays ..
- DOUBLE PRECISION FASTR( 5 )
- * ..
- * .. Intrinsic Functions ..
- INTRINSIC DABS, MAX, MIN, DBLE, DSIGN, DSQRT
- * ..
- * .. External Functions ..
- * ..
- * from BLAS
- DOUBLE PRECISION DDOT, DNRM2
- EXTERNAL DDOT, DNRM2
- INTEGER IDAMAX
- EXTERNAL IDAMAX
- * from LAPACK
- DOUBLE PRECISION DLAMCH
- EXTERNAL DLAMCH
- LOGICAL LSAME
- EXTERNAL LSAME
- * ..
- * .. External Subroutines ..
- * ..
- * from BLAS
- EXTERNAL DAXPY, DCOPY, DROTM, DSCAL, DSWAP
- * from LAPACK
- EXTERNAL DLASCL, DLASET, DLASSQ, XERBLA
- *
- EXTERNAL DGSVJ0, DGSVJ1
- * ..
- * .. Executable Statements ..
- *
- * Test the input arguments
- *
- LSVEC = LSAME( JOBU, 'U' )
- UCTOL = LSAME( JOBU, 'C' )
- RSVEC = LSAME( JOBV, 'V' )
- APPLV = LSAME( JOBV, 'A' )
- UPPER = LSAME( JOBA, 'U' )
- LOWER = LSAME( JOBA, 'L' )
- *
- 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. ( WORK( 1 ).LE.ONE ) ) THEN
- INFO = -12
- ELSE IF( LWORK.LT.MAX( M+N, 6 ) ) THEN
- INFO = -13
- ELSE
- INFO = 0
- END IF
- *
- * #:(
- IF( INFO.NE.0 ) THEN
- CALL XERBLA( 'DGESVJ', -INFO )
- 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)^T * 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 = WORK( 1 )
- ELSE
- * ... default
- IF( LSVEC .OR. RSVEC .OR. APPLV ) THEN
- CTOL = DSQRT( DBLE( M ) )
- ELSE
- CTOL = DBLE( M )
- END IF
- END IF
- * ... and the machine dependent parameters are
- *[!] (Make sure that DLAMCH() works properly on the target machine.)
- *
- EPSLN = DLAMCH( 'Epsilon' )
- ROOTEPS = DSQRT( EPSLN )
- SFMIN = DLAMCH( 'SafeMinimum' )
- ROOTSFMIN = DSQRT( SFMIN )
- SMALL = SFMIN / EPSLN
- BIG = DLAMCH( 'Overflow' )
- * BIG = ONE / SFMIN
- ROOTBIG = ONE / ROOTSFMIN
- LARGE = BIG / DSQRT( DBLE( M*N ) )
- BIGTHETA = ONE / ROOTEPS
- *
- TOL = CTOL*EPSLN
- ROOTTOL = DSQRT( TOL )
- *
- IF( DBLE( M )*EPSLN.GE.ONE ) THEN
- INFO = -4
- CALL XERBLA( 'DGESVJ', -INFO )
- RETURN
- END IF
- *
- * Initialize the right singular vector matrix.
- *
- IF( RSVEC ) THEN
- MVL = N
- CALL DLASET( 'A', MVL, N, ZERO, ONE, 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
- * DSQRT(N)*max_i SVA(i) does not overflow. If INFinite entries
- * in A are detected, the procedure returns with INFO=-6.
- *
- SKL= ONE / DSQRT( 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 DLASSQ( M-p+1, A( p, p ), 1, AAPP, AAQQ )
- IF( AAPP.GT.BIG ) THEN
- INFO = -6
- CALL XERBLA( 'DGESVJ', -INFO )
- RETURN
- END IF
- AAQQ = DSQRT( 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 DLASSQ( p, A( 1, p ), 1, AAPP, AAQQ )
- IF( AAPP.GT.BIG ) THEN
- INFO = -6
- CALL XERBLA( 'DGESVJ', -INFO )
- RETURN
- END IF
- AAQQ = DSQRT( 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 DLASSQ( M, A( 1, p ), 1, AAPP, AAQQ )
- IF( AAPP.GT.BIG ) THEN
- INFO = -6
- CALL XERBLA( 'DGESVJ', -INFO )
- RETURN
- END IF
- AAQQ = DSQRT( 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 DLASET( 'G', M, N, ZERO, ONE, A, LDA )
- WORK( 1 ) = ONE
- WORK( 2 ) = ZERO
- WORK( 3 ) = ZERO
- WORK( 4 ) = ZERO
- WORK( 5 ) = ZERO
- WORK( 6 ) = ZERO
- RETURN
- END IF
- *
- * #:) Quick return for one-column matrix
- *
- IF( N.EQ.1 ) THEN
- IF( LSVEC )CALL DLASCL( 'G', 0, 0, SVA( 1 ), SKL, M, 1,
- $ A( 1, 1 ), LDA, IERR )
- WORK( 1 ) = ONE / SKL
- IF( SVA( 1 ).GE.SFMIN ) THEN
- WORK( 2 ) = ONE
- ELSE
- WORK( 2 ) = ZERO
- END IF
- WORK( 3 ) = ZERO
- WORK( 4 ) = ZERO
- WORK( 5 ) = ZERO
- WORK( 6 ) = ZERO
- RETURN
- END IF
- *
- * Protect small singular values from underflow, and try to
- * avoid underflows/overflows in computing Jacobi rotations.
- *
- SN = DSQRT( SFMIN / EPSLN )
- TEMP1 = DSQRT( 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*DSQRT( 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 / ( DSQRT( 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 DLASCL( 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
- FASTR( 1 ) = ZERO
- *
- * A is represented in factored form A = A * diag(WORK), where diag(WORK)
- * is initialized to identity. WORK is updated during fast scaled
- * rotations.
- *
- DO 1868 q = 1, N
- WORK( q ) = ONE
- 1868 CONTINUE
- *
- *
- SWBAND = 3
- *[TP] SWBAND is a tuning parameter [TP]. It is meaningful and effective
- * if DGESVJ is used as a computational routine in the preconditioned
- * Jacobi SVD algorithm DGESVJ. 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 DGSVJ0( JOBV, M-N34, N-N34, A( N34+1, N34+1 ), LDA,
- $ WORK( N34+1 ), SVA( N34+1 ), MVL,
- $ V( N34*q+1, N34+1 ), LDV, EPSLN, SFMIN, TOL,
- $ 2, WORK( N+1 ), LWORK-N, IERR )
- *
- CALL DGSVJ0( JOBV, M-N2, N34-N2, A( N2+1, N2+1 ), LDA,
- $ WORK( N2+1 ), SVA( N2+1 ), MVL,
- $ V( N2*q+1, N2+1 ), LDV, EPSLN, SFMIN, TOL, 2,
- $ WORK( N+1 ), LWORK-N, IERR )
- *
- CALL DGSVJ1( JOBV, M-N2, N-N2, N4, A( N2+1, N2+1 ), LDA,
- $ WORK( N2+1 ), SVA( N2+1 ), MVL,
- $ V( N2*q+1, N2+1 ), LDV, EPSLN, SFMIN, TOL, 1,
- $ WORK( N+1 ), LWORK-N, IERR )
- *
- CALL DGSVJ0( JOBV, M-N4, N2-N4, A( N4+1, N4+1 ), LDA,
- $ WORK( N4+1 ), SVA( N4+1 ), MVL,
- $ V( N4*q+1, N4+1 ), LDV, EPSLN, SFMIN, TOL, 1,
- $ WORK( N+1 ), LWORK-N, IERR )
- *
- CALL DGSVJ0( JOBV, M, N4, A, LDA, WORK, SVA, MVL, V, LDV,
- $ EPSLN, SFMIN, TOL, 1, WORK( N+1 ), LWORK-N,
- $ IERR )
- *
- CALL DGSVJ1( JOBV, M, N2, N4, A, LDA, WORK, SVA, MVL, V,
- $ LDV, EPSLN, SFMIN, TOL, 1, WORK( N+1 ),
- $ LWORK-N, IERR )
- *
- *
- ELSE IF( UPPER ) THEN
- *
- *
- CALL DGSVJ0( JOBV, N4, N4, A, LDA, WORK, SVA, MVL, V, LDV,
- $ EPSLN, SFMIN, TOL, 2, WORK( N+1 ), LWORK-N,
- $ IERR )
- *
- CALL DGSVJ0( JOBV, N2, N4, A( 1, N4+1 ), LDA, WORK( N4+1 ),
- $ SVA( N4+1 ), MVL, V( N4*q+1, N4+1 ), LDV,
- $ EPSLN, SFMIN, TOL, 1, WORK( N+1 ), LWORK-N,
- $ IERR )
- *
- CALL DGSVJ1( JOBV, N2, N2, N4, A, LDA, WORK, SVA, MVL, V,
- $ LDV, EPSLN, SFMIN, TOL, 1, WORK( N+1 ),
- $ LWORK-N, IERR )
- *
- CALL DGSVJ0( JOBV, N2+N4, N4, A( 1, N2+1 ), LDA,
- $ WORK( N2+1 ), SVA( N2+1 ), MVL,
- $ V( N2*q+1, N2+1 ), LDV, EPSLN, SFMIN, TOL, 1,
- $ WORK( 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 DSWAP( M, A( 1, p ), 1, A( 1, q ), 1 )
- IF( RSVEC )CALL DSWAP( MVL, V( 1, p ), 1,
- $ V( 1, q ), 1 )
- TEMP1 = SVA( p )
- SVA( p ) = SVA( q )
- SVA( q ) = TEMP1
- TEMP1 = WORK( p )
- WORK( p ) = WORK( q )
- WORK( q ) = TEMP1
- END IF
- *
- IF( ir1.EQ.0 ) THEN
- *
- * Column norms are periodically updated by explicit
- * norm computation.
- * Caveat:
- * Unfortunately, some BLAS implementations compute DNRM2(M,A(1,p),1)
- * as DSQRT(DDOT(M,A(1,p),1,A(1,p),1)), which may cause the result to
- * overflow for ||A(:,p)||_2 > DSQRT(overflow_threshold), and to
- * underflow for ||A(:,p)||_2 < DSQRT(underflow_threshold).
- * Hence, DNRM2 cannot be trusted, not even in the case when
- * the true norm is far from the under(over)flow boundaries.
- * If properly implemented DNRM2 is available, the IF-THEN-ELSE
- * below should read "AAPP = DNRM2( M, A(1,p), 1 ) * WORK(p)".
- *
- IF( ( SVA( p ).LT.ROOTBIG ) .AND.
- $ ( SVA( p ).GT.ROOTSFMIN ) ) THEN
- SVA( p ) = DNRM2( M, A( 1, p ), 1 )*WORK( p )
- ELSE
- TEMP1 = ZERO
- AAPP = ONE
- CALL DLASSQ( M, A( 1, p ), 1, TEMP1, AAPP )
- SVA( p ) = TEMP1*DSQRT( AAPP )*WORK( p )
- 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 = ( DDOT( M, A( 1, p ), 1, A( 1,
- $ q ), 1 )*WORK( p )*WORK( q ) /
- $ AAQQ ) / AAPP
- ELSE
- CALL DCOPY( M, A( 1, p ), 1,
- $ WORK( N+1 ), 1 )
- CALL DLASCL( 'G', 0, 0, AAPP,
- $ WORK( p ), M, 1,
- $ WORK( N+1 ), LDA, IERR )
- AAPQ = DDOT( M, WORK( N+1 ), 1,
- $ A( 1, q ), 1 )*WORK( q ) / AAQQ
- END IF
- ELSE
- ROTOK = AAPP.LE.( AAQQ / SMALL )
- IF( AAPP.GT.( SMALL / AAQQ ) ) THEN
- AAPQ = ( DDOT( M, A( 1, p ), 1, A( 1,
- $ q ), 1 )*WORK( p )*WORK( q ) /
- $ AAQQ ) / AAPP
- ELSE
- CALL DCOPY( M, A( 1, q ), 1,
- $ WORK( N+1 ), 1 )
- CALL DLASCL( 'G', 0, 0, AAQQ,
- $ WORK( q ), M, 1,
- $ WORK( N+1 ), LDA, IERR )
- AAPQ = DDOT( M, WORK( N+1 ), 1,
- $ A( 1, p ), 1 )*WORK( p ) / AAPP
- END IF
- END IF
- *
- MXAAPQ = MAX( MXAAPQ, DABS( AAPQ ) )
- *
- * TO rotate or NOT to rotate, THAT is the question ...
- *
- IF( DABS( AAPQ ).GT.TOL ) THEN
- *
- * .. 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*DABS(AQOAP-APOAQ)/AAPQ
- *
- IF( DABS( THETA ).GT.BIGTHETA ) THEN
- *
- T = HALF / THETA
- FASTR( 3 ) = T*WORK( p ) / WORK( q )
- FASTR( 4 ) = -T*WORK( q ) /
- $ WORK( p )
- CALL DROTM( M, A( 1, p ), 1,
- $ A( 1, q ), 1, FASTR )
- IF( RSVEC )CALL DROTM( MVL,
- $ V( 1, p ), 1,
- $ V( 1, q ), 1,
- $ FASTR )
- SVA( q ) = AAQQ*DSQRT( MAX( ZERO,
- $ ONE+T*APOAQ*AAPQ ) )
- AAPP = AAPP*DSQRT( MAX( ZERO,
- $ ONE-T*AQOAP*AAPQ ) )
- MXSINJ = MAX( MXSINJ, DABS( T ) )
- *
- ELSE
- *
- * .. choose correct signum for THETA and rotate
- *
- THSIGN = -DSIGN( ONE, AAPQ )
- T = ONE / ( THETA+THSIGN*
- $ DSQRT( ONE+THETA*THETA ) )
- CS = DSQRT( ONE / ( ONE+T*T ) )
- SN = T*CS
- *
- MXSINJ = MAX( MXSINJ, DABS( SN ) )
- SVA( q ) = AAQQ*DSQRT( MAX( ZERO,
- $ ONE+T*APOAQ*AAPQ ) )
- AAPP = AAPP*DSQRT( MAX( ZERO,
- $ ONE-T*AQOAP*AAPQ ) )
- *
- APOAQ = WORK( p ) / WORK( q )
- AQOAP = WORK( q ) / WORK( p )
- IF( WORK( p ).GE.ONE ) THEN
- IF( WORK( q ).GE.ONE ) THEN
- FASTR( 3 ) = T*APOAQ
- FASTR( 4 ) = -T*AQOAP
- WORK( p ) = WORK( p )*CS
- WORK( q ) = WORK( q )*CS
- CALL DROTM( M, A( 1, p ), 1,
- $ A( 1, q ), 1,
- $ FASTR )
- IF( RSVEC )CALL DROTM( MVL,
- $ V( 1, p ), 1, V( 1, q ),
- $ 1, FASTR )
- ELSE
- CALL DAXPY( M, -T*AQOAP,
- $ A( 1, q ), 1,
- $ A( 1, p ), 1 )
- CALL DAXPY( M, CS*SN*APOAQ,
- $ A( 1, p ), 1,
- $ A( 1, q ), 1 )
- WORK( p ) = WORK( p )*CS
- WORK( q ) = WORK( q ) / CS
- IF( RSVEC ) THEN
- CALL DAXPY( MVL, -T*AQOAP,
- $ V( 1, q ), 1,
- $ V( 1, p ), 1 )
- CALL DAXPY( MVL,
- $ CS*SN*APOAQ,
- $ V( 1, p ), 1,
- $ V( 1, q ), 1 )
- END IF
- END IF
- ELSE
- IF( WORK( q ).GE.ONE ) THEN
- CALL DAXPY( M, T*APOAQ,
- $ A( 1, p ), 1,
- $ A( 1, q ), 1 )
- CALL DAXPY( M, -CS*SN*AQOAP,
- $ A( 1, q ), 1,
- $ A( 1, p ), 1 )
- WORK( p ) = WORK( p ) / CS
- WORK( q ) = WORK( q )*CS
- IF( RSVEC ) THEN
- CALL DAXPY( MVL, T*APOAQ,
- $ V( 1, p ), 1,
- $ V( 1, q ), 1 )
- CALL DAXPY( MVL,
- $ -CS*SN*AQOAP,
- $ V( 1, q ), 1,
- $ V( 1, p ), 1 )
- END IF
- ELSE
- IF( WORK( p ).GE.WORK( q ) )
- $ THEN
- CALL DAXPY( M, -T*AQOAP,
- $ A( 1, q ), 1,
- $ A( 1, p ), 1 )
- CALL DAXPY( M, CS*SN*APOAQ,
- $ A( 1, p ), 1,
- $ A( 1, q ), 1 )
- WORK( p ) = WORK( p )*CS
- WORK( q ) = WORK( q ) / CS
- IF( RSVEC ) THEN
- CALL DAXPY( MVL,
- $ -T*AQOAP,
- $ V( 1, q ), 1,
- $ V( 1, p ), 1 )
- CALL DAXPY( MVL,
- $ CS*SN*APOAQ,
- $ V( 1, p ), 1,
- $ V( 1, q ), 1 )
- END IF
- ELSE
- CALL DAXPY( M, T*APOAQ,
- $ A( 1, p ), 1,
- $ A( 1, q ), 1 )
- CALL DAXPY( M,
- $ -CS*SN*AQOAP,
- $ A( 1, q ), 1,
- $ A( 1, p ), 1 )
- WORK( p ) = WORK( p ) / CS
- WORK( q ) = WORK( q )*CS
- IF( RSVEC ) THEN
- CALL DAXPY( MVL,
- $ T*APOAQ, V( 1, p ),
- $ 1, V( 1, q ), 1 )
- CALL DAXPY( MVL,
- $ -CS*SN*AQOAP,
- $ V( 1, q ), 1,
- $ V( 1, p ), 1 )
- END IF
- END IF
- END IF
- END IF
- END IF
- *
- ELSE
- * .. have to use modified Gram-Schmidt like transformation
- CALL DCOPY( M, A( 1, p ), 1,
- $ WORK( N+1 ), 1 )
- CALL DLASCL( 'G', 0, 0, AAPP, ONE, M,
- $ 1, WORK( N+1 ), LDA,
- $ IERR )
- CALL DLASCL( 'G', 0, 0, AAQQ, ONE, M,
- $ 1, A( 1, q ), LDA, IERR )
- TEMP1 = -AAPQ*WORK( p ) / WORK( q )
- CALL DAXPY( M, TEMP1, WORK( N+1 ), 1,
- $ A( 1, q ), 1 )
- CALL DLASCL( 'G', 0, 0, ONE, AAQQ, M,
- $ 1, A( 1, q ), LDA, IERR )
- SVA( q ) = AAQQ*DSQRT( MAX( ZERO,
- $ ONE-AAPQ*AAPQ ) )
- 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 ) = DNRM2( M, A( 1, q ), 1 )*
- $ WORK( q )
- ELSE
- T = ZERO
- AAQQ = ONE
- CALL DLASSQ( M, A( 1, q ), 1, T,
- $ AAQQ )
- SVA( q ) = T*DSQRT( AAQQ )*WORK( q )
- END IF
- END IF
- IF( ( AAPP / AAPP0 ).LE.ROOTEPS ) THEN
- IF( ( AAPP.LT.ROOTBIG ) .AND.
- $ ( AAPP.GT.ROOTSFMIN ) ) THEN
- AAPP = DNRM2( M, A( 1, p ), 1 )*
- $ WORK( p )
- ELSE
- T = ZERO
- AAPP = ONE
- CALL DLASSQ( M, A( 1, p ), 1, T,
- $ AAPP )
- AAPP = T*DSQRT( AAPP )*WORK( p )
- 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 = ( DDOT( M, A( 1, p ), 1, A( 1,
- $ q ), 1 )*WORK( p )*WORK( q ) /
- $ AAQQ ) / AAPP
- ELSE
- CALL DCOPY( M, A( 1, p ), 1,
- $ WORK( N+1 ), 1 )
- CALL DLASCL( 'G', 0, 0, AAPP,
- $ WORK( p ), M, 1,
- $ WORK( N+1 ), LDA, IERR )
- AAPQ = DDOT( M, WORK( N+1 ), 1,
- $ A( 1, q ), 1 )*WORK( q ) / 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 = ( DDOT( M, A( 1, p ), 1, A( 1,
- $ q ), 1 )*WORK( p )*WORK( q ) /
- $ AAQQ ) / AAPP
- ELSE
- CALL DCOPY( M, A( 1, q ), 1,
- $ WORK( N+1 ), 1 )
- CALL DLASCL( 'G', 0, 0, AAQQ,
- $ WORK( q ), M, 1,
- $ WORK( N+1 ), LDA, IERR )
- AAPQ = DDOT( M, WORK( N+1 ), 1,
- $ A( 1, p ), 1 )*WORK( p ) / AAPP
- END IF
- END IF
- *
- MXAAPQ = MAX( MXAAPQ, DABS( AAPQ ) )
- *
- * TO rotate or NOT to rotate, THAT is the question ...
- *
- IF( DABS( AAPQ ).GT.TOL ) THEN
- NOTROT = 0
- *[RTD] ROTATED = ROTATED + 1
- PSKIPPED = 0
- ISWROT = ISWROT + 1
- *
- IF( ROTOK ) THEN
- *
- AQOAP = AAQQ / AAPP
- APOAQ = AAPP / AAQQ
- THETA = -HALF*DABS(AQOAP-APOAQ)/AAPQ
- IF( AAQQ.GT.AAPP0 )THETA = -THETA
- *
- IF( DABS( THETA ).GT.BIGTHETA ) THEN
- T = HALF / THETA
- FASTR( 3 ) = T*WORK( p ) / WORK( q )
- FASTR( 4 ) = -T*WORK( q ) /
- $ WORK( p )
- CALL DROTM( M, A( 1, p ), 1,
- $ A( 1, q ), 1, FASTR )
- IF( RSVEC )CALL DROTM( MVL,
- $ V( 1, p ), 1,
- $ V( 1, q ), 1,
- $ FASTR )
- SVA( q ) = AAQQ*DSQRT( MAX( ZERO,
- $ ONE+T*APOAQ*AAPQ ) )
- AAPP = AAPP*DSQRT( MAX( ZERO,
- $ ONE-T*AQOAP*AAPQ ) )
- MXSINJ = MAX( MXSINJ, DABS( T ) )
- ELSE
- *
- * .. choose correct signum for THETA and rotate
- *
- THSIGN = -DSIGN( ONE, AAPQ )
- IF( AAQQ.GT.AAPP0 )THSIGN = -THSIGN
- T = ONE / ( THETA+THSIGN*
- $ DSQRT( ONE+THETA*THETA ) )
- CS = DSQRT( ONE / ( ONE+T*T ) )
- SN = T*CS
- MXSINJ = MAX( MXSINJ, DABS( SN ) )
- SVA( q ) = AAQQ*DSQRT( MAX( ZERO,
- $ ONE+T*APOAQ*AAPQ ) )
- AAPP = AAPP*DSQRT( MAX( ZERO,
- $ ONE-T*AQOAP*AAPQ ) )
- *
- APOAQ = WORK( p ) / WORK( q )
- AQOAP = WORK( q ) / WORK( p )
- IF( WORK( p ).GE.ONE ) THEN
- *
- IF( WORK( q ).GE.ONE ) THEN
- FASTR( 3 ) = T*APOAQ
- FASTR( 4 ) = -T*AQOAP
- WORK( p ) = WORK( p )*CS
- WORK( q ) = WORK( q )*CS
- CALL DROTM( M, A( 1, p ), 1,
- $ A( 1, q ), 1,
- $ FASTR )
- IF( RSVEC )CALL DROTM( MVL,
- $ V( 1, p ), 1, V( 1, q ),
- $ 1, FASTR )
- ELSE
- CALL DAXPY( M, -T*AQOAP,
- $ A( 1, q ), 1,
- $ A( 1, p ), 1 )
- CALL DAXPY( M, CS*SN*APOAQ,
- $ A( 1, p ), 1,
- $ A( 1, q ), 1 )
- IF( RSVEC ) THEN
- CALL DAXPY( MVL, -T*AQOAP,
- $ V( 1, q ), 1,
- $ V( 1, p ), 1 )
- CALL DAXPY( MVL,
- $ CS*SN*APOAQ,
- $ V( 1, p ), 1,
- $ V( 1, q ), 1 )
- END IF
- WORK( p ) = WORK( p )*CS
- WORK( q ) = WORK( q ) / CS
- END IF
- ELSE
- IF( WORK( q ).GE.ONE ) THEN
- CALL DAXPY( M, T*APOAQ,
- $ A( 1, p ), 1,
- $ A( 1, q ), 1 )
- CALL DAXPY( M, -CS*SN*AQOAP,
- $ A( 1, q ), 1,
- $ A( 1, p ), 1 )
- IF( RSVEC ) THEN
- CALL DAXPY( MVL, T*APOAQ,
- $ V( 1, p ), 1,
- $ V( 1, q ), 1 )
- CALL DAXPY( MVL,
- $ -CS*SN*AQOAP,
- $ V( 1, q ), 1,
- $ V( 1, p ), 1 )
- END IF
- WORK( p ) = WORK( p ) / CS
- WORK( q ) = WORK( q )*CS
- ELSE
- IF( WORK( p ).GE.WORK( q ) )
- $ THEN
- CALL DAXPY( M, -T*AQOAP,
- $ A( 1, q ), 1,
- $ A( 1, p ), 1 )
- CALL DAXPY( M, CS*SN*APOAQ,
- $ A( 1, p ), 1,
- $ A( 1, q ), 1 )
- WORK( p ) = WORK( p )*CS
- WORK( q ) = WORK( q ) / CS
- IF( RSVEC ) THEN
- CALL DAXPY( MVL,
- $ -T*AQOAP,
- $ V( 1, q ), 1,
- $ V( 1, p ), 1 )
- CALL DAXPY( MVL,
- $ CS*SN*APOAQ,
- $ V( 1, p ), 1,
- $ V( 1, q ), 1 )
- END IF
- ELSE
- CALL DAXPY( M, T*APOAQ,
- $ A( 1, p ), 1,
- $ A( 1, q ), 1 )
- CALL DAXPY( M,
- $ -CS*SN*AQOAP,
- $ A( 1, q ), 1,
- $ A( 1, p ), 1 )
- WORK( p ) = WORK( p ) / CS
- WORK( q ) = WORK( q )*CS
- IF( RSVEC ) THEN
- CALL DAXPY( MVL,
- $ T*APOAQ, V( 1, p ),
- $ 1, V( 1, q ), 1 )
- CALL DAXPY( MVL,
- $ -CS*SN*AQOAP,
- $ V( 1, q ), 1,
- $ V( 1, p ), 1 )
- END IF
- END IF
- END IF
- END IF
- END IF
- *
- ELSE
- IF( AAPP.GT.AAQQ ) THEN
- CALL DCOPY( M, A( 1, p ), 1,
- $ WORK( N+1 ), 1 )
- CALL DLASCL( 'G', 0, 0, AAPP, ONE,
- $ M, 1, WORK( N+1 ), LDA,
- $ IERR )
- CALL DLASCL( 'G', 0, 0, AAQQ, ONE,
- $ M, 1, A( 1, q ), LDA,
- $ IERR )
- TEMP1 = -AAPQ*WORK( p ) / WORK( q )
- CALL DAXPY( M, TEMP1, WORK( N+1 ),
- $ 1, A( 1, q ), 1 )
- CALL DLASCL( 'G', 0, 0, ONE, AAQQ,
- $ M, 1, A( 1, q ), LDA,
- $ IERR )
- SVA( q ) = AAQQ*DSQRT( MAX( ZERO,
- $ ONE-AAPQ*AAPQ ) )
- MXSINJ = MAX( MXSINJ, SFMIN )
- ELSE
- CALL DCOPY( M, A( 1, q ), 1,
- $ WORK( N+1 ), 1 )
- CALL DLASCL( 'G', 0, 0, AAQQ, ONE,
- $ M, 1, WORK( N+1 ), LDA,
- $ IERR )
- CALL DLASCL( 'G', 0, 0, AAPP, ONE,
- $ M, 1, A( 1, p ), LDA,
- $ IERR )
- TEMP1 = -AAPQ*WORK( q ) / WORK( p )
- CALL DAXPY( M, TEMP1, WORK( N+1 ),
- $ 1, A( 1, p ), 1 )
- CALL DLASCL( 'G', 0, 0, ONE, AAPP,
- $ M, 1, A( 1, p ), LDA,
- $ IERR )
- SVA( p ) = AAPP*DSQRT( MAX( ZERO,
- $ ONE-AAPQ*AAPQ ) )
- MXSINJ = MAX( MXSINJ, SFMIN )
- END IF
- END IF
- * END IF ROTOK THEN ... ELSE
- *
- * In the case of cancellation in updating SVA(q)
- * .. recompute SVA(q)
- IF( ( SVA( q ) / AAQQ )**2.LE.ROOTEPS )
- $ THEN
- IF( ( AAQQ.LT.ROOTBIG ) .AND.
- $ ( AAQQ.GT.ROOTSFMIN ) ) THEN
- SVA( q ) = DNRM2( M, A( 1, q ), 1 )*
- $ WORK( q )
- ELSE
- T = ZERO
- AAQQ = ONE
- CALL DLASSQ( M, A( 1, q ), 1, T,
- $ AAQQ )
- SVA( q ) = T*DSQRT( AAQQ )*WORK( q )
- END IF
- END IF
- IF( ( AAPP / AAPP0 )**2.LE.ROOTEPS ) THEN
- IF( ( AAPP.LT.ROOTBIG ) .AND.
- $ ( AAPP.GT.ROOTSFMIN ) ) THEN
- AAPP = DNRM2( M, A( 1, p ), 1 )*
- $ WORK( p )
- ELSE
- T = ZERO
- AAPP = ONE
- CALL DLASSQ( M, A( 1, p ), 1, T,
- $ AAPP )
- AAPP = T*DSQRT( AAPP )*WORK( p )
- 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 ) = DABS( 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 ) = DNRM2( M, A( 1, N ), 1 )*WORK( N )
- ELSE
- T = ZERO
- AAPP = ONE
- CALL DLASSQ( M, A( 1, N ), 1, T, AAPP )
- SVA( N ) = T*DSQRT( AAPP )*WORK( N )
- 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.DSQRT( 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
- TEMP1 = WORK( p )
- WORK( p ) = WORK( q )
- WORK( q ) = TEMP1
- CALL DSWAP( M, A( 1, p ), 1, A( 1, q ), 1 )
- IF( RSVEC )CALL DSWAP( 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, N2
- CALL DSCAL( M, WORK( p ) / SVA( p ), A( 1, p ), 1 )
- 1998 CONTINUE
- END IF
- *
- * Scale the product of Jacobi rotations (assemble the fast rotations).
- *
- IF( RSVEC ) THEN
- IF( APPLV ) THEN
- DO 2398 p = 1, N
- CALL DSCAL( MVL, WORK( p ), V( 1, p ), 1 )
- 2398 CONTINUE
- ELSE
- DO 2399 p = 1, N
- TEMP1 = ONE / DNRM2( MVL, V( 1, p ), 1 )
- CALL DSCAL( MVL, TEMP1, V( 1, p ), 1 )
- 2399 CONTINUE
- END IF
- 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
- *
- WORK( 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.
- *
- WORK( 2 ) = DBLE( N4 )
- * N4 is the number of computed nonzero singular values of A.
- *
- WORK( 3 ) = DBLE( N2 )
- * N2 is the number of singular values of A greater than SFMIN.
- * If N2<N, SVA(N2:N) contains ZEROS and/or denormalized numbers
- * that may carry some information.
- *
- WORK( 4 ) = DBLE( i )
- * i is the index of the last sweep before declaring convergence.
- *
- WORK( 5 ) = MXAAPQ
- * MXAAPQ is the largest absolute value of scaled pivots in the
- * last sweep
- *
- WORK( 6 ) = MXSINJ
- * MXSINJ is the largest absolute value of the sines of Jacobi angles
- * in the last sweep
- *
- RETURN
- * ..
- * .. END OF DGESVJ
- * ..
- END
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