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clarrv.f
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*> \brief \b CLARRV computes the eigenvectors of the tridiagonal matrix T = L D LT given L, D and the eigenvalues of L D LT.
*
* =========== DOCUMENTATION ===========
*
* Online html documentation available at
* http://www.netlib.org/lapack/explore-html/
*
*> \htmlonly
*> Download CLARRV + dependencies
*> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/clarrv.f">
*> [TGZ]</a>
*> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/clarrv.f">
*> [ZIP]</a>
*> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/clarrv.f">
*> [TXT]</a>
*> \endhtmlonly
*
* Definition:
* ===========
*
* SUBROUTINE CLARRV( N, VL, VU, D, L, PIVMIN,
* ISPLIT, M, DOL, DOU, MINRGP,
* RTOL1, RTOL2, W, WERR, WGAP,
* IBLOCK, INDEXW, GERS, Z, LDZ, ISUPPZ,
* WORK, IWORK, INFO )
*
* .. Scalar Arguments ..
* INTEGER DOL, DOU, INFO, LDZ, M, N
* REAL MINRGP, PIVMIN, RTOL1, RTOL2, VL, VU
* ..
* .. Array Arguments ..
* INTEGER IBLOCK( * ), INDEXW( * ), ISPLIT( * ),
* $ ISUPPZ( * ), IWORK( * )
* REAL D( * ), GERS( * ), L( * ), W( * ), WERR( * ),
* $ WGAP( * ), WORK( * )
* COMPLEX Z( LDZ, * )
* ..
*
*
*> \par Purpose:
* =============
*>
*> \verbatim
*>
*> CLARRV computes the eigenvectors of the tridiagonal matrix
*> T = L D L**T given L, D and APPROXIMATIONS to the eigenvalues of L D L**T.
*> The input eigenvalues should have been computed by SLARRE.
*> \endverbatim
*
* Arguments:
* ==========
*
*> \param[in] N
*> \verbatim
*> N is INTEGER
*> The order of the matrix. N >= 0.
*> \endverbatim
*>
*> \param[in] VL
*> \verbatim
*> VL is REAL
*> Lower bound of the interval that contains the desired
*> eigenvalues. VL < VU. Needed to compute gaps on the left or right
*> end of the extremal eigenvalues in the desired RANGE.
*> \endverbatim
*>
*> \param[in] VU
*> \verbatim
*> VU is REAL
*> Upper bound of the interval that contains the desired
*> eigenvalues. VL < VU. Needed to compute gaps on the left or right
*> end of the extremal eigenvalues in the desired RANGE.
*> \endverbatim
*>
*> \param[in,out] D
*> \verbatim
*> D is REAL array, dimension (N)
*> On entry, the N diagonal elements of the diagonal matrix D.
*> On exit, D may be overwritten.
*> \endverbatim
*>
*> \param[in,out] L
*> \verbatim
*> L is REAL array, dimension (N)
*> On entry, the (N-1) subdiagonal elements of the unit
*> bidiagonal matrix L are in elements 1 to N-1 of L
*> (if the matrix is not split.) At the end of each block
*> is stored the corresponding shift as given by SLARRE.
*> On exit, L is overwritten.
*> \endverbatim
*>
*> \param[in] PIVMIN
*> \verbatim
*> PIVMIN is REAL
*> The minimum pivot allowed in the Sturm sequence.
*> \endverbatim
*>
*> \param[in] ISPLIT
*> \verbatim
*> ISPLIT is INTEGER array, dimension (N)
*> The splitting points, at which T breaks up into blocks.
*> The first block consists of rows/columns 1 to
*> ISPLIT( 1 ), the second of rows/columns ISPLIT( 1 )+1
*> through ISPLIT( 2 ), etc.
*> \endverbatim
*>
*> \param[in] M
*> \verbatim
*> M is INTEGER
*> The total number of input eigenvalues. 0 <= M <= N.
*> \endverbatim
*>
*> \param[in] DOL
*> \verbatim
*> DOL is INTEGER
*> \endverbatim
*>
*> \param[in] DOU
*> \verbatim
*> DOU is INTEGER
*> If the user wants to compute only selected eigenvectors from all
*> the eigenvalues supplied, he can specify an index range DOL:DOU.
*> Or else the setting DOL=1, DOU=M should be applied.
*> Note that DOL and DOU refer to the order in which the eigenvalues
*> are stored in W.
*> If the user wants to compute only selected eigenpairs, then
*> the columns DOL-1 to DOU+1 of the eigenvector space Z contain the
*> computed eigenvectors. All other columns of Z are set to zero.
*> \endverbatim
*>
*> \param[in] MINRGP
*> \verbatim
*> MINRGP is REAL
*> \endverbatim
*>
*> \param[in] RTOL1
*> \verbatim
*> RTOL1 is REAL
*> \endverbatim
*>
*> \param[in] RTOL2
*> \verbatim
*> RTOL2 is REAL
*> Parameters for bisection.
*> An interval [LEFT,RIGHT] has converged if
*> RIGHT-LEFT < MAX( RTOL1*GAP, RTOL2*MAX(|LEFT|,|RIGHT|) )
*> \endverbatim
*>
*> \param[in,out] W
*> \verbatim
*> W is REAL array, dimension (N)
*> The first M elements of W contain the APPROXIMATE eigenvalues for
*> which eigenvectors are to be computed. The eigenvalues
*> should be grouped by split-off block and ordered from
*> smallest to largest within the block ( The output array
*> W from SLARRE is expected here ). Furthermore, they are with
*> respect to the shift of the corresponding root representation
*> for their block. On exit, W holds the eigenvalues of the
*> UNshifted matrix.
*> \endverbatim
*>
*> \param[in,out] WERR
*> \verbatim
*> WERR is REAL array, dimension (N)
*> The first M elements contain the semiwidth of the uncertainty
*> interval of the corresponding eigenvalue in W
*> \endverbatim
*>
*> \param[in,out] WGAP
*> \verbatim
*> WGAP is REAL array, dimension (N)
*> The separation from the right neighbor eigenvalue in W.
*> \endverbatim
*>
*> \param[in] IBLOCK
*> \verbatim
*> IBLOCK is INTEGER array, dimension (N)
*> The indices of the blocks (submatrices) associated with the
*> corresponding eigenvalues in W; IBLOCK(i)=1 if eigenvalue
*> W(i) belongs to the first block from the top, =2 if W(i)
*> belongs to the second block, etc.
*> \endverbatim
*>
*> \param[in] INDEXW
*> \verbatim
*> INDEXW is INTEGER array, dimension (N)
*> The indices of the eigenvalues within each block (submatrix);
*> for example, INDEXW(i)= 10 and IBLOCK(i)=2 imply that the
*> i-th eigenvalue W(i) is the 10-th eigenvalue in the second block.
*> \endverbatim
*>
*> \param[in] GERS
*> \verbatim
*> GERS is REAL array, dimension (2*N)
*> The N Gerschgorin intervals (the i-th Gerschgorin interval
*> is (GERS(2*i-1), GERS(2*i)). The Gerschgorin intervals should
*> be computed from the original UNshifted matrix.
*> \endverbatim
*>
*> \param[out] Z
*> \verbatim
*> Z is COMPLEX array, dimension (LDZ, max(1,M) )
*> If INFO = 0, the first M columns of Z contain the
*> orthonormal eigenvectors of the matrix T
*> corresponding to the input eigenvalues, with the i-th
*> column of Z holding the eigenvector associated with W(i).
*> Note: the user must ensure that at least max(1,M) columns are
*> supplied in the array Z.
*> \endverbatim
*>
*> \param[in] LDZ
*> \verbatim
*> LDZ is INTEGER
*> The leading dimension of the array Z. LDZ >= 1, and if
*> JOBZ = 'V', LDZ >= max(1,N).
*> \endverbatim
*>
*> \param[out] ISUPPZ
*> \verbatim
*> ISUPPZ is INTEGER array, dimension ( 2*max(1,M) )
*> The support of the eigenvectors in Z, i.e., the indices
*> indicating the nonzero elements in Z. The I-th eigenvector
*> is nonzero only in elements ISUPPZ( 2*I-1 ) through
*> ISUPPZ( 2*I ).
*> \endverbatim
*>
*> \param[out] WORK
*> \verbatim
*> WORK is REAL array, dimension (12*N)
*> \endverbatim
*>
*> \param[out] IWORK
*> \verbatim
*> IWORK is INTEGER array, dimension (7*N)
*> \endverbatim
*>
*> \param[out] INFO
*> \verbatim
*> INFO is INTEGER
*> = 0: successful exit
*>
*> > 0: A problem occurred in CLARRV.
*> < 0: One of the called subroutines signaled an internal problem.
*> Needs inspection of the corresponding parameter IINFO
*> for further information.
*>
*> =-1: Problem in SLARRB when refining a child's eigenvalues.
*> =-2: Problem in SLARRF when computing the RRR of a child.
*> When a child is inside a tight cluster, it can be difficult
*> to find an RRR. A partial remedy from the user's point of
*> view is to make the parameter MINRGP smaller and recompile.
*> However, as the orthogonality of the computed vectors is
*> proportional to 1/MINRGP, the user should be aware that
*> he might be trading in precision when he decreases MINRGP.
*> =-3: Problem in SLARRB when refining a single eigenvalue
*> after the Rayleigh correction was rejected.
*> = 5: The Rayleigh Quotient Iteration failed to converge to
*> full accuracy in MAXITR steps.
*> \endverbatim
*
* Authors:
* ========
*
*> \author Univ. of Tennessee
*> \author Univ. of California Berkeley
*> \author Univ. of Colorado Denver
*> \author NAG Ltd.
*
*> \ingroup complexOTHERauxiliary
*
*> \par Contributors:
* ==================
*>
*> Beresford Parlett, University of California, Berkeley, USA \n
*> Jim Demmel, University of California, Berkeley, USA \n
*> Inderjit Dhillon, University of Texas, Austin, USA \n
*> Osni Marques, LBNL/NERSC, USA \n
*> Christof Voemel, University of California, Berkeley, USA
*
* =====================================================================
SUBROUTINE CLARRV( N, VL, VU, D, L, PIVMIN,
$ ISPLIT, M, DOL, DOU, MINRGP,
$ RTOL1, RTOL2, W, WERR, WGAP,
$ IBLOCK, INDEXW, GERS, Z, LDZ, ISUPPZ,
$ WORK, IWORK, 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 ..
INTEGER DOL, DOU, INFO, LDZ, M, N
REAL MINRGP, PIVMIN, RTOL1, RTOL2, VL, VU
* ..
* .. Array Arguments ..
INTEGER IBLOCK( * ), INDEXW( * ), ISPLIT( * ),
$ ISUPPZ( * ), IWORK( * )
REAL D( * ), GERS( * ), L( * ), W( * ), WERR( * ),
$ WGAP( * ), WORK( * )
COMPLEX Z( LDZ, * )
* ..
*
* =====================================================================
*
* .. Parameters ..
INTEGER MAXITR
PARAMETER ( MAXITR = 10 )
COMPLEX CZERO
PARAMETER ( CZERO = ( 0.0E0, 0.0E0 ) )
REAL ZERO, ONE, TWO, THREE, FOUR, HALF
PARAMETER ( ZERO = 0.0E0, ONE = 1.0E0,
$ TWO = 2.0E0, THREE = 3.0E0,
$ FOUR = 4.0E0, HALF = 0.5E0)
* ..
* .. Local Scalars ..
LOGICAL ESKIP, NEEDBS, STP2II, TRYRQC, USEDBS, USEDRQ
INTEGER DONE, I, IBEGIN, IDONE, IEND, II, IINDC1,
$ IINDC2, IINDR, IINDWK, IINFO, IM, IN, INDEIG,
$ INDLD, INDLLD, INDWRK, ISUPMN, ISUPMX, ITER,
$ ITMP1, J, JBLK, K, MINIWSIZE, MINWSIZE, NCLUS,
$ NDEPTH, NEGCNT, NEWCLS, NEWFST, NEWFTT, NEWLST,
$ NEWSIZ, OFFSET, OLDCLS, OLDFST, OLDIEN, OLDLST,
$ OLDNCL, P, PARITY, Q, WBEGIN, WEND, WINDEX,
$ WINDMN, WINDPL, ZFROM, ZTO, ZUSEDL, ZUSEDU,
$ ZUSEDW
INTEGER INDIN1, INDIN2
REAL BSTRES, BSTW, EPS, FUDGE, GAP, GAPTOL, GL, GU,
$ LAMBDA, LEFT, LGAP, MINGMA, NRMINV, RESID,
$ RGAP, RIGHT, RQCORR, RQTOL, SAVGAP, SGNDEF,
$ SIGMA, SPDIAM, SSIGMA, TAU, TMP, TOL, ZTZ
* ..
* .. External Functions ..
REAL SLAMCH
EXTERNAL SLAMCH
* ..
* .. External Subroutines ..
EXTERNAL CLAR1V, CLASET, CSSCAL, SCOPY, SLARRB,
$ SLARRF
* ..
* .. Intrinsic Functions ..
INTRINSIC ABS, REAL, MAX, MIN
INTRINSIC CMPLX
* ..
* .. Executable Statements ..
* ..
INFO = 0
*
* Quick return if possible
*
IF( (N.LE.0).OR.(M.LE.0) ) THEN
RETURN
END IF
*
* The first N entries of WORK are reserved for the eigenvalues
INDLD = N+1
INDLLD= 2*N+1
INDIN1 = 3*N + 1
INDIN2 = 4*N + 1
INDWRK = 5*N + 1
MINWSIZE = 12 * N
DO 5 I= 1,MINWSIZE
WORK( I ) = ZERO
5 CONTINUE
* IWORK(IINDR+1:IINDR+N) hold the twist indices R for the
* factorization used to compute the FP vector
IINDR = 0
* IWORK(IINDC1+1:IINC2+N) are used to store the clusters of the current
* layer and the one above.
IINDC1 = N
IINDC2 = 2*N
IINDWK = 3*N + 1
MINIWSIZE = 7 * N
DO 10 I= 1,MINIWSIZE
IWORK( I ) = 0
10 CONTINUE
ZUSEDL = 1
IF(DOL.GT.1) THEN
* Set lower bound for use of Z
ZUSEDL = DOL-1
ENDIF
ZUSEDU = M
IF(DOU.LT.M) THEN
* Set lower bound for use of Z
ZUSEDU = DOU+1
ENDIF
* The width of the part of Z that is used
ZUSEDW = ZUSEDU - ZUSEDL + 1
CALL CLASET( 'Full', N, ZUSEDW, CZERO, CZERO,
$ Z(1,ZUSEDL), LDZ )
EPS = SLAMCH( 'Precision' )
RQTOL = TWO * EPS
*
* Set expert flags for standard code.
TRYRQC = .TRUE.
IF((DOL.EQ.1).AND.(DOU.EQ.M)) THEN
ELSE
* Only selected eigenpairs are computed. Since the other evalues
* are not refined by RQ iteration, bisection has to compute to full
* accuracy.
RTOL1 = FOUR * EPS
RTOL2 = FOUR * EPS
ENDIF
* The entries WBEGIN:WEND in W, WERR, WGAP correspond to the
* desired eigenvalues. The support of the nonzero eigenvector
* entries is contained in the interval IBEGIN:IEND.
* Remark that if k eigenpairs are desired, then the eigenvectors
* are stored in k contiguous columns of Z.
* DONE is the number of eigenvectors already computed
DONE = 0
IBEGIN = 1
WBEGIN = 1
DO 170 JBLK = 1, IBLOCK( M )
IEND = ISPLIT( JBLK )
SIGMA = L( IEND )
* Find the eigenvectors of the submatrix indexed IBEGIN
* through IEND.
WEND = WBEGIN - 1
15 CONTINUE
IF( WEND.LT.M ) THEN
IF( IBLOCK( WEND+1 ).EQ.JBLK ) THEN
WEND = WEND + 1
GO TO 15
END IF
END IF
IF( WEND.LT.WBEGIN ) THEN
IBEGIN = IEND + 1
GO TO 170
ELSEIF( (WEND.LT.DOL).OR.(WBEGIN.GT.DOU) ) THEN
IBEGIN = IEND + 1
WBEGIN = WEND + 1
GO TO 170
END IF
* Find local spectral diameter of the block
GL = GERS( 2*IBEGIN-1 )
GU = GERS( 2*IBEGIN )
DO 20 I = IBEGIN+1 , IEND
GL = MIN( GERS( 2*I-1 ), GL )
GU = MAX( GERS( 2*I ), GU )
20 CONTINUE
SPDIAM = GU - GL
* OLDIEN is the last index of the previous block
OLDIEN = IBEGIN - 1
* Calculate the size of the current block
IN = IEND - IBEGIN + 1
* The number of eigenvalues in the current block
IM = WEND - WBEGIN + 1
* This is for a 1x1 block
IF( IBEGIN.EQ.IEND ) THEN
DONE = DONE+1
Z( IBEGIN, WBEGIN ) = CMPLX( ONE, ZERO )
ISUPPZ( 2*WBEGIN-1 ) = IBEGIN
ISUPPZ( 2*WBEGIN ) = IBEGIN
W( WBEGIN ) = W( WBEGIN ) + SIGMA
WORK( WBEGIN ) = W( WBEGIN )
IBEGIN = IEND + 1
WBEGIN = WBEGIN + 1
GO TO 170
END IF
* The desired (shifted) eigenvalues are stored in W(WBEGIN:WEND)
* Note that these can be approximations, in this case, the corresp.
* entries of WERR give the size of the uncertainty interval.
* The eigenvalue approximations will be refined when necessary as
* high relative accuracy is required for the computation of the
* corresponding eigenvectors.
CALL SCOPY( IM, W( WBEGIN ), 1,
$ WORK( WBEGIN ), 1 )
* We store in W the eigenvalue approximations w.r.t. the original
* matrix T.
DO 30 I=1,IM
W(WBEGIN+I-1) = W(WBEGIN+I-1)+SIGMA
30 CONTINUE
* NDEPTH is the current depth of the representation tree
NDEPTH = 0
* PARITY is either 1 or 0
PARITY = 1
* NCLUS is the number of clusters for the next level of the
* representation tree, we start with NCLUS = 1 for the root
NCLUS = 1
IWORK( IINDC1+1 ) = 1
IWORK( IINDC1+2 ) = IM
* IDONE is the number of eigenvectors already computed in the current
* block
IDONE = 0
* loop while( IDONE.LT.IM )
* generate the representation tree for the current block and
* compute the eigenvectors
40 CONTINUE
IF( IDONE.LT.IM ) THEN
* This is a crude protection against infinitely deep trees
IF( NDEPTH.GT.M ) THEN
INFO = -2
RETURN
ENDIF
* breadth first processing of the current level of the representation
* tree: OLDNCL = number of clusters on current level
OLDNCL = NCLUS
* reset NCLUS to count the number of child clusters
NCLUS = 0
*
PARITY = 1 - PARITY
IF( PARITY.EQ.0 ) THEN
OLDCLS = IINDC1
NEWCLS = IINDC2
ELSE
OLDCLS = IINDC2
NEWCLS = IINDC1
END IF
* Process the clusters on the current level
DO 150 I = 1, OLDNCL
J = OLDCLS + 2*I
* OLDFST, OLDLST = first, last index of current cluster.
* cluster indices start with 1 and are relative
* to WBEGIN when accessing W, WGAP, WERR, Z
OLDFST = IWORK( J-1 )
OLDLST = IWORK( J )
IF( NDEPTH.GT.0 ) THEN
* Retrieve relatively robust representation (RRR) of cluster
* that has been computed at the previous level
* The RRR is stored in Z and overwritten once the eigenvectors
* have been computed or when the cluster is refined
IF((DOL.EQ.1).AND.(DOU.EQ.M)) THEN
* Get representation from location of the leftmost evalue
* of the cluster
J = WBEGIN + OLDFST - 1
ELSE
IF(WBEGIN+OLDFST-1.LT.DOL) THEN
* Get representation from the left end of Z array
J = DOL - 1
ELSEIF(WBEGIN+OLDFST-1.GT.DOU) THEN
* Get representation from the right end of Z array
J = DOU
ELSE
J = WBEGIN + OLDFST - 1
ENDIF
ENDIF
DO 45 K = 1, IN - 1
D( IBEGIN+K-1 ) = REAL( Z( IBEGIN+K-1,
$ J ) )
L( IBEGIN+K-1 ) = REAL( Z( IBEGIN+K-1,
$ J+1 ) )
45 CONTINUE
D( IEND ) = REAL( Z( IEND, J ) )
SIGMA = REAL( Z( IEND, J+1 ) )
* Set the corresponding entries in Z to zero
CALL CLASET( 'Full', IN, 2, CZERO, CZERO,
$ Z( IBEGIN, J), LDZ )
END IF
* Compute DL and DLL of current RRR
DO 50 J = IBEGIN, IEND-1
TMP = D( J )*L( J )
WORK( INDLD-1+J ) = TMP
WORK( INDLLD-1+J ) = TMP*L( J )
50 CONTINUE
IF( NDEPTH.GT.0 ) THEN
* P and Q are index of the first and last eigenvalue to compute
* within the current block
P = INDEXW( WBEGIN-1+OLDFST )
Q = INDEXW( WBEGIN-1+OLDLST )
* Offset for the arrays WORK, WGAP and WERR, i.e., the P-OFFSET
* through the Q-OFFSET elements of these arrays are to be used.
* OFFSET = P-OLDFST
OFFSET = INDEXW( WBEGIN ) - 1
* perform limited bisection (if necessary) to get approximate
* eigenvalues to the precision needed.
CALL SLARRB( IN, D( IBEGIN ),
$ WORK(INDLLD+IBEGIN-1),
$ P, Q, RTOL1, RTOL2, OFFSET,
$ WORK(WBEGIN),WGAP(WBEGIN),WERR(WBEGIN),
$ WORK( INDWRK ), IWORK( IINDWK ),
$ PIVMIN, SPDIAM, IN, IINFO )
IF( IINFO.NE.0 ) THEN
INFO = -1
RETURN
ENDIF
* We also recompute the extremal gaps. W holds all eigenvalues
* of the unshifted matrix and must be used for computation
* of WGAP, the entries of WORK might stem from RRRs with
* different shifts. The gaps from WBEGIN-1+OLDFST to
* WBEGIN-1+OLDLST are correctly computed in SLARRB.
* However, we only allow the gaps to become greater since
* this is what should happen when we decrease WERR
IF( OLDFST.GT.1) THEN
WGAP( WBEGIN+OLDFST-2 ) =
$ MAX(WGAP(WBEGIN+OLDFST-2),
$ W(WBEGIN+OLDFST-1)-WERR(WBEGIN+OLDFST-1)
$ - W(WBEGIN+OLDFST-2)-WERR(WBEGIN+OLDFST-2) )
ENDIF
IF( WBEGIN + OLDLST -1 .LT. WEND ) THEN
WGAP( WBEGIN+OLDLST-1 ) =
$ MAX(WGAP(WBEGIN+OLDLST-1),
$ W(WBEGIN+OLDLST)-WERR(WBEGIN+OLDLST)
$ - W(WBEGIN+OLDLST-1)-WERR(WBEGIN+OLDLST-1) )
ENDIF
* Each time the eigenvalues in WORK get refined, we store
* the newly found approximation with all shifts applied in W
DO 53 J=OLDFST,OLDLST
W(WBEGIN+J-1) = WORK(WBEGIN+J-1)+SIGMA
53 CONTINUE
END IF
* Process the current node.
NEWFST = OLDFST
DO 140 J = OLDFST, OLDLST
IF( J.EQ.OLDLST ) THEN
* we are at the right end of the cluster, this is also the
* boundary of the child cluster
NEWLST = J
ELSE IF ( WGAP( WBEGIN + J -1).GE.
$ MINRGP* ABS( WORK(WBEGIN + J -1) ) ) THEN
* the right relative gap is big enough, the child cluster
* (NEWFST,..,NEWLST) is well separated from the following
NEWLST = J
ELSE
* inside a child cluster, the relative gap is not
* big enough.
GOTO 140
END IF
* Compute size of child cluster found
NEWSIZ = NEWLST - NEWFST + 1
* NEWFTT is the place in Z where the new RRR or the computed
* eigenvector is to be stored
IF((DOL.EQ.1).AND.(DOU.EQ.M)) THEN
* Store representation at location of the leftmost evalue
* of the cluster
NEWFTT = WBEGIN + NEWFST - 1
ELSE
IF(WBEGIN+NEWFST-1.LT.DOL) THEN
* Store representation at the left end of Z array
NEWFTT = DOL - 1
ELSEIF(WBEGIN+NEWFST-1.GT.DOU) THEN
* Store representation at the right end of Z array
NEWFTT = DOU
ELSE
NEWFTT = WBEGIN + NEWFST - 1
ENDIF
ENDIF
IF( NEWSIZ.GT.1) THEN
*
* Current child is not a singleton but a cluster.
* Compute and store new representation of child.
*
*
* Compute left and right cluster gap.
*
* LGAP and RGAP are not computed from WORK because
* the eigenvalue approximations may stem from RRRs
* different shifts. However, W hold all eigenvalues
* of the unshifted matrix. Still, the entries in WGAP
* have to be computed from WORK since the entries
* in W might be of the same order so that gaps are not
* exhibited correctly for very close eigenvalues.
IF( NEWFST.EQ.1 ) THEN
LGAP = MAX( ZERO,
$ W(WBEGIN)-WERR(WBEGIN) - VL )
ELSE
LGAP = WGAP( WBEGIN+NEWFST-2 )
ENDIF
RGAP = WGAP( WBEGIN+NEWLST-1 )
*
* Compute left- and rightmost eigenvalue of child
* to high precision in order to shift as close
* as possible and obtain as large relative gaps
* as possible
*
DO 55 K =1,2
IF(K.EQ.1) THEN
P = INDEXW( WBEGIN-1+NEWFST )
ELSE
P = INDEXW( WBEGIN-1+NEWLST )
ENDIF
OFFSET = INDEXW( WBEGIN ) - 1
CALL SLARRB( IN, D(IBEGIN),
$ WORK( INDLLD+IBEGIN-1 ),P,P,
$ RQTOL, RQTOL, OFFSET,
$ WORK(WBEGIN),WGAP(WBEGIN),
$ WERR(WBEGIN),WORK( INDWRK ),
$ IWORK( IINDWK ), PIVMIN, SPDIAM,
$ IN, IINFO )
55 CONTINUE
*
IF((WBEGIN+NEWLST-1.LT.DOL).OR.
$ (WBEGIN+NEWFST-1.GT.DOU)) THEN
* if the cluster contains no desired eigenvalues
* skip the computation of that branch of the rep. tree
*
* We could skip before the refinement of the extremal
* eigenvalues of the child, but then the representation
* tree could be different from the one when nothing is
* skipped. For this reason we skip at this place.
IDONE = IDONE + NEWLST - NEWFST + 1
GOTO 139
ENDIF
*
* Compute RRR of child cluster.
* Note that the new RRR is stored in Z
*
* SLARRF needs LWORK = 2*N
CALL SLARRF( IN, D( IBEGIN ), L( IBEGIN ),
$ WORK(INDLD+IBEGIN-1),
$ NEWFST, NEWLST, WORK(WBEGIN),
$ WGAP(WBEGIN), WERR(WBEGIN),
$ SPDIAM, LGAP, RGAP, PIVMIN, TAU,
$ WORK( INDIN1 ), WORK( INDIN2 ),
$ WORK( INDWRK ), IINFO )
* In the complex case, SLARRF cannot write
* the new RRR directly into Z and needs an intermediate
* workspace
DO 56 K = 1, IN-1
Z( IBEGIN+K-1, NEWFTT ) =
$ CMPLX( WORK( INDIN1+K-1 ), ZERO )
Z( IBEGIN+K-1, NEWFTT+1 ) =
$ CMPLX( WORK( INDIN2+K-1 ), ZERO )
56 CONTINUE
Z( IEND, NEWFTT ) =
$ CMPLX( WORK( INDIN1+IN-1 ), ZERO )
IF( IINFO.EQ.0 ) THEN
* a new RRR for the cluster was found by SLARRF
* update shift and store it
SSIGMA = SIGMA + TAU
Z( IEND, NEWFTT+1 ) = CMPLX( SSIGMA, ZERO )
* WORK() are the midpoints and WERR() the semi-width
* Note that the entries in W are unchanged.
DO 116 K = NEWFST, NEWLST
FUDGE =
$ THREE*EPS*ABS(WORK(WBEGIN+K-1))
WORK( WBEGIN + K - 1 ) =
$ WORK( WBEGIN + K - 1) - TAU
FUDGE = FUDGE +
$ FOUR*EPS*ABS(WORK(WBEGIN+K-1))
* Fudge errors
WERR( WBEGIN + K - 1 ) =
$ WERR( WBEGIN + K - 1 ) + FUDGE
* Gaps are not fudged. Provided that WERR is small
* when eigenvalues are close, a zero gap indicates
* that a new representation is needed for resolving
* the cluster. A fudge could lead to a wrong decision
* of judging eigenvalues 'separated' which in
* reality are not. This could have a negative impact
* on the orthogonality of the computed eigenvectors.
116 CONTINUE
NCLUS = NCLUS + 1
K = NEWCLS + 2*NCLUS
IWORK( K-1 ) = NEWFST
IWORK( K ) = NEWLST
ELSE
INFO = -2
RETURN
ENDIF
ELSE
*
* Compute eigenvector of singleton
*
ITER = 0
*
TOL = FOUR * LOG(REAL(IN)) * EPS
*
K = NEWFST
WINDEX = WBEGIN + K - 1
WINDMN = MAX(WINDEX - 1,1)
WINDPL = MIN(WINDEX + 1,M)
LAMBDA = WORK( WINDEX )
DONE = DONE + 1
* Check if eigenvector computation is to be skipped
IF((WINDEX.LT.DOL).OR.
$ (WINDEX.GT.DOU)) THEN
ESKIP = .TRUE.
GOTO 125
ELSE
ESKIP = .FALSE.
ENDIF
LEFT = WORK( WINDEX ) - WERR( WINDEX )
RIGHT = WORK( WINDEX ) + WERR( WINDEX )
INDEIG = INDEXW( WINDEX )
* Note that since we compute the eigenpairs for a child,
* all eigenvalue approximations are w.r.t the same shift.
* In this case, the entries in WORK should be used for
* computing the gaps since they exhibit even very small
* differences in the eigenvalues, as opposed to the
* entries in W which might "look" the same.
IF( K .EQ. 1) THEN
* In the case RANGE='I' and with not much initial
* accuracy in LAMBDA and VL, the formula
* LGAP = MAX( ZERO, (SIGMA - VL) + LAMBDA )
* can lead to an overestimation of the left gap and
* thus to inadequately early RQI 'convergence'.
* Prevent this by forcing a small left gap.
LGAP = EPS*MAX(ABS(LEFT),ABS(RIGHT))
ELSE
LGAP = WGAP(WINDMN)
ENDIF
IF( K .EQ. IM) THEN
* In the case RANGE='I' and with not much initial
* accuracy in LAMBDA and VU, the formula
* can lead to an overestimation of the right gap and
* thus to inadequately early RQI 'convergence'.
* Prevent this by forcing a small right gap.
RGAP = EPS*MAX(ABS(LEFT),ABS(RIGHT))
ELSE
RGAP = WGAP(WINDEX)
ENDIF
GAP = MIN( LGAP, RGAP )
IF(( K .EQ. 1).OR.(K .EQ. IM)) THEN
* The eigenvector support can become wrong
* because significant entries could be cut off due to a
* large GAPTOL parameter in LAR1V. Prevent this.
GAPTOL = ZERO
ELSE
GAPTOL = GAP * EPS
ENDIF
ISUPMN = IN
ISUPMX = 1
* Update WGAP so that it holds the minimum gap
* to the left or the right. This is crucial in the
* case where bisection is used to ensure that the
* eigenvalue is refined up to the required precision.
* The correct value is restored afterwards.
SAVGAP = WGAP(WINDEX)
WGAP(WINDEX) = GAP
* We want to use the Rayleigh Quotient Correction
* as often as possible since it converges quadratically
* when we are close enough to the desired eigenvalue.
* However, the Rayleigh Quotient can have the wrong sign
* and lead us away from the desired eigenvalue. In this
* case, the best we can do is to use bisection.
USEDBS = .FALSE.
USEDRQ = .FALSE.
* Bisection is initially turned off unless it is forced
NEEDBS = .NOT.TRYRQC
120 CONTINUE
* Check if bisection should be used to refine eigenvalue
IF(NEEDBS) THEN
* Take the bisection as new iterate
USEDBS = .TRUE.
ITMP1 = IWORK( IINDR+WINDEX )
OFFSET = INDEXW( WBEGIN ) - 1
CALL SLARRB( IN, D(IBEGIN),
$ WORK(INDLLD+IBEGIN-1),INDEIG,INDEIG,
$ ZERO, TWO*EPS, OFFSET,
$ WORK(WBEGIN),WGAP(WBEGIN),
$ WERR(WBEGIN),WORK( INDWRK ),
$ IWORK( IINDWK ), PIVMIN, SPDIAM,
$ ITMP1, IINFO )
IF( IINFO.NE.0 ) THEN
INFO = -3
RETURN
ENDIF
LAMBDA = WORK( WINDEX )
* Reset twist index from inaccurate LAMBDA to
* force computation of true MINGMA
IWORK( IINDR+WINDEX ) = 0
ENDIF
* Given LAMBDA, compute the eigenvector.
CALL CLAR1V( IN, 1, IN, LAMBDA, D( IBEGIN ),
$ L( IBEGIN ), WORK(INDLD+IBEGIN-1),
$ WORK(INDLLD+IBEGIN-1),
$ PIVMIN, GAPTOL, Z( IBEGIN, WINDEX ),
$ .NOT.USEDBS, NEGCNT, ZTZ, MINGMA,
$ IWORK( IINDR+WINDEX ), ISUPPZ( 2*WINDEX-1 ),
$ NRMINV, RESID, RQCORR, WORK( INDWRK ) )
IF(ITER .EQ. 0) THEN
BSTRES = RESID
BSTW = LAMBDA
ELSEIF(RESID.LT.BSTRES) THEN
BSTRES = RESID
BSTW = LAMBDA
ENDIF
ISUPMN = MIN(ISUPMN,ISUPPZ( 2*WINDEX-1 ))
ISUPMX = MAX(ISUPMX,ISUPPZ( 2*WINDEX ))
ITER = ITER + 1
* sin alpha <= |resid|/gap
* Note that both the residual and the gap are
* proportional to the matrix, so ||T|| doesn't play
* a role in the quotient
*
* Convergence test for Rayleigh-Quotient iteration
* (omitted when Bisection has been used)
*
IF( RESID.GT.TOL*GAP .AND. ABS( RQCORR ).GT.
$ RQTOL*ABS( LAMBDA ) .AND. .NOT. USEDBS)
$ THEN
* We need to check that the RQCORR update doesn't
* move the eigenvalue away from the desired one and
* towards a neighbor. -> protection with bisection
IF(INDEIG.LE.NEGCNT) THEN
* The wanted eigenvalue lies to the left
SGNDEF = -ONE
ELSE
* The wanted eigenvalue lies to the right
SGNDEF = ONE
ENDIF
* We only use the RQCORR if it improves the
* the iterate reasonably.
IF( ( RQCORR*SGNDEF.GE.ZERO )
$ .AND.( LAMBDA + RQCORR.LE. RIGHT)
$ .AND.( LAMBDA + RQCORR.GE. LEFT)
$ ) THEN
USEDRQ = .TRUE.
* Store new midpoint of bisection interval in WORK
IF(SGNDEF.EQ.ONE) THEN
* The current LAMBDA is on the left of the true
* eigenvalue
LEFT = LAMBDA
* We prefer to assume that the error estimate
* is correct. We could make the interval not
* as a bracket but to be modified if the RQCORR
* chooses to. In this case, the RIGHT side should
* be modified as follows:
* RIGHT = MAX(RIGHT, LAMBDA + RQCORR)
ELSE
* The current LAMBDA is on the right of the true
* eigenvalue
RIGHT = LAMBDA
* See comment about assuming the error estimate is
* correct above.
* LEFT = MIN(LEFT, LAMBDA + RQCORR)
ENDIF
WORK( WINDEX ) =
$ HALF * (RIGHT + LEFT)
* Take RQCORR since it has the correct sign and
* improves the iterate reasonably
LAMBDA = LAMBDA + RQCORR
* Update width of error interval
WERR( WINDEX ) =
$ HALF * (RIGHT-LEFT)
ELSE
NEEDBS = .TRUE.
ENDIF
IF(RIGHT-LEFT.LT.RQTOL*ABS(LAMBDA)) THEN
* The eigenvalue is computed to bisection accuracy
* compute eigenvector and stop
USEDBS = .TRUE.
GOTO 120
ELSEIF( ITER.LT.MAXITR ) THEN
GOTO 120
ELSEIF( ITER.EQ.MAXITR ) THEN
NEEDBS = .TRUE.
GOTO 120
ELSE
INFO = 5
RETURN
END IF
ELSE
STP2II = .FALSE.
IF(USEDRQ .AND. USEDBS .AND.
$ BSTRES.LE.RESID) THEN
LAMBDA = BSTW
STP2II = .TRUE.
ENDIF
IF (STP2II) THEN
* improve error angle by second step
CALL CLAR1V( IN, 1, IN, LAMBDA,
$ D( IBEGIN ), L( IBEGIN ),
$ WORK(INDLD+IBEGIN-1),
$ WORK(INDLLD+IBEGIN-1),
$ PIVMIN, GAPTOL, Z( IBEGIN, WINDEX ),
$ .NOT.USEDBS, NEGCNT, ZTZ, MINGMA,
$ IWORK( IINDR+WINDEX ),
$ ISUPPZ( 2*WINDEX-1 ),
$ NRMINV, RESID, RQCORR, WORK( INDWRK ) )
ENDIF
WORK( WINDEX ) = LAMBDA
END IF
*
* Compute FP-vector support w.r.t. whole matrix
*
ISUPPZ( 2*WINDEX-1 ) = ISUPPZ( 2*WINDEX-1 )+OLDIEN
ISUPPZ( 2*WINDEX ) = ISUPPZ( 2*WINDEX )+OLDIEN
ZFROM = ISUPPZ( 2*WINDEX-1 )
ZTO = ISUPPZ( 2*WINDEX )
ISUPMN = ISUPMN + OLDIEN