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SSTEVR(l)			       )			     SSTEVR(l)

NAME
       SSTEVR  - compute selected eigenvalues and, optionally, eigenvectors of
       a real symmetric tridiagonal matrix T

SYNOPSIS
       SUBROUTINE SSTEVR( JOBZ, RANGE, N, D, E, VL, VU, IL, IU, ABSTOL, M,  W,
			  Z, LDZ, ISUPPZ, WORK, LWORK, IWORK, LIWORK, INFO )

	   CHARACTER	  JOBZ, RANGE

	   INTEGER	  IL, INFO, IU, LDZ, LIWORK, LWORK, M, N

	   REAL		  ABSTOL, VL, VU

	   INTEGER	  ISUPPZ( * ), IWORK( * )

	   REAL		  D( * ), E( * ), W( * ), WORK( * ), Z( LDZ, * )

PURPOSE
       SSTEVR computes selected eigenvalues and, optionally, eigenvectors of a
       real symmetric tridiagonal matrix T. Eigenvalues and  eigenvectors  can
       be  selected  by	 specifying  either  a	range  of values or a range of
       indices for the desired eigenvalues.

       Whenever possible, SSTEVR calls SSTEGR to compute the
       eigenspectrum using Relatively Robust Representations.  SSTEGR computes
       eigenvalues  by	the  dqds algorithm, while orthogonal eigenvectors are
       computed from various "good" L D L^T  representations  (also  known  as
       Relatively  Robust  Representations). Gram-Schmidt orthogonalization is
       avoided as far as possible. More specifically, the various steps of the
       algorithm are as follows. For the i-th unreduced block of T,
	  (a) Compute T - sigma_i = L_i D_i L_i^T, such that L_i D_i L_i^T
	       is a relatively robust representation,
	  (b) Compute the eigenvalues, lambda_j, of L_i D_i L_i^T to high
	      relative accuracy by the dqds algorithm,
	  (c) If there is a cluster of close eigenvalues, "choose" sigma_i
	      close to the cluster, and go to step (a),
	  (d) Given the approximate eigenvalue lambda_j of L_i D_i L_i^T,
	      compute the corresponding eigenvector by forming a
	      rank-revealing twisted factorization.
       The desired accuracy of the output can be specified by the input param‐
       eter ABSTOL.

       For more details, see "A new O(n^2) algorithm for the symmetric	tridi‐
       agonal  eigenvalue/eigenvector  problem", by Inderjit Dhillon, Computer
       Science Division Technical Report No. UCB//CSD-97-971, UC Berkeley, May
       1997.

       Note  1	:  SSTEVR  calls SSTEGR when the full spectrum is requested on
       machines which conform to the ieee-754 floating point standard.	SSTEVR
       calls SSTEBZ and SSTEIN on non-ieee machines and
       when partial spectrum requests are made.

       Normal execution of SSTEGR may create NaNs and infinities and hence may
       abort due to a floating point exception in environments	which  do  not
       handle NaNs and infinities in the ieee standard default manner.

ARGUMENTS
       JOBZ    (input) CHARACTER*1
	       = 'N':  Compute eigenvalues only;
	       = 'V':  Compute eigenvalues and eigenvectors.

       RANGE   (input) CHARACTER*1
	       = 'A': all eigenvalues will be found.
	       =  'V':	all eigenvalues in the half-open interval (VL,VU] will
	       be found.  = 'I': the IL-th through IU-th eigenvalues  will  be
	       found.

       N       (input) INTEGER
	       The order of the matrix.	 N >= 0.

       D       (input/output) REAL array, dimension (N)
	       On  entry, the n diagonal elements of the tridiagonal matrix A.
	       On exit, D may be multiplied by a  constant  factor  chosen  to
	       avoid over/underflow in computing the eigenvalues.

       E       (input/output) REAL array, dimension (N)
	       On  entry,  the	(n-1)  subdiagonal elements of the tridiagonal
	       matrix A in elements 1 to N-1 of E; E(N) need not be  set.   On
	       exit,  E may be multiplied by a constant factor chosen to avoid
	       over/underflow in computing the eigenvalues.

       VL      (input) REAL
	       VU      (input) REAL If RANGE='V', the lower and	 upper	bounds
	       of  the	interval to be searched for eigenvalues. VL < VU.  Not
	       referenced if RANGE = 'A' or 'I'.

       IL      (input) INTEGER
	       IU      (input) INTEGER If RANGE='I', the indices (in ascending
	       order)  of the smallest and largest eigenvalues to be returned.
	       1 <= IL <= IU <= N, if N > 0; IL = 1 and IU = 0 if N = 0.   Not
	       referenced if RANGE = 'A' or 'V'.

       ABSTOL  (input) REAL
	       The  absolute error tolerance for the eigenvalues.  An approxi‐
	       mate eigenvalue is accepted as converged when it is  determined
	       to lie in an interval [a,b] of width less than or equal to

	       ABSTOL + EPS *	max( |a|,|b| ) ,

	       where  EPS is the machine precision.  If ABSTOL is less than or
	       equal to zero, then  EPS*|T|  will be used in its place,	 where
	       |T|  is the 1-norm of the tridiagonal matrix obtained by reduc‐
	       ing A to tridiagonal form.

	       See "Computing Small Singular  Values  of  Bidiagonal  Matrices
	       with  Guaranteed	 High Relative Accuracy," by Demmel and Kahan,
	       LAPACK Working Note #3.

	       If high relative accuracy is important, set ABSTOL  to  SLAMCH(
	       'Safe minimum' ).  Doing so will guarantee that eigenvalues are
	       computed to high relative  accuracy  when  possible  in	future
	       releases.   The current code does not make any guarantees about
	       high relative accuracy, but future releases will. See J. Barlow
	       and J. Demmel, "Computing Accurate Eigensystems of Scaled Diag‐
	       onally Dominant Matrices", LAPACK Working Note #7, for  a  dis‐
	       cussion of which matrices define their eigenvalues to high rel‐
	       ative accuracy.

       M       (output) INTEGER
	       The total number of eigenvalues found.  0 <= M <= N.  If	 RANGE
	       = 'A', M = N, and if RANGE = 'I', M = IU-IL+1.

       W       (output) REAL array, dimension (N)
	       The  first  M  elements	contain	 the  selected	eigenvalues in
	       ascending order.

       Z       (output) REAL array, dimension (LDZ, max(1,M) )
	       If JOBZ = 'V', then if INFO = 0, the first M columns of Z  con‐
	       tain the orthonormal eigenvectors of the matrix A corresponding
	       to the selected 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; if RANGE = 'V', the exact value of M is not known in advance
	       and an upper bound must be used.

       LDZ     (input) INTEGER
	       The leading dimension of the array Z.  LDZ >= 1, and if JOBZ  =
	       'V', LDZ >= max(1,N).

       ISUPPZ  (output) INTEGER array, dimension ( 2*max(1,M) )
	       The  support  of the eigenvectors in Z, i.e., the indices indi‐
	       cating the nonzero elements  in	Z.  The	 i-th  eigenvector  is
	       nonzero only in elements ISUPPZ( 2*i-1 ) through ISUPPZ( 2*i ).

       WORK    (workspace/output) REAL array, dimension (LWORK)
	       On exit, if INFO = 0, WORK(1) returns the optimal (and minimal)
	       LWORK.

       LWORK   (input) INTEGER
	       The dimension of the array WORK.	 LWORK >= 20*N.

	       If LWORK = -1, then a workspace query is assumed;  the  routine
	       only  calculates	 the  optimal  size of the WORK array, returns
	       this value as the first entry of the WORK array, and  no	 error
	       message related to LWORK is issued by XERBLA.

       IWORK   (workspace/output) INTEGER array, dimension (LIWORK)
	       On  exit,  if INFO = 0, IWORK(1) returns the optimal (and mini‐
	       mal) LIWORK.

       LIWORK  (input) INTEGER
	       The dimension of the array IWORK.  LIWORK >= 10*N.

	       If LIWORK = -1, then a workspace query is assumed; the  routine
	       only  calculates	 the  optimal size of the IWORK array, returns
	       this value as the first entry of the IWORK array, and no	 error
	       message related to LIWORK is issued by XERBLA.

       INFO    (output) INTEGER
	       = 0:  successful exit
	       < 0:  if INFO = -i, the i-th argument had an illegal value
	       > 0:  Internal error

FURTHER DETAILS
       Based on contributions by
	  Inderjit Dhillon, IBM Almaden, USA
	  Osni Marques, LBNL/NERSC, USA
	  Ken Stanley, Computer Science Division, University of
	    California at Berkeley, USA

LAPACK version 3.0		 15 June 2000			     SSTEVR(l)
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