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SSTEVR(1)	      LAPACK driver routine (version 3.2)	     SSTEVR(1)

NAME
       SSTEVR - computes 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 SSTEMR to compute the
       eigenspectrum using Relatively Robust Representations.  SSTEMR 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 SSTEMR 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 SSTEMR 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 (max(1,N-1))
	       On  entry,  the	(n-1)  subdiagonal elements of the tridiagonal
	       matrix A in elements 1 to N-1 of E.  On exit, E may  be	multi‐
	       plied  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 pre‐
	       cision.	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 reducing 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	impor‐
	       tant,  set  ABSTOL  to SLAMCH( 'Safe minimum' ).	 Doing so will
	       guarantee that eigenvalues are computed to high relative	 accu‐
	       racy  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 Diagonally Dominant  Matrices",
	       LAPACK  Working	Note  #7,  for	a discussion of which matrices
	       define their eigenvalues to high relative 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 (MAX(1,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 calcu‐
	       lates the optimal sizes of the WORK and IWORK  arrays,  returns
	       these values as the first entries of the WORK and IWORK arrays,
	       and no error message related to LWORK or LIWORK	is  issued  by
	       XERBLA.

       IWORK   (workspace/output) INTEGER array, dimension (MAX(1,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 calcu‐
	       lates the optimal sizes of the WORK and IWORK  arrays,  returns
	       these values as the first entries of the WORK and IWORK arrays,
	       and no error message related to LWORK or 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
	  Jason Riedy, Computer Science Division, University of
	    California at Berkeley, USA

 LAPACK driver routine (version 3November 2008			     SSTEVR(1)
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