!>
!> SSYEVR_2STAGE computes selected eigenvalues and, optionally, eigenvectors
!> of a real symmetric matrix A using the 2stage technique for
!> the reduction to tridiagonal.  Eigenvalues and eigenvectors can be
!> selected by specifying either a range of values or a range of
!> indices for the desired eigenvalues.
!>
!> SSYEVR_2STAGE first reduces the matrix A to tridiagonal form T with a call
!> to SSYTRD.  Then, whenever possible, SSYEVR_2STAGE calls SSTEMR to compute
!> the eigenspectrum using Relatively Robust Representations.  SSTEMR
!> computes eigenvalues by the dqds algorithm, while orthogonal
!> eigenvectors are computed from various  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 each unreduced block (submatrix) of T,
!>    (a) Compute T - sigma I  = L D L^T, so that L and D
!>        define all the wanted eigenvalues to high relative accuracy.
!>        This means that small relative changes in the entries of D and L
!>        cause only small relative changes in the eigenvalues and
!>        eigenvectors. The standard (unfactored) representation of the
!>        tridiagonal matrix T does not have this property in general.
!>    (b) Compute the eigenvalues to suitable accuracy.
!>        If the eigenvectors are desired, the algorithm attains full
!>        accuracy of the computed eigenvalues only right before
!>        the corresponding vectors have to be computed, see steps c) and d).
!>    (c) For each cluster of close eigenvalues, select a new
!>        shift close to the cluster, find a new factorization, and refine
!>        the shifted eigenvalues to suitable accuracy.
!>    (d) For each eigenvalue with a large enough relative separation compute
!>        the corresponding eigenvector by forming a rank revealing twisted
!>        factorization. Go back to (c) for any clusters that remain.
!>
!> The desired accuracy of the output can be specified by the input
!> parameter ABSTOL.
!>
!> For more details, see SSTEMR's documentation and:
!> - Inderjit S. Dhillon and Beresford N. Parlett: 
!>   Linear Algebra and its Applications, 387(1), pp. 1-28, August 2004.
!> - Inderjit Dhillon and Beresford Parlett:  SIAM Journal on Matrix Analysis and Applications, Vol. 25,
!>   2004.  Also LAPACK Working Note 154.
!> - Inderjit Dhillon: ,
!>   Computer Science Division Technical Report No. UCB/CSD-97-971,
!>   UC Berkeley, May 1997.
!>
!>
!> Note 1 : SSYEVR_2STAGE calls SSTEMR when the full spectrum is requested
!> on machines which conform to the ieee-754 floating point standard.
!> SSYEVR_2STAGE 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.
!> 

JOBZ

!>          JOBZ is CHARACTER*1
!>          = 'N':  Compute eigenvalues only;
!>          = 'V':  Compute eigenvalues and eigenvectors.
!>                  Not available in this release.
!> 

RANGE

!>          RANGE is 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.
!>          For RANGE = 'V' or 'I' and IU - IL < N - 1, SSTEBZ and
!>          SSTEIN are called
!> 

UPLO

!>          UPLO is CHARACTER*1
!>          = 'U':  Upper triangle of A is stored;
!>          = 'L':  Lower triangle of A is stored.
!> 

N

!>          N is INTEGER
!>          The order of the matrix A.  N >= 0.
!> 

A

!>          A is REAL array, dimension (LDA, N)
!>          On entry, the symmetric matrix A.  If UPLO = 'U', the
!>          leading N-by-N upper triangular part of A contains the
!>          upper triangular part of the matrix A.  If UPLO = 'L',
!>          the leading N-by-N lower triangular part of A contains
!>          the lower triangular part of the matrix A.
!>          On exit, the lower triangle (if UPLO='L') or the upper
!>          triangle (if UPLO='U') of A, including the diagonal, is
!>          destroyed.
!> 

LDA

!>          LDA is INTEGER
!>          The leading dimension of the array A.  LDA >= max(1,N).
!> 

VL

!>          VL is REAL
!>          If RANGE='V', the lower bound of the interval to
!>          be searched for eigenvalues. VL < VU.
!>          Not referenced if RANGE = 'A' or 'I'.
!> 

VU

!>          VU is REAL
!>          If RANGE='V', the upper bound of the interval to
!>          be searched for eigenvalues. VL < VU.
!>          Not referenced if RANGE = 'A' or 'I'.
!> 

IL

!>          IL is INTEGER
!>          If RANGE='I', the index of the
!>          smallest eigenvalue 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'.
!> 

IU

!>          IU is INTEGER
!>          If RANGE='I', the index of the
!>          largest eigenvalue 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

!>          ABSTOL is REAL
!>          The absolute error tolerance for the eigenvalues.
!>          An approximate 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 reducing A to tridiagonal form.
!>
!>          See  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,
!>          , LAPACK Working Note #7, for a discussion
!>          of which matrices define their eigenvalues to high relative
!>          accuracy.
!> 

M

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

W

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

Z

!>          Z is REAL array, dimension (LDZ, max(1,M))
!>          If JOBZ = 'V', then if INFO = 0, the first M columns of Z
!>          contain 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).
!>          If JOBZ = 'N', then Z is not referenced.
!>          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.
!>          Supplying N columns is always safe.
!> 

LDZ

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

ISUPPZ

!>          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 ). This is an output of SSTEMR (tridiagonal
!>          matrix). The support of the eigenvectors of A is typically
!>          1:N because of the orthogonal transformations applied by SORMTR.
!>          Implemented only for RANGE = 'A' or 'I' and IU - IL = N - 1
!> 

WORK

!>          WORK is REAL array, dimension (MAX(1,LWORK))
!>          On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
!> 

LWORK

!>          LWORK is INTEGER
!>          The dimension of the array WORK.
!>          If JOBZ = 'N' and N > 1, LWORK must be queried.
!>                                   LWORK = MAX(1, 26*N, dimension) where
!>                                   dimension = max(stage1,stage2) + (KD+1)*N + 5*N
!>                                             = N*KD + N*max(KD+1,FACTOPTNB)
!>                                               + max(2*KD*KD, KD*NTHREADS)
!>                                               + (KD+1)*N + 5*N
!>                                   where KD is the blocking size of the reduction,
!>                                   FACTOPTNB is the blocking used by the QR or LQ
!>                                   algorithm, usually FACTOPTNB=128 is a good choice
!>                                   NTHREADS is the number of threads used when
!>                                   openMP compilation is enabled, otherwise =1.
!>          If JOBZ = 'V' and N > 1, LWORK must be queried. Not yet available
!>
!>          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

!>          IWORK is INTEGER array, dimension (MAX(1,LIWORK))
!>          On exit, if INFO = 0, IWORK(1) returns the optimal LWORK.
!> 

LIWORK

!>          LIWORK is INTEGER
!>          The dimension of the array IWORK.  LIWORK >= max(1,10*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

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

Univ. of Tennessee

Univ. of California Berkeley

Univ. of Colorado Denver

NAG Ltd.

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

!>
!>  All details about the 2stage techniques are available in:
!>
!>  Azzam Haidar, Hatem Ltaief, and Jack Dongarra.
!>  Parallel reduction to condensed forms for symmetric eigenvalue problems
!>  using aggregated fine-grained and memory-aware kernels. In Proceedings
!>  of 2011 International Conference for High Performance Computing,
!>  Networking, Storage and Analysis (SC '11), New York, NY, USA,
!>  Article 8 , 11 pages.
!>  http://doi.acm.org/10.1145/2063384.2063394
!>
!>  A. Haidar, J. Kurzak, P. Luszczek, 2013.
!>  An improved parallel singular value algorithm and its implementation
!>  for multicore hardware, In Proceedings of 2013 International Conference
!>  for High Performance Computing, Networking, Storage and Analysis (SC '13).
!>  Denver, Colorado, USA, 2013.
!>  Article 90, 12 pages.
!>  http://doi.acm.org/10.1145/2503210.2503292
!>
!>  A. Haidar, R. Solca, S. Tomov, T. Schulthess and J. Dongarra.
!>  A novel hybrid CPU-GPU generalized eigensolver for electronic structure
!>  calculations based on fine-grained memory aware tasks.
!>  International Journal of High Performance Computing Applications.
!>  Volume 28 Issue 2, Pages 196-209, May 2014.
!>  http://hpc.sagepub.com/content/28/2/196
!>
!>