NAG CL Interface
f08jlc (dstegr)

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1 Purpose

f08jlc computes selected eigenvalues and, optionally, the corresponding eigenvectors of a real n×n symmetric tridiagonal matrix.

2 Specification

#include <nag.h>
void  f08jlc (Nag_OrderType order, Nag_JobType job, Nag_RangeType range, Integer n, double d[], double e[], double vl, double vu, Integer il, Integer iu, Integer *m, double w[], double z[], Integer pdz, Integer isuppz[], NagError *fail)
The function may be called by the names: f08jlc, nag_lapackeig_dstegr or nag_dstegr.

3 Description

f08jlc computes selected eigenvalues and, optionally, the corresponding eigenvectors, of a real symmetric tridiagonal matrix T. That is, the function computes the (partial) spectral factorization of T given by
ZΛZT ,  
where Λ is a diagonal matrix whose diagonal elements are the selected eigenvalues, λi, of T and Z is an orthogonal matrix whose columns are the corresponding eigenvectors, zi, of T. Thus
Tzi= λi zi ,   i = 1,2,,m  
where m is the number of selected eigenvalues computed.
The function may also be used to compute selected eigenvalues and eigenvectors of a real symmetric matrix A which has been reduced to tridiagonal form T:
(QZ) Λ (QZ)T , where ​Q​ is orthogonal.  
In this case, the matrix Q must be explicitly applied to the output matrix Z. The functions which must be called to perform the reduction to tridiagonal form and apply Q are:
full matrix f08fec and f08fgc
full matrix, packed storage f08gec and f08ggc
band matrix f08hec with vect=Nag_FormQ and f16yac.
This function uses the dqds and the Relatively Robust Representation algorithms to compute the eigenvalues and eigenvectors respectively; see for example Parlett and Dhillon (2000) and Dhillon and Parlett (2004) for further details. f08jlc can usually compute all the eigenvalues and eigenvectors in O(n2) floating-point operations and so, for large matrices, is often considerably faster than the other symmetric tridiagonal functions in this chapter when all the eigenvectors are required, particularly so compared to those functions that are based on the QR algorithm.

4 References

Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J J, Du Croz J J, Greenbaum A, Hammarling S, McKenney A and Sorensen D (1999) LAPACK Users' Guide (3rd Edition) SIAM, Philadelphia https://www.netlib.org/lapack/lug
Barlow J and Demmel J W (1990) Computing accurate eigensystems of scaled diagonally dominant matrices SIAM J. Numer. Anal. 27 762–791
Dhillon I S and Parlett B N (2004) Orthogonal eigenvectors and relative gaps SIAM J. Appl. Math. 25 858–899
Parlett B N and Dhillon I S (2000) Relatively robust representations of symmetric tridiagonals Linear Algebra Appl. 309 121–151

5 Arguments

1: order Nag_OrderType Input
On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by order=Nag_RowMajor. See Section 3.1.3 in the Introduction to the NAG Library CL Interface for a more detailed explanation of the use of this argument.
Constraint: order=Nag_RowMajor or Nag_ColMajor.
2: job Nag_JobType Input
On entry: indicates whether eigenvectors are computed.
job=Nag_EigVals
Only eigenvalues are computed.
job=Nag_DoBoth
Eigenvalues and eigenvectors are computed.
Constraint: job=Nag_EigVals or Nag_DoBoth.
3: range Nag_RangeType Input
On entry: indicates which eigenvalues should be returned.
range=Nag_AllValues
All eigenvalues will be found.
range=Nag_Interval
All eigenvalues in the half-open interval (vl,vu] will be found.
range=Nag_Indices
The ilth through iuth eigenvectors will be found.
Constraint: range=Nag_AllValues, Nag_Interval or Nag_Indices.
4: n Integer Input
On entry: n, the order of the matrix T.
Constraint: n0.
5: d[dim] double Input/Output
Note: the dimension, dim, of the array d must be at least max(1,n).
On entry: the n diagonal elements of the tridiagonal matrix T.
On exit: d is overwritten.
6: e[dim] double Input/Output
Note: the dimension, dim, of the array e must be at least max(1,n).
On entry: e[0] to e[n-2] are the subdiagonal elements of the tridiagonal matrix T. e[n-1] need not be set.
On exit: e is overwritten.
7: vl double Input
8: vu double Input
On entry: if range=Nag_Interval, vl and vu contain the lower and upper bounds respectively of the interval to be searched for eigenvalues.
If range=Nag_AllValues or Nag_Indices, vl and vu are not referenced.
Constraint: if range=Nag_Interval, vl<vu.
9: il Integer Input
10: iu Integer Input
On entry: if range=Nag_Indices, il and iu specify the indices (in ascending order) of the smallest and largest eigenvalues to be returned, respectively.
If range=Nag_AllValues or Nag_Interval, il and iu are not referenced.
Constraints:
  • if range=Nag_Indices and n>0, 1 il iu n ;
  • if range=Nag_Indices and n=0, il=1 and iu=0.
11: m Integer * Output
On exit: the total number of eigenvalues found. 0mn.
If range=Nag_AllValues, m=n.
If range=Nag_Indices, m=iu-il+1.
12: w[dim] double Output
Note: the dimension, dim, of the array w must be at least max(1,n).
On exit: the eigenvalues in ascending order.
13: z[dim] double Output
Note: the dimension, dim, of the array z must be at least
  • max(1,pdz×n) when job=Nag_DoBoth;
  • 1 otherwise.
The (i,j)th element of the matrix Z is stored in
  • z[(j-1)×pdz+i-1] when order=Nag_ColMajor;
  • z[(i-1)×pdz+j-1] when order=Nag_RowMajor.
On exit: if job=Nag_DoBoth, then if fail.code= NE_NOERROR, the columns of z contain the orthonormal eigenvectors of the matrix T, with the ith column of Z holding the eigenvector associated with w[i-1].
If job=Nag_EigVals, z is not referenced.
14: pdz Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array z.
Constraints:
  • if job=Nag_DoBoth, pdz max(1,n) ;
  • otherwise pdz1.
15: isuppz[dim] Integer Output
Note: the dimension, dim, of the array isuppz must be at least max(1,2×m).
On exit: the support of the eigenvectors in Z, i.e., the indices indicating the nonzero elements in Z. The ith eigenvector is nonzero only in elements isuppz[2×i-2] through isuppz[2×i-1].
16: fail NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_CONVERGENCE
Inverse iteration failed to converge.
The dqds algorithm failed to converge.
NE_ENUM_INT_2
On entry, job=value, pdz=value and n=value.
Constraint: if job=Nag_DoBoth, pdz max(1,n) ;
otherwise pdz1.
NE_ENUM_INT_3
On entry, range=value, il=value, iu=value and n=value.
Constraint: if range=Nag_Indices and n>0, 1 il iu n ;
if range=Nag_Indices and n=0, il=1 and iu=0.
NE_ENUM_REAL_2
On entry, range=value, vl=value and vu=value.
Constraint: if range=Nag_Interval, vl<vu.
NE_INT
On entry, n=value.
Constraint: n0.
On entry, pdz=value.
Constraint: pdz>0.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.

7 Accuracy

See Section 4.7 of Anderson et al. (1999) and Barlow and Demmel (1990) for further details.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.
f08jlc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f08jlc makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

The total number of floating-point operations required to compute all the eigenvalues and eigenvectors is approximately proportional to n2.
The complex analogue of this function is f08jyc.

10 Example

This example finds all the eigenvalues and eigenvectors of the symmetric tridiagonal matrix
T = ( 1.0 1.0 0.0 0.0 1.0 4.0 2.0 0.0 0.0 2.0 9.0 3.0 0.0 0.0 3.0 16.0 ) .  

10.1 Program Text

Program Text (f08jlce.c)

10.2 Program Data

Program Data (f08jlce.d)

10.3 Program Results

Program Results (f08jlce.r)