F08JBF (DSTEVX) (PDF version)
F08 Chapter Contents
F08 Chapter Introduction
NAG Library Manual

NAG Library Routine Document

F08JBF (DSTEVX)

Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

F08JBF (DSTEVX) computes selected eigenvalues and, optionally, eigenvectors of a real symmetric tridiagonal matrix A. Eigenvalues and eigenvectors can be selected by specifying either a range of values or a range of indices for the desired eigenvalues.

2  Specification

SUBROUTINE F08JBF ( JOBZ, RANGE, N, D, E, VL, VU, IL, IU, ABSTOL, M, W, Z, LDZ, WORK, IWORK, JFAIL, INFO)
INTEGER  N, IL, IU, M, LDZ, IWORK(5*N), JFAIL(*), INFO
REAL (KIND=nag_wp)  D(*), E(*), VL, VU, ABSTOL, W(N), Z(LDZ,*), WORK(5*N)
CHARACTER(1)  JOBZ, RANGE
The routine may be called by its LAPACK name dstevx.

3  Description

F08JBF (DSTEVX) computes the required eigenvalues and eigenvectors of A by reducing the tridiagonal matrix to diagonal form using the QR algorithm. Bisection is used to determine selected eigenvalues.

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 http://www.netlib.org/lapack/lug
Demmel J W and Kahan W (1990) Accurate singular values of bidiagonal matrices SIAM J. Sci. Statist. Comput. 11 873–912
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

5  Parameters

1:     JOBZ – CHARACTER(1)Input
On entry: if JOBZ='N', compute eigenvalues only.
If JOBZ='V', compute eigenvalues and eigenvectors.
Constraint: JOBZ='N' or 'V'.
2:     RANGE – CHARACTER(1)Input
Constraint: RANGE='A', 'V' or 'I'.
3:     N – INTEGERInput
On entry: n, the order of the matrix.
Constraint: N0.
4:     D(*) – REAL (KIND=nag_wp) arrayInput/Output
Note: the dimension of the array D must be at least max1,N.
On entry: the n diagonal elements of the tridiagonal matrix A.
On exit: may be multiplied by a constant factor chosen to avoid over/underflow in computing the eigenvalues.
5:     E(*) – REAL (KIND=nag_wp) arrayInput/Output
Note: the dimension of the array E must be at least max1,N-1.
On entry: the n-1 subdiagonal elements of the tridiagonal matrix A.
On exit: may be multiplied by a constant factor chosen to avoid over/underflow in computing the eigenvalues.
6:     VL – REAL (KIND=nag_wp)Input
7:     VU – REAL (KIND=nag_wp)Input
Constraint: if RANGE='V', VL<VU.
8:     IL – INTEGERInput
9:     IU – INTEGERInput
Constraints:
  • if RANGE='I' and N=0, IL=1 and IU=0;
  • if RANGE='I' and N>0, 1ILIUN.
10:   ABSTOL – REAL (KIND=nag_wp)Input
On entry: 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+ε maxa,b ,
where ε  is the machine precision. If
ABSTOL is less than or equal to zero, then ε A1  will be used in its place. Eigenvalues will be computed most accurately when ABSTOL is set to twice the underflow threshold 2 × X02AMF   , not zero. If this routine returns with INFO>0, indicating that some eigenvectors did not converge, try setting ABSTOL to 2 × X02AMF   . See Demmel and Kahan (1990).
11:   M – INTEGEROutput
On exit: the total number of eigenvalues found. 0MN.
If RANGE='A', M=N.
If RANGE='I', M=IU-IL+1.
12:   W(N) – REAL (KIND=nag_wp) arrayOutput
On exit: the first M elements contain the selected eigenvalues in ascending order.
13:   Z(LDZ,*) – REAL (KIND=nag_wp) arrayOutput
Note: the second dimension of the array Z must be at least max1,M if JOBZ='V', and at least 1 otherwise.
On exit: 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 ith column of Z holding the eigenvector associated with Wi.
If an eigenvector fails to converge (INFO>0), then that column of Z contains the latest approximation to the eigenvector, and the index of the eigenvector is returned in JFAIL.
If JOBZ='N', Z is not referenced.
Note:  you must ensure that at least max1,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 of at least N must be used.
14:   LDZ – INTEGERInput
On entry: the first dimension of the array Z as declared in the (sub)program from which F08JBF (DSTEVX) is called.
Constraints:
  • if JOBZ='V', LDZmax1,N;
  • otherwise LDZ1.
15:   WORK(5×N) – REAL (KIND=nag_wp) arrayWorkspace
16:   IWORK(5×N) – INTEGER arrayWorkspace
17:   JFAIL(*) – INTEGER arrayOutput
On exit: if JOBZ='V', then if INFO=0, the first M elements of JFAIL are zero.
If INFO>0, JFAIL contains the indices of the eigenvectors that failed to converge.
If JOBZ='N', JFAIL is not referenced.
18:   INFO – INTEGEROutput

6  Error Indicators and Warnings

Errors or warnings detected by the routine:
INFO<0
If INFO=-i, argument i had an illegal value. An explanatory message is output, and execution of the program is terminated.
INFO>0

7  Accuracy

The computed eigenvalues and eigenvectors are exact for a nearby matrix A+E, where
E2 = Oε A2 ,
and ε is the machine precision. See Section 4.7 of
Anderson et al. (1999) for further details.

8  Further Comments

The total number of floating point operations is proportional to n2 if JOBZ='N' and is proportional to n3 if JOBZ='V' and RANGE='A', otherwise the number of floating point operations will depend upon the number of computed eigenvectors.

9  Example

This example finds the eigenvalues in the half-open interval 0,5 , and the corresponding eigenvectors, of the symmetric tridiagonal matrix
A = 1 1 0 0 1 4 2 0 0 2 9 3 0 0 3 16 .

9.1  Program Text

Program Text (f08jbfe.f90)

9.2  Program Data

Program Data (f08jbfe.d)

9.3  Program Results

Program Results (f08jbfe.r)


F08JBF (DSTEVX) (PDF version)
F08 Chapter Contents
F08 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2011