F08JGF (DPTEQR) (PDF version)
F08 Chapter Contents
F08 Chapter Introduction
NAG Library Manual

NAG Library Routine Document

F08JGF (DPTEQR)

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.
Warning. The specification of the parameter WORK changed at Mark 20: the length of WORK needs to be increased.

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

F08JGF (DPTEQR) computes all the eigenvalues and, optionally, all the eigenvectors of a real symmetric positive definite tridiagonal matrix, or of a real symmetric positive definite matrix which has been reduced to tridiagonal form.

2  Specification

SUBROUTINE F08JGF ( COMPZ, N, D, E, Z, LDZ, WORK, INFO)
INTEGER  N, LDZ, INFO
REAL (KIND=nag_wp)  D(*), E(*), Z(LDZ,*), WORK(4*N)
CHARACTER(1)  COMPZ
The routine may be called by its LAPACK name dpteqr.

3  Description

F08JGF (DPTEQR) computes all the eigenvalues and, optionally, all the eigenvectors of a real symmetric positive definite tridiagonal matrix T. In other words, it can compute the spectral factorization of T as
T=ZΛZT,
where Λ is a diagonal matrix whose diagonal elements are the eigenvalues λi, and Z is the orthogonal matrix whose columns are the eigenvectors zi. Thus
Tzi=λizi,  i=1,2,,n.
The routine may also be used to compute all the eigenvalues and eigenvectors of a real symmetric positive definite matrix A which has been reduced to tridiagonal form T:
A =QTQT, where ​Q​ is orthogonal =QZΛQZT.
In this case, the matrix Q must be formed explicitly and passed to F08JGF (DPTEQR), which must be called with COMPZ='V'. The routines which must be called to perform the reduction to tridiagonal form and form Q are:
full matrix F08FEF (DSYTRD) and F08FFF (DORGTR)
full matrix, packed storage F08GEF (DSPTRD) and F08GFF (DOPGTR)
band matrix F08HEF (DSBTRD) with VECT='V'.
F08JGF (DPTEQR) first factorizes T as LDLT where L is unit lower bidiagonal and D is diagonal. It forms the bidiagonal matrix B=LD12, and then calls F08MEF (DBDSQR) to compute the singular values of B which are the same as the eigenvalues of T. The method used by the routine allows high relative accuracy to be achieved in the small eigenvalues of T. The eigenvectors are normalized so that zi2=1, but are determined only to within a factor ±1.

4  References

Barlow J and Demmel J W (1990) Computing accurate eigensystems of scaled diagonally dominant matrices SIAM J. Numer. Anal. 27 762–791

5  Parameters

1:     COMPZ – CHARACTER(1)Input
On entry: indicates whether the eigenvectors are to be computed.
COMPZ='N'
Only the eigenvalues are computed (and the array Z is not referenced).
COMPZ='I'
The eigenvalues and eigenvectors of T are computed (and the array Z is initialized by the routine).
COMPZ='V'
The eigenvalues and eigenvectors of A are computed (and the array Z must contain the matrix Q on entry).
Constraint: COMPZ='N', 'V' or 'I'.
2:     N – INTEGERInput
On entry: n, the order of the matrix T.
Constraint: N0.
3:     D(*) – REAL (KIND=nag_wp) arrayInput/Output
Note: the dimension of the array D must be at least max1,N.
On entry: the diagonal elements of the tridiagonal matrix T.
On exit: the n eigenvalues in descending order, unless INFO>0, in which case D is overwritten.
4:     E(*) – REAL (KIND=nag_wp) arrayInput/Output
Note: the dimension of the array E must be at least max1,N-1.
On entry: the off-diagonal elements of the tridiagonal matrix T.
On exit: E is overwritten.
5:     Z(LDZ,*) – REAL (KIND=nag_wp) arrayInput/Output
Note: the second dimension of the array Z must be at least max1,N if COMPZ='V' or 'I' and at least 1 if COMPZ='N'.
On entry: if COMPZ='V', Z must contain the orthogonal matrix Q from the reduction to tridiagonal form.
If COMPZ='I', Z need not be set.
On exit: if COMPZ='I' or 'V', the n required orthonormal eigenvectors stored as columns of Z; the ith column corresponds to the ith eigenvalue, where i=1,2,,n, unless INFO>0.
If COMPZ='N', Z is not referenced.
6:     LDZ – INTEGERInput
On entry: the first dimension of the array Z as declared in the (sub)program from which F08JGF (DPTEQR) is called.
Constraints:
  • if COMPZ='V' or 'I', LDZmax1,N;
  • if COMPZ='N', LDZ1.
7:     WORK(4×N) – REAL (KIND=nag_wp) arrayWorkspace
8:     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
If INFO=i, the leading minor of order i is not positive definite and the Cholesky factorization of T could not be completed. Hence T itself is not positive definite.
If INFO=N+i, the algorithm to compute the singular values of the Cholesky factor B failed to converge; i off-diagonal elements did not converge to zero.

7  Accuracy

The eigenvalues and eigenvectors of T are computed to high relative accuracy which means that if they vary widely in magnitude, then any small eigenvalues (and corresponding eigenvectors) will be computed more accurately than, for example, with the standard QR method. However, the reduction to tridiagonal form (prior to calling the routine) may exclude the possibility of obtaining high relative accuracy in the small eigenvalues of the original matrix if its eigenvalues vary widely in magnitude.
To be more precise, let H be the tridiagonal matrix defined by H=DTD, where D is diagonal with dii = t ii -12 , and hii = 1  for all i. If λi is an exact eigenvalue of T and λ~i is the corresponding computed value, then
λ~i - λi c n ε κ2 H λi
where cn is a modestly increasing function of n, ε is the machine precision, and κ2H is the condition number of H with respect to inversion defined by: κ2H=H·H-1.
If zi is the corresponding exact eigenvector of T, and z~i is the corresponding computed eigenvector, then the angle θz~i,zi between them is bounded as follows:
θ z~i,zi c n ε κ2 H relgapi
where relgapi is the relative gap between λi and the other eigenvalues, defined by
relgapi = min ij λi - λj λi + λj .

8  Further Comments

The total number of floating point operations is typically about 30n2 if COMPZ='N' and about 6n3 if COMPZ='V' or 'I', but depends on how rapidly the algorithm converges. When COMPZ='N', the operations are all performed in scalar mode; the additional operations to compute the eigenvectors when COMPZ='V' or 'I' can be vectorized and on some machines may be performed much faster.
The complex analogue of this routine is F08JUF (ZPTEQR).

9  Example

This example computes all the eigenvalues and eigenvectors of the symmetric positive definite tridiagonal matrix T, where
T = 4.16 3.17 0.00 0.00 3.17 5.25 -0.97 0.00 0.00 -0.97 1.09 0.55 0.00 0.00 0.55 0.62 .

9.1  Program Text

Program Text (f08jgfe.f90)

9.2  Program Data

Program Data (f08jgfe.d)

9.3  Program Results

Program Results (f08jgfe.r)


F08JGF (DPTEQR) (PDF version)
F08 Chapter Contents
F08 Chapter Introduction
NAG Library Manual

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