NAG FL Interface
f08juf (zpteqr)

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1 Purpose

f08juf computes all the eigenvalues and, optionally, all the eigenvectors of a complex Hermitian positive definite matrix which has been reduced to tridiagonal form.

2 Specification

Fortran Interface
Subroutine f08juf ( compz, n, d, e, z, ldz, work, info)
Integer, Intent (In) :: n, ldz
Integer, Intent (Out) :: info
Real (Kind=nag_wp), Intent (Inout) :: d(*), e(*)
Real (Kind=nag_wp), Intent (Out) :: work(4*n)
Complex (Kind=nag_wp), Intent (Inout) :: z(ldz,*)
Character (1), Intent (In) :: compz
C Header Interface
#include <nag.h>
void  f08juf_ (const char *compz, const Integer *n, double d[], double e[], Complex z[], const Integer *ldz, double work[], Integer *info, const Charlen length_compz)
The routine may be called by the names f08juf, nagf_lapackeig_zpteqr or its LAPACK name zpteqr.

3 Description

f08juf 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 stores the real orthogonal matrix Z in a complex array, so that it may be used to compute all the eigenvalues and eigenvectors of a complex Hermitian positive definite matrix A which has been reduced to tridiagonal form T:
A =QTQH, where ​Q​ is unitary =(QZ)Λ(QZ)H.  
In this case, the matrix Q must be formed explicitly and passed to f08juf, 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 f08fsf and f08ftf
full matrix, packed storage f08gsf and f08gtf
band matrix f08hsf with vect='V'.
f08juf first factorizes T as LDLH where L is unit lower bidiagonal and D is diagonal. It forms the bidiagonal matrix B=LD12, and then calls f08msf 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 complex factor of absolute value 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 Arguments

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='V'
The eigenvalues and eigenvectors of A are computed (and the array z must contain the matrix Q on entry).
compz='I'
The eigenvalues and eigenvectors of T are computed (and the array z is initialized by the routine).
Constraint: compz='N', 'V' or 'I'.
2: n Integer Input
On entry: n, the order of the matrix T.
Constraint: n0.
3: d(*) Real (Kind=nag_wp) array Input/Output
Note: the dimension of the array d must be at least max(1,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) array Input/Output
Note: the dimension of the array e must be at least max(1,n-1).
On entry: the off-diagonal elements of the tridiagonal matrix T.
On exit: e is overwritten.
5: z(ldz,*) Complex (Kind=nag_wp) array Input/Output
Note: the second dimension of the array z must be at least max(1,n) if compz='V' or 'I' and at least 1 if compz='N'.
On entry: if compz='V', z must contain the unitary matrix Q from the reduction to tridiagonal form.
If compz='I', z need not be set.
On exit: if compz='V' or 'I', 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 Integer Input
On entry: the first dimension of the array z as declared in the (sub)program from which f08juf is called.
Constraints:
  • if compz='V' or 'I', ldz max(1,n) ;
  • if compz='N', ldz1.
7: work(4×n) Real (Kind=nag_wp) array Workspace
8: info Integer Output
On exit: info=0 unless the routine detects an error (see Section 6).

6 Error Indicators and Warnings

info<0
If info=-i, argument i had an illegal value. An explanatory message is output, and execution of the program is terminated.
info=1,,n
The leading minor of order value is not positive definite and the Cholesky factorization of T could not be completed. Hence T itself is not positive definite.
info>n
The algorithm to compute the singular values of the Cholesky factor B failed to converge; value 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 c(n) is a modestly increasing function of n, ε is the machine precision, and κ2(H) is the condition number of H with respect to inversion defined by: κ2(H)=H·H-1.
If zi is the corresponding exact eigenvector of T, and z~i is the corresponding computed eigenvector, then the angle θ(z~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 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.
f08juf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f08juf 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 routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

The total number of real floating-point operations is typically about 30n2 if compz='N' and about 12n3 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 real analogue of this routine is f08jgf.

10 Example

This example computes all the eigenvalues and eigenvectors of the complex Hermitian positive definite matrix A, where
A = ( 6.02+0.00i -0.45+0.25i -1.30+1.74i 1.45-0.66i -0.45-0.25i 2.91+0.00i 0.05+1.56i -1.04+1.27i -1.30-1.74i 0.05-1.56i 3.29+0.00i 0.14+1.70i 1.45+0.66i -1.04-1.27i 0.14-1.70i 4.18+0.00i ) .  

10.1 Program Text

Program Text (f08jufe.f90)

10.2 Program Data

Program Data (f08jufe.d)

10.3 Program Results

Program Results (f08jufe.r)