NAG FL Interface
f08saf (dsygv)

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1 Purpose

f08saf computes all the eigenvalues and, optionally, the eigenvectors of a real generalized symmetric-definite eigenproblem, of the form
Az=λBz ,   ABz=λz   or   BAz=λz ,  
where A and B are symmetric and B is also positive definite.

2 Specification

Fortran Interface
Subroutine f08saf ( itype, jobz, uplo, n, a, lda, b, ldb, w, work, lwork, info)
Integer, Intent (In) :: itype, n, lda, ldb, lwork
Integer, Intent (Out) :: info
Real (Kind=nag_wp), Intent (Inout) :: a(lda,*), b(ldb,*)
Real (Kind=nag_wp), Intent (Out) :: w(n), work(max(1,lwork))
Character (1), Intent (In) :: jobz, uplo
C Header Interface
#include <nag.h>
void  f08saf_ (const Integer *itype, const char *jobz, const char *uplo, const Integer *n, double a[], const Integer *lda, double b[], const Integer *ldb, double w[], double work[], const Integer *lwork, Integer *info, const Charlen length_jobz, const Charlen length_uplo)
The routine may be called by the names f08saf, nagf_lapackeig_dsygv or its LAPACK name dsygv.

3 Description

f08saf first performs a Cholesky factorization of the matrix B as B=UTU , when uplo='U' or B=LLT , when uplo='L'. The generalized problem is then reduced to a standard symmetric eigenvalue problem
Cx=λx ,  
which is solved for the eigenvalues and, optionally, the eigenvectors; the eigenvectors are then backtransformed to give the eigenvectors of the original problem.
For the problem Az=λBz , the eigenvectors are normalized so that the matrix of eigenvectors, z, satisfies
ZT A Z = Λ   and   ZT B Z = I ,  
where Λ is the diagonal matrix whose diagonal elements are the eigenvalues. For the problem A B z = λ z we correspondingly have
Z-1 A Z-T = Λ   and   ZT B Z = I ,  
and for B A z = λ z we have
ZT A Z = Λ   and   ZT B-1 Z = I .  

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
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

5 Arguments

1: itype Integer Input
On entry: specifies the problem type to be solved.
itype=1
Az=λBz.
itype=2
ABz=λz.
itype=3
BAz=λz.
Constraint: itype=1, 2 or 3.
2: jobz Character(1) Input
On entry: indicates whether eigenvectors are computed.
jobz='N'
Only eigenvalues are computed.
jobz='V'
Eigenvalues and eigenvectors are computed.
Constraint: jobz='N' or 'V'.
3: uplo Character(1) Input
On entry: if uplo='U', the upper triangles of A and B are stored.
If uplo='L', the lower triangles of A and B are stored.
Constraint: uplo='U' or 'L'.
4: n Integer Input
On entry: n, the order of the matrices A and B.
Constraint: n0.
5: a(lda,*) Real (Kind=nag_wp) array Input/Output
Note: the second dimension of the array a must be at least max(1,n).
On entry: the n×n symmetric matrix A.
  • If uplo='U', the upper triangular part of A must be stored and the elements of the array below the diagonal are not referenced.
  • If uplo='L', the lower triangular part of A must be stored and the elements of the array above the diagonal are not referenced.
On exit: if jobz='V', a contains the matrix Z of eigenvectors. The eigenvectors are normalized as follows:
  • if itype=1 or 2, ZTBZ=I;
  • if itype=3, ZTB-1Z=I.
If jobz='N', the upper triangle (if uplo='U') or the lower triangle (if uplo='L') of a, including the diagonal, is overwritten.
6: lda Integer Input
On entry: the first dimension of the array a as declared in the (sub)program from which f08saf is called.
Constraint: ldamax(1,n).
7: b(ldb,*) Real (Kind=nag_wp) array Input/Output
Note: the second dimension of the array b must be at least max(1,n).
On entry: the n×n symmetric positive definite matrix B.
  • If uplo='U', the upper triangular part of B must be stored and the elements of the array below the diagonal are not referenced.
  • If uplo='L', the lower triangular part of B must be stored and the elements of the array above the diagonal are not referenced.
On exit: if 0infon, the part of b containing the matrix is overwritten by the triangular factor U or L from the Cholesky factorization B=UTU or B=LLT.
8: ldb Integer Input
On entry: the first dimension of the array b as declared in the (sub)program from which f08saf is called.
Constraint: ldbmax(1,n).
9: w(n) Real (Kind=nag_wp) array Output
On exit: the eigenvalues in ascending order.
10: work(max(1,lwork)) Real (Kind=nag_wp) array Workspace
On exit: if info=0, work(1) contains the minimum value of lwork required for optimal performance.
11: lwork Integer Input
On entry: the dimension of the array work as declared in the (sub)program from which f08saf is called.
If lwork=−1, 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.
Suggested value: for optimal performance, lwork(nb+2)×n, where nb is the optimal block size for f08fef.
Constraint: lworkmax(1,3×n-1) or lwork=−1.
12: 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 algorithm failed to converge; value off-diagonal elements of an intermediate tridiagonal form did not converge to zero.
info>n
If info=n+value, for 1valuen, then the leading minor of order value of B is not positive definite. The factorization of B could not be completed and no eigenvalues or eigenvectors were computed.

7 Accuracy

If B is ill-conditioned with respect to inversion, then the error bounds for the computed eigenvalues and vectors may be large, although when the diagonal elements of B differ widely in magnitude the eigenvalues and eigenvectors may be less sensitive than the condition of B would suggest. See Section 4.10 of Anderson et al. (1999) for details of the error bounds.
The example program below illustrates the computation of approximate error bounds.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.
f08saf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f08saf 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 floating-point operations is proportional to n3.
The complex analogue of this routine is f08snf.

10 Example

This example finds all the eigenvalues and eigenvectors of the generalized symmetric eigenproblem Az=λBz, where
A = ( 0.24 0.39 0.42 -0.16 0.39 -0.11 0.79 0.63 0.42 0.79 -0.25 0.48 -0.16 0.63 0.48 -0.03 )   and   B= ( 4.16 -3.12 0.56 -0.10 -3.12 5.03 -0.83 1.09 0.56 -0.83 0.76 0.34 -0.10 1.09 0.34 1.18 ) ,  
together with and estimate of the condition number of B, and approximate error bounds for the computed eigenvalues and eigenvectors.
The example program for f08scf illustrates solving a generalized symmetric eigenproblem of the form ABz=λz.

10.1 Program Text

Program Text (f08safe.f90)

10.2 Program Data

Program Data (f08safe.d)

10.3 Program Results

Program Results (f08safe.r)