F08 Chapter Contents
F08 Chapter Introduction
NAG Library Manual

# NAG Library Routine DocumentF08TNF (ZHPGV)

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.

## 1  Purpose

F08TNF (ZHPGV) computes all the eigenvalues and, optionally, all the eigenvectors of a complex generalized Hermitian-definite eigenproblem, of the form
 $Az=λBz , ABz=λz or BAz=λz ,$
where $A$ and $B$ are Hermitian, stored in packed format, and $B$ is also positive definite.

## 2  Specification

 SUBROUTINE F08TNF ( ITYPE, JOBZ, UPLO, N, AP, BP, W, Z, LDZ, WORK, RWORK, INFO)
 INTEGER ITYPE, N, LDZ, INFO REAL (KIND=nag_wp) W(N), RWORK(3*N-2) COMPLEX (KIND=nag_wp) AP(*), BP(*), Z(LDZ,*), WORK(2*N-1) CHARACTER(1) JOBZ, UPLO
The routine may be called by its LAPACK name zhpgv.

## 3  Description

F08TNF (ZHPGV) first performs a Cholesky factorization of the matrix $B$ as $B={U}^{\mathrm{H}}U$, when ${\mathbf{UPLO}}=\text{'U'}$ or $B=L{L}^{\mathrm{H}}$, when ${\mathbf{UPLO}}=\text{'L'}$. 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 $Az=\lambda Bz$, the eigenvectors are normalized so that the matrix of eigenvectors, $Z$, satisfies
 $ZH A Z = Λ and ZH B Z = I ,$
where $\Lambda$ is the diagonal matrix whose diagonal elements are the eigenvalues. For the problem $ABz=\lambda z$ we correspondingly have
 $Z-1 A Z-H = Λ and ZH B Z = I ,$
and for $BAz=\lambda z$ we have
 $ZH A Z = Λ and ZH B-1 Z = I .$

## 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 http://www.netlib.org/lapack/lug
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

## 5  Parameters

1:     ITYPE – INTEGERInput
On entry: specifies the problem type to be solved.
${\mathbf{ITYPE}}=1$
$Az=\lambda Bz$.
${\mathbf{ITYPE}}=2$
$ABz=\lambda z$.
${\mathbf{ITYPE}}=3$
$BAz=\lambda z$.
Constraint: ${\mathbf{ITYPE}}=1$, $2$ or $3$.
2:     JOBZ – CHARACTER(1)Input
On entry: indicates whether eigenvectors are computed.
${\mathbf{JOBZ}}=\text{'N'}$
Only eigenvalues are computed.
${\mathbf{JOBZ}}=\text{'V'}$
Eigenvalues and eigenvectors are computed.
Constraint: ${\mathbf{JOBZ}}=\text{'N'}$ or $\text{'V'}$.
3:     UPLO – CHARACTER(1)Input
On entry: if ${\mathbf{UPLO}}=\text{'U'}$, the upper triangles of $A$ and $B$ are stored.
If ${\mathbf{UPLO}}=\text{'L'}$, the lower triangles of $A$ and $B$ are stored.
Constraint: ${\mathbf{UPLO}}=\text{'U'}$ or $\text{'L'}$.
4:     N – INTEGERInput
On entry: $n$, the order of the matrices $A$ and $B$.
Constraint: ${\mathbf{N}}\ge 0$.
5:     AP($*$) – COMPLEX (KIND=nag_wp) arrayInput/Output
Note: the dimension of the array AP must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}×\left({\mathbf{N}}+1\right)/2\right)$.
On entry: the upper or lower triangle of the $n$ by $n$ Hermitian matrix $A$, packed by columns.
More precisely,
• if ${\mathbf{UPLO}}=\text{'U'}$, the upper triangle of $A$ must be stored with element ${A}_{ij}$ in ${\mathbf{AP}}\left(i+j\left(j-1\right)/2\right)$ for $i\le j$;
• if ${\mathbf{UPLO}}=\text{'L'}$, the lower triangle of $A$ must be stored with element ${A}_{ij}$ in ${\mathbf{AP}}\left(i+\left(2n-j\right)\left(j-1\right)/2\right)$ for $i\ge j$.
On exit: the contents of AP are destroyed.
6:     BP($*$) – COMPLEX (KIND=nag_wp) arrayInput/Output
Note: the dimension of the array BP must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}×\left({\mathbf{N}}+1\right)/2\right)$.
On entry: the upper or lower triangle of the $n$ by $n$ Hermitian matrix $B$, packed by columns.
More precisely,
• if ${\mathbf{UPLO}}=\text{'U'}$, the upper triangle of $B$ must be stored with element ${B}_{ij}$ in ${\mathbf{BP}}\left(i+j\left(j-1\right)/2\right)$ for $i\le j$;
• if ${\mathbf{UPLO}}=\text{'L'}$, the lower triangle of $B$ must be stored with element ${B}_{ij}$ in ${\mathbf{BP}}\left(i+\left(2n-j\right)\left(j-1\right)/2\right)$ for $i\ge j$.
On exit: the triangular factor $U$ or $L$ from the Cholesky factorization $B={U}^{\mathrm{H}}U$ or $B=L{L}^{\mathrm{H}}$, in the same storage format as $B$.
7:     W(N) – REAL (KIND=nag_wp) arrayOutput
On exit: the eigenvalues in ascending order.
8:     Z(LDZ,$*$) – COMPLEX (KIND=nag_wp) arrayOutput
Note: the second dimension of the array Z must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$ if ${\mathbf{JOBZ}}=\text{'V'}$, and at least $1$ otherwise.
On exit: if ${\mathbf{JOBZ}}=\text{'V'}$, Z contains the matrix $Z$ of eigenvectors. The eigenvectors are normalized as follows:
• if ${\mathbf{ITYPE}}=1$ or $2$, ${Z}^{\mathrm{H}}BZ=I$;
• if ${\mathbf{ITYPE}}=3$, ${Z}^{\mathrm{H}}{B}^{-1}Z=I$.
If ${\mathbf{JOBZ}}=\text{'N'}$, Z is not referenced.
9:     LDZ – INTEGERInput
On entry: the first dimension of the array Z as declared in the (sub)program from which F08TNF (ZHPGV) is called.
Constraints:
• if ${\mathbf{JOBZ}}=\text{'V'}$, ${\mathbf{LDZ}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$;
• otherwise ${\mathbf{LDZ}}\ge 1$.
10:   WORK($2×{\mathbf{N}}-1$) – COMPLEX (KIND=nag_wp) arrayWorkspace
11:   RWORK($3×{\mathbf{N}}-2$) – REAL (KIND=nag_wp) arrayWorkspace
12:   INFO – INTEGEROutput
On exit: ${\mathbf{INFO}}=0$ unless the routine detects an error (see Section 6).

## 6  Error Indicators and Warnings

Errors or warnings detected by the routine:
${\mathbf{INFO}}<0$
If ${\mathbf{INFO}}=-i$, argument $i$ had an illegal value. An explanatory message is output, and execution of the program is terminated.
${\mathbf{INFO}}>0$
F07GRF (ZPPTRF) or F08GNF (ZHPEV) returned an error code:
 $\le {\mathbf{N}}$ if ${\mathbf{INFO}}=i$, F08GNF (ZHPEV) failed to converge; $i$ off-diagonal elements of an intermediate tridiagonal form did not converge to zero; $>{\mathbf{N}}$ if ${\mathbf{INFO}}={\mathbf{N}}+i$, for $1\le i\le {\mathbf{N}}$, then the leading minor of order $i$ 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.

The total number of floating point operations is proportional to ${n}^{3}$.
The real analogue of this routine is F08TAF (DSPGV).

## 9  Example

This example finds all the eigenvalues and eigenvectors of the generalized Hermitian eigenproblem $Az=\lambda Bz$, where
 $A = -7.36i+0.00 0.77-0.43i -0.64-0.92i 3.01-6.97i 0.77+0.43i 3.49i+0.00 2.19+4.45i 1.90+3.73i -0.64+0.92i 2.19-4.45i 0.12i+0.00 2.88-3.17i 3.01+6.97i 1.90-3.73i 2.88+3.17i -2.54i+0.00$
and
 $B = 3.23i+0.00 1.51-1.92i 1.90+0.84i 0.42+2.50i 1.51+1.92i 3.58i+0.00 -0.23+1.11i -1.18+1.37i 1.90-0.84i -0.23-1.11i 4.09i+0.00 2.33-0.14i 0.42-2.50i -1.18-1.37i 2.33+0.14i 4.29i+0.00 ,$
together with an estimate of the condition number of $B$, and approximate error bounds for the computed eigenvalues and eigenvectors.
The example program for F08TQF (ZHPGVD) illustrates solving a generalized symmetric eigenproblem of the form $ABz=\lambda z$.

### 9.1  Program Text

Program Text (f08tnfe.f90)

### 9.2  Program Data

Program Data (f08tnfe.d)

### 9.3  Program Results

Program Results (f08tnfe.r)