F08SAF (DSYGV) (PDF version)
F08 Chapter Contents
F08 Chapter Introduction
NAG Library Manual

# NAG Library Routine DocumentF08SAF (DSYGV)

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

F08SAF (DSYGV) 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

 SUBROUTINE F08SAF ( ITYPE, JOBZ, UPLO, N, A, LDA, B, LDB, W, WORK, LWORK, INFO)
 INTEGER ITYPE, N, LDA, LDB, LWORK, INFO REAL (KIND=nag_wp) A(LDA,*), B(LDB,*), W(N), WORK(max(1,LWORK)) CHARACTER(1) JOBZ, UPLO
The routine may be called by its LAPACK name dsygv.

## 3  Description

F08SAF (DSYGV) first performs a Cholesky factorization of the matrix $B$ as $B={U}^{\mathrm{T}}U$, when ${\mathbf{UPLO}}=\text{'U'}$ or $B=L{L}^{\mathrm{T}}$, 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
 $ZT A Z = Λ and ZT B Z = I ,$
where $\Lambda$ is the diagonal matrix whose diagonal elements are the eigenvalues. For the problem $ABz=\lambda z$ we correspondingly have
 $Z-1 A Z-T = Λ and ZT B Z = I ,$
and for $BAz=\lambda z$ 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 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  Arguments

1:     $\mathrm{ITYPE}$ – 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:     $\mathrm{JOBZ}$ – 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:     $\mathrm{UPLO}$ – 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:     $\mathrm{N}$ – INTEGERInput
On entry: $n$, the order of the matrices $A$ and $B$.
Constraint: ${\mathbf{N}}\ge 0$.
5:     $\mathrm{A}\left({\mathbf{LDA}},*\right)$ – REAL (KIND=nag_wp) arrayInput/Output
Note: the second dimension of the array A must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$.
On entry: the $n$ by $n$ symmetric matrix $A$.
• If ${\mathbf{UPLO}}=\text{'U'}$, the upper triangular part of $A$ must be stored and the elements of the array below the diagonal are not referenced.
• If ${\mathbf{UPLO}}=\text{'L'}$, the lower triangular part of $A$ must be stored and the elements of the array above the diagonal are not referenced.
On exit: if ${\mathbf{JOBZ}}=\text{'V'}$, A contains the matrix $Z$ of eigenvectors. The eigenvectors are normalized as follows:
• if ${\mathbf{ITYPE}}=1$ or $2$, ${Z}^{\mathrm{T}}BZ=I$;
• if ${\mathbf{ITYPE}}=3$, ${Z}^{\mathrm{T}}{B}^{-1}Z=I$.
If ${\mathbf{JOBZ}}=\text{'N'}$, the upper triangle (if ${\mathbf{UPLO}}=\text{'U'}$) or the lower triangle (if ${\mathbf{UPLO}}=\text{'L'}$) of A, including the diagonal, is overwritten.
6:     $\mathrm{LDA}$ – INTEGERInput
On entry: the first dimension of the array A as declared in the (sub)program from which F08SAF (DSYGV) is called.
Constraint: ${\mathbf{LDA}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$.
7:     $\mathrm{B}\left({\mathbf{LDB}},*\right)$ – REAL (KIND=nag_wp) arrayInput/Output
Note: the second dimension of the array B must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$.
On entry: the $n$ by $n$ symmetric positive definite matrix $B$.
• If ${\mathbf{UPLO}}=\text{'U'}$, the upper triangular part of $B$ must be stored and the elements of the array below the diagonal are not referenced.
• If ${\mathbf{UPLO}}=\text{'L'}$, the lower triangular part of $B$ must be stored and the elements of the array above the diagonal are not referenced.
On exit: if $0\le {\mathbf{INFO}}\le {\mathbf{N}}$, the part of B containing the matrix is overwritten by the triangular factor $U$ or $L$ from the Cholesky factorization $B={U}^{\mathrm{T}}U$ or $B=L{L}^{\mathrm{T}}$.
8:     $\mathrm{LDB}$ – INTEGERInput
On entry: the first dimension of the array B as declared in the (sub)program from which F08SAF (DSYGV) is called.
Constraint: ${\mathbf{LDB}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$.
9:     $\mathrm{W}\left({\mathbf{N}}\right)$ – REAL (KIND=nag_wp) arrayOutput
On exit: the eigenvalues in ascending order.
10:   $\mathrm{WORK}\left(\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{LWORK}}\right)\right)$ – REAL (KIND=nag_wp) arrayWorkspace
On exit: if ${\mathbf{INFO}}={\mathbf{0}}$, ${\mathbf{WORK}}\left(1\right)$ contains the minimum value of LWORK required for optimal performance.
11:   $\mathrm{LWORK}$ – INTEGERInput
On entry: the dimension of the array WORK as declared in the (sub)program from which F08SAF (DSYGV) is called.
If ${\mathbf{LWORK}}=-1$, 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, ${\mathbf{LWORK}}\ge \left(\mathit{nb}+2\right)×{\mathbf{N}}$, where $\mathit{nb}$ is the optimal block size for F08FEF (DSYTRD).
Constraint: ${\mathbf{LWORK}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,3×{\mathbf{N}}-1\right)$.
12:   $\mathrm{INFO}$ – INTEGEROutput
On exit: ${\mathbf{INFO}}=0$ unless the routine detects an error (see Section 6).

## 6  Error Indicators and Warnings

${\mathbf{INFO}}<0$
If ${\mathbf{INFO}}=-i$, argument $i$ had an illegal value. An explanatory message is output, and execution of the program is terminated.
${\mathbf{INFO}}=1 \text{to} {\mathbf{N}}$
If ${\mathbf{INFO}}=i$, F08FAF (DSYEV) failed to converge; $i$ $i$ off-diagonal elements of an intermediate tridiagonal form did not converge to zero.
${\mathbf{INFO}}>{\mathbf{N}}$
F07FDF (DPOTRF) returned an error code; i.e., 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.

## 8  Parallelism and Performance

F08SAF (DSYGV) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
F08SAF (DSYGV) 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 ${n}^{3}$.
The complex analogue of this routine is F08SNF (ZHEGV).

## 10  Example

This example finds all the eigenvalues and eigenvectors of the generalized symmetric eigenproblem $Az=\lambda Bz$, 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 (DSYGVD) illustrates solving a generalized symmetric eigenproblem of the form $ABz=\lambda z$.

### 10.1  Program Text

Program Text (f08safe.f90)

### 10.2  Program Data

Program Data (f08safe.d)

### 10.3  Program Results

Program Results (f08safe.r)

F08SAF (DSYGV) (PDF version)
F08 Chapter Contents
F08 Chapter Introduction
NAG Library Manual