F08 Chapter Contents
F08 Chapter Introduction
NAG Library Manual

# NAG Library Routine DocumentF08SCF (DSYGVD)

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

F08SCF (DSYGVD) 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. If eigenvectors are desired, it uses a divide-and-conquer algorithm.

## 2  Specification

 SUBROUTINE F08SCF ( ITYPE, JOBZ, UPLO, N, A, LDA, B, LDB, W, WORK, LWORK, IWORK, LIWORK, INFO)
 INTEGER ITYPE, N, LDA, LDB, LWORK, IWORK(max(1,LIWORK)), LIWORK, 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 dsygvd.

## 3  Description

F08SCF (DSYGVD) 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  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:     A(LDA,$*$) – 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:     LDA – INTEGERInput
On entry: the first dimension of the array A as declared in the (sub)program from which F08SCF (DSYGVD) is called.
Constraint: ${\mathbf{LDA}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$.
7:     B(LDB,$*$) – 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 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: the triangular factor $U$ or $L$ from the Cholesky factorization $B={U}^{\mathrm{T}}U$ or $B=L{L}^{\mathrm{T}}$.
8:     LDB – INTEGERInput
On entry: the first dimension of the array B as declared in the (sub)program from which F08SCF (DSYGVD) is called.
Constraint: ${\mathbf{LDB}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$.
9:     W(N) – REAL (KIND=nag_wp) arrayOutput
On exit: the eigenvalues in ascending order.
10:   WORK($\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{LWORK}}\right)$) – 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:   LWORK – INTEGERInput
On entry: the dimension of the array WORK as declared in the (sub)program from which F08SCF (DSYGVD) is called.
If ${\mathbf{LWORK}}=-1$, a workspace query is assumed; the routine only calculates the optimal size of the WORK array and the minimum size of the IWORK array, returns these values as the first entries of the WORK and IWORK arrays, and no error message related to LWORK or LIWORK is issued.
Suggested value: for optimal performance, LWORK should usually be larger than the minimum, try increasing by $\mathit{nb}×{\mathbf{N}}$, where $\mathit{nb}$ is the optimal block size.
Constraints:
• if ${\mathbf{N}}\le 1$, ${\mathbf{LWORK}}\ge 1$;
• if ${\mathbf{JOBZ}}=\text{'N'}$ and ${\mathbf{N}}>1$, ${\mathbf{LWORK}}\ge 2×{\mathbf{N}}+1$;
• if ${\mathbf{JOBZ}}=\text{'V'}$ and ${\mathbf{N}}>1$, ${\mathbf{LWORK}}\ge 1+6×{\mathbf{N}}+2×{{\mathbf{N}}}^{2}$.
12:   IWORK($\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{LIWORK}}\right)$) – INTEGER arrayWorkspace
On exit: if ${\mathbf{INFO}}={\mathbf{0}}$, ${\mathbf{IWORK}}\left(1\right)$ returns the minimum LIWORK.
13:   LIWORK – INTEGERInput
On entry: the dimension of the array IWORK as declared in the (sub)program from which F08SCF (DSYGVD) is called.
If ${\mathbf{LIWORK}}=-1$, a workspace query is assumed; the routine only calculates the optimal size of the WORK array and the minimum size of the IWORK array, returns these values as the first entries of the WORK and IWORK arrays, and no error message related to LWORK or LIWORK is issued.
Constraints:
• if ${\mathbf{N}}\le 1$, ${\mathbf{LIWORK}}\ge 1$;
• if ${\mathbf{JOBZ}}=\text{'N'}$ and ${\mathbf{N}}>1$, ${\mathbf{LIWORK}}\ge 1$;
• if ${\mathbf{JOBZ}}=\text{'V'}$ and ${\mathbf{N}}>1$, ${\mathbf{LIWORK}}\ge 3+5×{\mathbf{N}}$.
14:   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.
If ${\mathbf{INFO}}=i$, F08FCF (DSYEVD) 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.

The total number of floating point operations is proportional to ${n}^{3}$.
The complex analogue of this routine is F08SQF (ZHEGVD).

## 9  Example

This example finds all the eigenvalues and eigenvectors of the generalized symmetric eigenproblem $ABz=\lambda z$, 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 an estimate of the condition number of $B$, and approximate error bounds for the computed eigenvalues and eigenvectors.
The example program for F08SAF (DSYGV) illustrates solving a generalized symmetric eigenproblem of the form $Az=\lambda Bz$.

### 9.1  Program Text

Program Text (f08scfe.f90)

### 9.2  Program Data

Program Data (f08scfe.d)

### 9.3  Program Results

Program Results (f08scfe.r)