nag_lapack_dsygv (f08sa) computes all the eigenvalues and, optionally, the eigenvectors of a real generalized symmetric-definite eigenproblem, of the form

A z = λ B z, A B z = λ z or B A z = λ z,

where

A

and

B

are symmetric and

B

is also positive definite.

Syntax

[a, b, w, info] = f08sa(itype, jobz, uplo, a, b, 'n', n)

[a, b, w, info] = nag_lapack_dsygv(itype, jobz, uplo, a, b, 'n', n)

Description

nag_lapack_dsygv (f08sa) first performs a Cholesky factorization of the matrix

B

B = U^{T} U

, when

uplo ='U'

B = L L^{T}

, when

uplo ='L'

. The generalized problem is then reduced to a standard symmetric eigenvalue problem

C x = λ x,

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

A z = λ B z

, the eigenvectors are normalized so that the matrix of eigenvectors,

z

, satisfies

Z^{T} A Z = Λ and Z^{T} 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 Z^{T} B Z = I,

and for

B A z = λ z

we have

Z^{T} A Z = Λ and Z^{T} B^{- 1} Z = I .

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

Parameters

Compulsory Input Parameters

1: $itype$ – int64int32nag_int scalar

Specifies the problem type to be solved.

$itype = 1$: $A z = λ B z$ .
$itype = 2$: $A B z = λ z$ .
$itype = 3$: $B A z = λ z$ .

Constraint:

itype = 1

2

3

2: $jobz$ – string (length ≥ 1)

Indicates whether eigenvectors are computed.

$jobz ='N'$: Only eigenvalues are computed.
$jobz ='V'$: Eigenvalues and eigenvectors are computed.

Constraint:

jobz ='N'

'V'

3: $uplo$ – string (length ≥ 1)

uplo ='U'

, the upper triangles of

A

and

B

are stored.

uplo ='L'

, the lower triangles of

A

and

B

are stored.

Constraint:

uplo ='U'

'L'

4: $a (lda :)$ – double array

The first dimension of the array a must be at least

\max (1, n)

The second dimension of the array a must be at least

\max (1, n)

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.

5: $b (ldb :)$ – double array

The first dimension of the array b must be at least

\max (1, n)

The second dimension of the array b must be at least

\max (1, n)

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.

Optional Input Parameters

1: $n$ – int64int32nag_int scalar: Default: the first dimension of the arrays a, b and the second dimension of the arrays a, b. (An error is raised if these dimensions are not equal.)
$n$ , the order of the matrices $A$ and $B$ .

Constraint: $n \geq 0$ .

Output Parameters

1: $a (lda :)$ – double array

The first dimension of the array a will be

\max (1, n)

The second dimension of the array a will be

\max (1, n)

jobz ='V'

, a contains the matrix

Z

of eigenvectors. The eigenvectors are normalized as follows:

if $itype = 1$ or $2$ , $Z^{T} B Z = I$ ;
if $itype = 3$ , $Z^{T} B^{- 1} Z = I$ .

jobz ='N'

, the upper triangle (if

uplo ='U'

) or the lower triangle (if

uplo ='L'

) of a, including the diagonal, is overwritten.

2: $b (ldb :)$ – double array

The first dimension of the array b will be

\max (1, n)

The second dimension of the array b will be

\max (1, n)

0 \leq info \leq n

, the part of b containing the matrix stores the triangular factor

U

L

from the Cholesky factorization

B = U^{T} U

B = L L^{T}

3: $w (n)$ – double array

The eigenvalues in ascending order.

4: $info$ – int64int32nag_int scalar

info = 0

unless the function detects an error (see Error Indicators and Warnings).

Error Indicators and Warnings

$info = - i$: If $info = - i$ , parameter $i$ had an illegal value on entry. The parameters are numbered as follows:
1: itype, 2: jobz, 3: uplo, 4: n, 5: a, 6: lda, 7: b, 8: ldb, 9: w, 10: work, 11: lwork, 12: info.
It is possible that info refers to a parameter that is omitted from the MATLAB interface. This usually indicates that an error in one of the other input parameters has caused an incorrect value to be inferred.

$info = 1 to n$: If $info = i$ , nag_lapack_dsyev (f08fa) failed to converge; $i$ $i$ off-diagonal elements of an intermediate tridiagonal form did not converge to zero.

$info > n$: nag_lapack_dpotrf (f07fd) returned an error code; i.e., if $info = n + i$ , for $1 \leq i \leq n$ , 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.

Accuracy

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.

Further Comments

The total number of floating-point operations is proportional to

n^{3}

The complex analogue of this function is nag_lapack_zhegv (f08sn).

Example

This example finds all the eigenvalues and eigenvectors of the generalized symmetric eigenproblem

A z = λ B z

, where

A = (\begin{array}{r} 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 \end{array}) and B = (\begin{matrix} 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 \end{matrix}),

together with and estimate of the condition number of

B

, and approximate error bounds for the computed eigenvalues and eigenvectors.

The example program for nag_lapack_dsygvd (f08sc) illustrates solving a generalized symmetric eigenproblem of the form

A B z = λ z

Open in the MATLAB editor: f08sa_example

function f08sa_example


fprintf('f08sa example results\n\n');

% Upper triangular parts of symmetric matrix A and symmetric definite matrix B
uplo = 'Upper';
n = 4;
a = [0.24,  0.39,  0.42, -0.16;
     0,    -0.11,  0.79,  0.63;
     0,     0,    -0.25,  0.48;
     0,     0,     0,    -0.03];
b = [4.16, -3.12,  0.56, -0.10;
     0,     5.03, -0.83,  1.09;
     0,     0,     0.76,  0.34;
     0,     0,     0,     1.18];

% Generalized eigenvalues and eigenvectors for problem Az = lambda Bz
itype = int64(1);
jobz = 'Vectors';
[Z, U, w, info] = f08sa( ...
			 itype, jobz, uplo, a, b);

% Normalize eigenvectors: largest element positive (with z'Bz = I)
for j = 1:n
  [~,k] = max(abs(Z(:,j)));
  if Z(k,j) < 0
    Z(:,j) = -Z(:,j);
  end
end                            

disp('Eigenvalues');
disp(w');
disp('Eigenvectors');
disp(Z);

f08sa example results

Eigenvalues
   -2.2254   -0.4548    0.1001    1.1270

Eigenvectors
    0.0690   -0.3080   -0.4469    0.5528
    0.5740   -0.5329   -0.0371    0.6766
    1.5428    0.3496    0.0505    0.9276
   -1.4004    0.6211    0.4743   -0.2510

PDF version (NAG web site, 64-bit version, 64-bit version)

Chapter Contents

Chapter Introduction

NAG Toolbox