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NAG Toolbox: nag_lapack_zhbev (f08hn)
Purpose
nag_lapack_zhbev (f08hn) computes all the eigenvalues and, optionally, all the eigenvectors of a complex by Hermitian band matrix of bandwidth .
Syntax
Description
The Hermitian band matrix is first reduced to real tridiagonal form, using unitary similarity transformations, and then the algorithm is applied to the tridiagonal matrix to compute the eigenvalues and (optionally) the eigenvectors.
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:
– string (length ≥ 1)
-
Indicates whether eigenvectors are computed.
- Only eigenvalues are computed.
- Eigenvalues and eigenvectors are computed.
Constraint:
or .
- 2:
– string (length ≥ 1)
-
If
, the upper triangular part of
is stored.
If , the lower triangular part of is stored.
Constraint:
or .
- 3:
– int64int32nag_int scalar
-
If
, the number of superdiagonals,
, of the matrix
.
If , the number of subdiagonals, , of the matrix .
Constraint:
.
- 4:
– complex array
-
The first dimension of the array
ab must be at least
.
The second dimension of the array
ab must be at least
.
The upper or lower triangle of the
by
Hermitian band matrix
.
The matrix is stored in rows
to
, more precisely,
- if , the elements of the upper triangle of within the band must be stored with element in ;
- if , the elements of the lower triangle of within the band must be stored with element in
Optional Input Parameters
- 1:
– int64int32nag_int scalar
-
Default:
the second dimension of the array
ab.
, the order of the matrix .
Constraint:
.
Output Parameters
- 1:
– complex array
-
The first dimension of the array
ab will be
.
The second dimension of the array
ab will be
.
ab stores values generated during the reduction to tridiagonal form.
The first superdiagonal or subdiagonal and the diagonal of the tridiagonal matrix
are returned in
ab using the same storage format as described above.
- 2:
– double array
-
The eigenvalues in ascending order.
- 3:
– complex array
-
The first dimension,
, of the array
z will be
- if , ;
- otherwise .
The second dimension of the array
z will be
if
and
otherwise.
If
,
z contains the orthonormal eigenvectors of the matrix
, with the
th column of
holding the eigenvector associated with
.
If
,
z is not referenced.
- 4:
– int64int32nag_int scalar
unless the function detects an error (see
Error Indicators and Warnings).
Error Indicators and Warnings
-
If , parameter had an illegal value on entry. The parameters are numbered as follows:
1:
jobz, 2:
uplo, 3:
n, 4:
kd, 5:
ab, 6:
ldab, 7:
w, 8:
z, 9:
ldz, 10:
work, 11:
rwork, 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.
-
-
If , the algorithm failed to converge; off-diagonal elements of an intermediate tridiagonal form did not converge to zero.
Accuracy
The computed eigenvalues and eigenvectors are exact for a nearby matrix
, where
and
is the
machine precision. See Section 4.7 of
Anderson et al. (1999) for further details.
Further Comments
The total number of floating-point operations is proportional to if and is proportional to otherwise.
The real analogue of this function is
nag_lapack_dsbev (f08ha).
Example
This example finds all the eigenvalues and eigenvectors of the Hermitian band matrix
together with approximate error bounds for the computed eigenvalues and eigenvectors.
Open in the MATLAB editor:
f08hn_example
function f08hn_example
fprintf('f08hn example results\n\n');
uplo = 'U';
kd = int64(2);
n = int64(4);
ab = [0, 0 + 0i, 3 - 1i, 4 - 2i, 5 - 3i;
0 + 0i, 2 - 1i, 3 - 2i, 4 - 3i, 5 - 4i;
1 + 0i, 2 + 0i, 3 + 0i, 4 + 0i, 5 + 0i];
jobz = 'Vectors';
[~, w, z, info] = f08hn( ...
jobz, uplo, kd, ab);
for i = 1:n
[~,k] = max(abs(real(z(:,i)))+abs(imag(z(:,i))));
z(:,i) = z(:,i)*conj(z(k,i))/abs(z(k,i));
end
disp('Eigenvalues');
disp(w');
[ifail] = x04da( ...
'General', ' ', z, 'Eigenvectors');
errbnd = x02aj*max(abs(w(1)),abs(w(end)));
[rcondz, info] = f08fl( ...
'Eigenvectors', n, n, w);
zerrbd = errbnd./rcondz;
fprintf('\nError estimate for the eigenvalues\n');
fprintf('%12.1e\n',errbnd);
disp('Error estimates for the eigenvectors');
fprintf('%12.1e',zerrbd);
fprintf('\n');
f08hn example results
Eigenvalues
-6.4185 -1.4094 1.4421 4.4856 16.9002
Eigenvectors
1 2 3 4 5
1 -0.2534 -0.4188 -0.2560 0.4767 0.1439
-0.0538 0.4797 0.3721 -0.2748 0.0000
2 -0.0662 -0.0122 0.5344 0.5524 0.3060
0.4301 -0.3529 0.0000 0.0000 0.0411
3 0.5274 0.4621 -0.4245 0.2076 0.4681
0.0000 0.0000 0.0915 -0.0660 0.2306
4 0.1061 -0.1642 0.4964 -0.1379 0.4098
-0.4981 0.3146 -0.1546 0.1026 0.3832
5 -0.4519 -0.0360 -0.1979 -0.2651 0.1819
0.0424 -0.3593 -0.1114 -0.4948 0.5136
Error estimate for the eigenvalues
1.9e-15
Error estimates for the eigenvectors
3.7e-16 6.6e-16 6.6e-16 6.2e-16
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