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NAG Toolbox: nag_lapack_dspgv (f08ta)
Purpose
nag_lapack_dspgv (f08ta) computes all the eigenvalues and, optionally, all the eigenvectors of a real generalized symmetric-definite eigenproblem, of the form
where
and
are symmetric, stored in packed format, and
is also positive definite.
Syntax
Description
nag_lapack_dspgv (f08ta) first performs a Cholesky factorization of the matrix
as
, when
or
, when
. The generalized problem is then reduced to a standard symmetric eigenvalue problem
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
, the eigenvectors are normalized so that the matrix of eigenvectors,
, satisfies
where
is the diagonal matrix whose diagonal elements are the eigenvalues. For the problem
we correspondingly have
and for
we have
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:
– int64int32nag_int scalar
-
Specifies the problem type to be solved.
- .
- .
- .
Constraint:
, or .
- 2:
– string (length ≥ 1)
-
Indicates whether eigenvectors are computed.
- Only eigenvalues are computed.
- Eigenvalues and eigenvectors are computed.
Constraint:
or .
- 3:
– string (length ≥ 1)
-
If
, the upper triangles of
and
are stored.
If , the lower triangles of and are stored.
Constraint:
or .
- 4:
– int64int32nag_int scalar
-
, the order of the matrices and .
Constraint:
.
- 5:
– double array
-
The dimension of the array
ap
must be at least
The upper or lower triangle of the
by
symmetric matrix
, packed by columns.
More precisely,
- if , the upper triangle of must be stored with element in for ;
- if , the lower triangle of must be stored with element in for .
- 6:
– double array
-
The dimension of the array
bp
must be at least
The upper or lower triangle of the
by
symmetric matrix
, packed by columns.
More precisely,
- if , the upper triangle of must be stored with element in for ;
- if , the lower triangle of must be stored with element in for .
Optional Input Parameters
None.
Output Parameters
- 1:
– double array
-
The dimension of the array
ap will be
The contents of
ap are destroyed.
- 2:
– double array
-
The dimension of the array
bp will be
The triangular factor or from the Cholesky factorization or , in the same storage format as .
- 3:
– double array
-
The eigenvalues in ascending order.
- 4:
– double 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 matrix
of eigenvectors. The eigenvectors are normalized as follows:
- if or , ;
- if , .
If
,
z is not referenced.
- 5:
– 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:
itype, 2:
jobz, 3:
uplo, 4:
n, 5:
ap, 6:
bp, 7:
w, 8:
z, 9:
ldz, 10:
work, 11:
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.
-
-
nag_lapack_dpptrf (f07gd) or
nag_lapack_dspev (f08ga) returned an error code:
|
if , nag_lapack_dspev (f08ga) failed to converge; off-diagonal elements of an intermediate tridiagonal form did not converge to zero; |
|
if , for , then the leading minor of order of is not positive definite. The factorization of could not be completed and no eigenvalues or eigenvectors were computed. |
Accuracy
If
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
differ widely in magnitude the eigenvalues and eigenvectors may be less sensitive than the condition of
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 .
The complex analogue of this function is
nag_lapack_zhpgv (f08tn).
Example
This example finds all the eigenvalues and eigenvectors of the generalized symmetric eigenproblem
, where
together with an estimate of the condition number of
, and approximate error bounds for the computed eigenvalues and eigenvectors.
The example program for
nag_lapack_dspgvd (f08tc) illustrates solving a generalized symmetric eigenproblem of the form
.
Open in the MATLAB editor:
f08ta_example
function f08ta_example
fprintf('f08ta example results\n\n');
n = int64(4);
uplo = 'U';
ap = [0.24;
0.39; -0.11;
0.42; 0.79; -0.25;
-0.16; 0.63; 0.48; -0.03];
bp = [4.16;
-3.12; 5.03;
0.56; -0.83; 0.76;
-0.10; 1.09; 0.34; 1.18];
[a, ap, ifail] = f01za( ...
'Unpack', uplo, 'N', zeros(n,n), ap);
[b, bp, ifail] = f01za( ...
'Unpack', uplo, 'N', zeros(n,n), bp);
anorm = norm(a + a' - diag(diag(a)),1);
bnorm = norm(b + b' - diag(diag(b)),1);
itype = int64(1);
jobz = 'No vectors';
[~, L, w, z, info] = f08ta( ...
itype, jobz, uplo, n, ap, bp);
disp('Eigenvalues');
disp(w');
[rcond, info] = f07ug( ...
'1', uplo, 'N', n, L);
rcondb = rcond^2;
fprintf('Estimate of reciprocal condition number for B\n%12.1e\n', rcondb);
t1 = x02aj/rcondb;
t2 = anorm/bnorm;
errbnd(1:n) = t1*t2 + t1.*abs(w);
fprintf('\n');
disp('Error estimate for the eigenvalues');
fprintf('%12.1e',errbnd);
fprintf('\n');
f08ta example results
Eigenvalues
-2.2254 -0.4548 0.1001 1.1270
Estimate of reciprocal condition number for B
5.8e-03
Error estimate for the eigenvalues
4.7e-14 1.2e-14 5.6e-15 2.5e-14
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