f08fdf computes selected eigenvalues and, optionally, eigenvectors of a real symmetric matrix . Eigenvalues and eigenvectors can be selected by specifying either a range of values or a range of indices for the desired eigenvalues.
The routine may be called by the names f08fdf, nagf_lapackeig_dsyevr or its LAPACK name dsyevr.
3Description
The symmetric matrix is first reduced to a tridiagonal matrix , using orthogonal similarity transformations. Then whenever possible, f08fdf computes the eigenspectrum using Relatively Robust Representations. f08fdf computes eigenvalues by the dqds algorithm, while orthogonal eigenvectors are computed from various ‘good’ representations (also known as Relatively Robust Representations). Gram–Schmidt orthogonalization is avoided as far as possible. More specifically, the various steps of the algorithm are as follows. For the th unreduced block of :
(a)compute
, such that
is a relatively robust representation,
(b)compute the eigenvalues, , of
to high relative accuracy by the dqds algorithm,
(c)if there is a cluster of close eigenvalues, ‘choose’ close to the cluster, and go to (a),
(d)given the approximate eigenvalue of
, compute the corresponding eigenvector by forming a rank-revealing twisted factorization.
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 https://www.netlib.org/lapack/lug
Barlow J and Demmel J W (1990) Computing accurate eigensystems of scaled diagonally dominant matrices SIAM J. Numer. Anal.27 762–791
Demmel J W and Kahan W (1990) Accurate singular values of bidiagonal matrices SIAM J. Sci. Statist. Comput.11 873–912
Dhillon I (1997) A new algorithm for the symmetric tridiagonal eigenvalue/eigenvector problem Computer Science Division Technical Report No. UCB//CSD-97-971 UC Berkeley
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore
Parlett B N and Dhillon I S (2000) Relatively robust representations of symmetric tridiagonals Linear Algebra Appl.309 121–151
5Arguments
1: – Character(1)Input
On entry: indicates whether eigenvectors are computed.
Only eigenvalues are computed.
Eigenvalues and eigenvectors are computed.
Constraint:
or .
2: – Character(1)Input
On entry: if , all eigenvalues will be found.
If , all eigenvalues in the half-open interval will be found.
On entry: the absolute error tolerance for the eigenvalues. An approximate eigenvalue is accepted as converged when it is determined to lie in an interval of width less than or equal to
where is the machine precision. If abstol is less than or equal to zero, then will be used in its place, where is the tridiagonal matrix obtained by reducing to tridiagonal form. See Demmel and Kahan (1990).
If high relative accuracy is important, set abstol to , although doing so does not currently guarantee that eigenvalues are computed to high relative accuracy. See Barlow and Demmel (1990) for a discussion of which matrices can define their eigenvalues to high relative accuracy.
12: – IntegerOutput
On exit: the total number of eigenvalues found. .
If , .
If , .
13: – Real (Kind=nag_wp) arrayOutput
Note: the dimension of the array w
must be at least
.
On exit: the first m elements contain the selected eigenvalues in ascending order.
14: – Real (Kind=nag_wp) arrayOutput
Note: the second dimension of the array z
must be at least
if , and at least otherwise.
On exit: if , the first m columns of contain the orthonormal eigenvectors of the matrix corresponding to the selected eigenvalues, with the th column of holding the eigenvector associated with .
Note: you must ensure that at least columns are supplied in the array z; if , the exact value of m is not known in advance and an upper bound of at least n must be used.
15: – IntegerInput
On entry: the first dimension of the array z as declared in the (sub)program from which f08fdf is called.
Constraints:
if , ;
otherwise .
16: – Integer arrayOutput
Note: the dimension of the array isuppz
must be at least
.
On exit: the support of the eigenvectors in z, i.e., the indices indicating the nonzero elements in z. The th eigenvector is nonzero only in elements through . Implemented only for or and .
17: – Real (Kind=nag_wp) arrayWorkspace
On exit: if , contains the minimum value of lwork required for optimal performance.
18: – IntegerInput
On entry: the dimension of the array work as declared in the (sub)program from which f08fdf is called.
If , 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, , where is the largest optimal block size for f08fefandf08fgf.
On entry: the dimension of the array iwork as declared in the (sub)program from which f08fdf is called.
If , 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.
Constraint:
or .
21: – IntegerOutput
On exit: unless the routine detects an error (see Section 6).
6Error Indicators and Warnings
If , argument had an illegal value. An explanatory message is output, and execution of the program is terminated.
f08fdf failed to converge.
7Accuracy
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.
8Parallelism and Performance
Background information to multithreading can be found in the Multithreading documentation.
f08fdf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f08fdf 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.
9Further Comments
The total number of floating-point operations is proportional to .