NAG Library Routine Document
F08JDF (DSTEVR)
1 Purpose
F08JDF (DSTEVR) computes selected eigenvalues and, optionally, eigenvectors of a real by symmetric tridiagonal matrix . Eigenvalues and eigenvectors can be selected by specifying either a range of values or a range of indices for the desired eigenvalues.
2 Specification
SUBROUTINE F08JDF ( |
JOBZ, RANGE, N, D, E, VL, VU, IL, IU, ABSTOL, M, W, Z, LDZ, ISUPPZ, WORK, LWORK, IWORK, LIWORK, INFO) |
INTEGER |
N, IL, IU, M, LDZ, ISUPPZ(*), LWORK, IWORK(max(1,LIWORK)), LIWORK, INFO |
REAL (KIND=nag_wp) |
D(*), E(*), VL, VU, ABSTOL, W(*), Z(LDZ,*), WORK(max(1,LWORK)) |
CHARACTER(1) |
JOBZ, RANGE |
|
The routine may be called by its
LAPACK
name dstevr.
3 Description
Whenever possible F08JDF (DSTEVR) computes the eigenspectrum using Relatively Robust Representations. F08JDF (DSTEVR) 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. |
The desired accuracy of the output can be specified by the parameter
ABSTOL. For more details, see
Dhillon (1997) and
Parlett and Dhillon (2000).
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
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
5 Parameters
- 1: JOBZ – CHARACTER(1)Input
On entry: indicates whether eigenvectors are computed.
- Only eigenvalues are computed.
- Eigenvalues and eigenvectors are computed.
Constraint:
or .
- 2: RANGE – CHARACTER(1)Input
On entry: if
, all eigenvalues will be found.
If , all eigenvalues in the half-open interval will be found.
If
, the
ILth to
IUth eigenvalues will be found.
Constraint:
, or .
- 3: N – INTEGERInput
On entry: , the order of the matrix.
Constraint:
.
- 4: D() – REAL (KIND=nag_wp) arrayInput/Output
-
Note: the dimension of the array
D
must be at least
.
On entry: the diagonal elements of the tridiagonal matrix .
On exit: may be multiplied by a constant factor chosen to avoid over/underflow in computing the eigenvalues.
- 5: E() – REAL (KIND=nag_wp) arrayInput/Output
-
Note: the dimension of the array
E
must be at least
.
On entry: the subdiagonal elements of the tridiagonal matrix .
On exit: may be multiplied by a constant factor chosen to avoid over/underflow in computing the eigenvalues.
- 6: VL – REAL (KIND=nag_wp)Input
- 7: VU – REAL (KIND=nag_wp)Input
On entry: if
, the lower and upper bounds of the interval to be searched for eigenvalues.
If
or
,
VL and
VU are not referenced.
Constraint:
if , .
- 8: IL – INTEGERInput
- 9: IU – INTEGERInput
On entry: if
, the indices (in ascending order) of the smallest and largest eigenvalues to be returned.
If
or
,
IL and
IU are not referenced.
Constraints:
- if and , and ;
- if and , .
- 10: ABSTOL – REAL (KIND=nag_wp)Input
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. 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.
- 11: M – INTEGEROutput
On exit: the total number of eigenvalues found.
.
If , .
If , .
- 12: W() – 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.
- 13: Z(LDZ,) – 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
.
If
,
Z is not referenced.
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.
- 14: LDZ – INTEGERInput
On entry: the first dimension of the array
Z as declared in the (sub)program from which F08JDF (DSTEVR) is called.
Constraints:
- if , ;
- otherwise .
- 15: ISUPPZ() – 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
.
- 16: WORK() – REAL (KIND=nag_wp) arrayWorkspace
On exit: if
,
contains the minimum value of
LWORK required for optimal performance.
- 17: LWORK – INTEGERInput
On entry: the dimension of the array
WORK as declared in the (sub)program from which F08JDF (DSTEVR) is called.
If
, a workspace query is assumed; the routine only calculates the minimum sizes of the
WORK and
IWORK arrays, 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:
.
- 18: IWORK() – INTEGER arrayWorkspace
On exit: if
,
returns the minimum
LIWORK.
- 19: LIWORK – INTEGERInput
On entry: the dimension of the array
IWORK as declared in the (sub)program from which F08JDF (DSTEVR) is called.
If
, a workspace query is assumed; the routine only calculates the minimum sizes of the
WORK and
IWORK arrays, 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:
.
- 20: INFO – INTEGEROutput
On exit:
unless the routine detects an error (see
Section 6).
6 Error Indicators and Warnings
Errors or warnings detected by the routine:
If , argument had an illegal value. An explanatory message is output, and execution of the program is terminated.
An internal error has occurred in this routine. Please refer to
INFO in
F08JJF (DSTEBZ).
7 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.
The total number of floating point operations is proportional to if and is proportional to if and , otherwise the number of floating point operations will depend upon the number of computed eigenvectors.
9 Example
This example finds the eigenvalues with indices in the range
, and the corresponding eigenvectors, of the symmetric tridiagonal matrix
9.1 Program Text
Program Text (f08jdfe.f90)
9.2 Program Data
Program Data (f08jdfe.d)
9.3 Program Results
Program Results (f08jdfe.r)