f12faf is a setup routine in a suite of routines consisting of f12faf,f12fbf,f12fcf,f12fdfandf12fef. It is used to find some of the eigenvalues (and optionally the corresponding eigenvectors) of a standard or generalized eigenvalue problem defined by real symmetric matrices.
The suite of routines is suitable for the solution of large sparse, standard or generalized, symmetric eigenproblems where only a few eigenvalues from a selected range of the spectrum are required.
The routine may be called by the names f12faf or nagf_sparseig_real_symm_init.
3Description
The suite of routines is designed to calculate some of the eigenvalues, $\lambda $, (and optionally the corresponding eigenvectors, $x$) of a standard eigenvalue problem $Ax=\lambda x$, or of a generalized eigenvalue problem $Ax=\lambda Bx$ of order $n$, where $n$ is large and the coefficient matrices $A$ and $B$ are sparse, real and symmetric. The suite can also be used to find selected eigenvalues/eigenvectors of smaller scale dense, real and symmetric problems.
f12faf is a setup routine which must be called before f12fbf, the reverse communication iterative solver, and before f12fdf, the options setting routine. f12fcf, is a post-processing routine that must be called following a successful final exit from f12fbf, while f12fef can be used to return additional monitoring information during the computation.
This setup routine initializes the communication arrays, sets (to their default values) all options that can be set by you via the option setting routine f12fdf, and checks that the lengths of the communication arrays as passed by you are of sufficient length. For details of the options available and how to set them see Section 11.1 in f12fdf.
4References
Lehoucq R B (2001) Implicitly restarted Arnoldi methods and subspace iteration SIAM Journal on Matrix Analysis and Applications23 551–562
Lehoucq R B and Scott J A (1996) An evaluation of software for computing eigenvalues of sparse nonsymmetric matrices Preprint MCS-P547-1195 Argonne National Laboratory
Lehoucq R B and Sorensen D C (1996) Deflation techniques for an implicitly restarted Arnoldi iteration SIAM Journal on Matrix Analysis and Applications17 789–821
Lehoucq R B, Sorensen D C and Yang C (1998) ARPACK Users' Guide: Solution of Large-scale Eigenvalue Problems with Implicitly Restarted Arnoldi Methods SIAM, Philadelphia
5Arguments
1: $\mathbf{n}$ – IntegerInput
On entry: the order of the matrix $A$ (and the order of the matrix $B$ for the generalized problem) that defines the eigenvalue problem.
Constraint:
${\mathbf{n}}>0$.
2: $\mathbf{nev}$ – IntegerInput
On entry: the number of eigenvalues to be computed.
Constraint:
$0<{\mathbf{nev}}<{\mathbf{n}}-1$.
3: $\mathbf{ncv}$ – IntegerInput
On entry: the number of Lanczos basis vectors to use during the computation.
At present there is no a priori analysis to guide the selection of ncv relative to nev. However, it is recommended that ${\mathbf{ncv}}\ge 2\times {\mathbf{nev}}+1$. If many problems of the same type are to be solved, you should experiment with increasing ncv while keeping nev fixed for a given test problem. This will usually decrease the required number of matrix-vector operations but it also increases the work and storage required to maintain the orthogonal basis vectors. The optimal ‘cross-over’ with respect to CPU time is problem dependent and must be determined empirically.
On exit: contains data to be communicated to the other routines in the suite.
5: $\mathbf{licomm}$ – IntegerInput
On entry: the dimension of the array icomm as declared in the (sub)program from which f12faf is called.
If ${\mathbf{licomm}}=\mathrm{-1}$, a workspace query is assumed and the routine only calculates the required dimensions of icomm and comm, which it returns in ${\mathbf{icomm}}\left(1\right)$ and ${\mathbf{comm}}\left(1\right)$ respectively.
Constraint:
${\mathbf{licomm}}\ge 140$ or ${\mathbf{licomm}}=\mathrm{-1}$.
6: $\mathbf{comm}\left(\mathrm{max}\phantom{\rule{0.125em}{0ex}}(1,{\mathbf{lcomm}})\right)$ – Real (Kind=nag_wp) arrayCommunication Array
On exit: contains data to be communicated to the other routines in the suite.
7: $\mathbf{lcomm}$ – IntegerInput
On entry: the dimension of the array comm as declared in the (sub)program from which f12faf is called.
If ${\mathbf{lcomm}}=\mathrm{-1}$, a workspace query is assumed and the routine only calculates the dimensions of icomm and comm required by f12fbf, which it returns in ${\mathbf{icomm}}\left(1\right)$ and ${\mathbf{comm}}\left(1\right)$ respectively.
Constraint:
${\mathbf{lcomm}}\ge 3\times {\mathbf{n}}+{\mathbf{ncv}}\times {\mathbf{ncv}}+8\times {\mathbf{ncv}}+60$ or ${\mathbf{lcomm}}=\mathrm{-1}$.
8: $\mathbf{ifail}$ – IntegerInput/Output
On entry: ifail must be set to $0$, $\mathrm{-1}$ or $1$ to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of $0$ causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of $\mathrm{-1}$ means that an error message is printed while a value of $1$ means that it is not.
If halting is not appropriate, the value $\mathrm{-1}$ or $1$ is recommended. If message printing is undesirable, then the value $1$ is recommended. Otherwise, the value $0$ is recommended. When the value $-\mathbf{1}$ or $\mathbf{1}$ is used it is essential to test the value of ifail on exit.
On exit: ${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see Section 6).
6Error Indicators and Warnings
If on entry ${\mathbf{ifail}}=0$ or $\mathrm{-1}$, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
${\mathbf{ifail}}=1$
On entry, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{n}}>0$.
${\mathbf{ifail}}=2$
On entry, ${\mathbf{nev}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{nev}}>0$.
${\mathbf{ifail}}=3$
On entry, ${\mathbf{ncv}}=\u27e8\mathit{\text{value}}\u27e9$, ${\mathbf{nev}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$. Constraint: ${\mathbf{ncv}}>{\mathbf{nev}}$ and ${\mathbf{ncv}}\le {\mathbf{n}}$.
${\mathbf{ifail}}=4$
The length of the integer array icomm is too small ${\mathbf{licomm}}=\u27e8\mathit{\text{value}}\u27e9$, but must be at least $\u27e8\mathit{\text{value}}\u27e9$.
${\mathbf{ifail}}=5$
On entry, ${\mathbf{lcomm}}=\u27e8\mathit{\text{value}}\u27e9$, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{ncv}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{lcomm}}\ge 3\times {\mathbf{n}}+{\mathbf{ncv}}\times {\mathbf{ncv}}+8\times {\mathbf{ncv}}+60$.
${\mathbf{ifail}}=-99$
An unexpected error has been triggered by this routine. Please
contact NAG.
See Section 7 in the Introduction to the NAG Library FL Interface for further information.
${\mathbf{ifail}}=-399$
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.
${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.
7Accuracy
Not applicable.
8Parallelism and Performance
f12faf is not threaded in any implementation.
9Further Comments
None.
10Example
This example solves $Ax=\lambda x$ in regular mode, where $A$ is obtained from the standard central difference discretization of the Laplacian operator $\frac{{\partial}^{2}u}{\partial {x}^{2}}+\frac{{\partial}^{2}u}{\partial {y}^{2}}$
on the unit square, with zero Dirichlet boundary conditions. Eigenvalues of smallest magnitude are selected.