f12fc: FL CL CPP AD PY MB

NAG CL Interface
f12fcc (real_symm_proc)

Note: this function uses optional parameters to define choices in the problem specification. If you wish to use default settings for all of the optional parameters, then the option setting function f12fdc need not be called. If, however, you wish to reset some or all of the settings please refer to Section 11 in f12fdc for a detailed description of the specification of the optional parameters.

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NAG Library Manual, Mark 30.2

Interfaces: FL CL CPP AD PY MB

NAG CL Interface Introduction

F12 (Sparseig) Chapter Contents

F12 (Sparseig) Chapter Introduction

f12fc: FL CL CPP AD PY MB

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

▸▿ 10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

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1 Purpose

f12fcc is a post-processing function in a suite of functions which includes f12fac, f12fbc, f12fdc and f12fec. f12fcc must be called following a final exit from f12fbc.

2 Specification

#include <nag.h>

void	f12fcc (Integer nconv, double d[], double z[], double sigma, const double resid[], double v[], double comm[], Integer icomm[], NagError fail)

The function may be called by the names: f12fcc, nag_sparseig_real_symm_proc or nag_real_symm_sparse_eigensystem_sol.

3 Description

The suite of functions is designed to calculate some of the eigenvalues,

λ

, (and optionally the corresponding eigenvectors,

x

) of a standard eigenvalue problem

A x = λ x

, or of a generalized eigenvalue problem

A x = λ B x

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.

Following a call to f12fbc, f12fcc returns the converged approximations to eigenvalues and (optionally) the corresponding approximate eigenvectors and/or an orthonormal basis for the associated approximate invariant subspace. The eigenvalues (and eigenvectors) are selected from those of a standard or generalized eigenvalue problem defined by real symmetric matrices. There is negligible additional cost to obtain eigenvectors; an orthonormal basis is always computed, but there is an additional storage cost if both are requested.

f12fcc is based on the function dseupd from the ARPACK package, which uses the Implicitly Restarted Lanczos iteration method. The method is described in Lehoucq and Sorensen (1996) and Lehoucq (2001) while its use within the ARPACK software is described in great detail in Lehoucq et al. (1998). An evaluation of software for computing eigenvalues of sparse symmetric matrices is provided in Lehoucq and Scott (1996). This suite of functions offers the same functionality as the ARPACK software for real symmetric problems, but the interface design is quite different in order to make the option setting clearer and to simplify some of the interfaces.

f12fcc, is a post-processing function that must be called following a successful final exit from f12fbc. f12fcc uses data returned from f12fbc and options, set either by default or explicitly by calling f12fdc, to return the converged approximations to selected eigenvalues and (optionally):

–the corresponding approximate eigenvectors;
–an orthonormal basis for the associated approximate invariant subspace;
–both.

4 References

Lehoucq R B (2001) Implicitly restarted Arnoldi methods and subspace iteration SIAM Journal on Matrix Analysis and Applications 23 551–562

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 Applications 17 789–821

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

5 Arguments

1: $nconv$ – Integer * Output: On exit: the number of converged eigenvalues as found by f12fbc.
2: $d [\dim]$ – double Output: Note: the dimension, dim, of the array d must be at least $ncv$ (see f12fac).

On exit: the first nconv locations of the array d contain the converged approximate eigenvalues.
3: $z [n \times (nev + 1)]$ – double Output: On exit: if the default option $Vectors = RITZ$ (see f12fdc) has been selected then z contains the final set of eigenvectors corresponding to the eigenvalues held in d. The real eigenvector associated with an eigenvalue is stored in the corresponding array section of z.
4: $sigma$ – double Input: On entry: if one of the $Shifted Inverse$ (see f12fdc) modes has been selected then sigma contains the real shift used; otherwise sigma is not referenced.
5: $resid [\dim]$ – const double Input: Note: the dimension, dim, of the array resid must be at least $n$ (see f12fac).

On entry: must not be modified following a call to f12fbc since it contains data required by f12fcc.
6: $v [n \times ncv]$ – double Input/Output: The $i$ th element of the $j$ th basis vector is stored in location $v [n \times (j - 1) + i - 1]$ , for $i = 1, 2, \dots, n$ and $j = 1, 2, \dots, ncv$ .

On entry: the ncv sections of v, of length $n$ , contain the Lanczos basis vectors for $op$ as constructed by f12fbc.

On exit: if the option $Vectors = SCHUR$ has been set, or the option $Vectors = RITZ$ has been set and a separate array z has been passed (i.e., z does not equal v), then the first nconv sections of v, of length $n$ , will contain approximate Schur vectors that span the desired invariant subspace.
7: $comm [\dim]$ – double Communication Array: Note: the actual argument supplied must be the array comm supplied to the initialization routine f12fac.

On initial entry: must remain unchanged from the prior call to f12fac.

On exit: contains data on the current state of the solution.
8: $icomm [\dim]$ – Integer Communication Array: Note: the actual argument supplied must be the array icomm supplied to the initialization routine f12fac.

On initial entry: must remain unchanged from the prior call to f12fac.

On exit: contains data on the current state of the solution.
9: $fail$ – NagError * Input/Output: The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM: On entry, argument $⟨ value ⟩$ had an illegal value.
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_INVALID_OPTION: On entry, $Vectors = SELECT$ , but this is not yet implemented.
NE_MAX_ITER: During calculation of a tridiagonal form, there was a failure to compute $⟨ value ⟩$ eigenvalues in a total of $⟨ value ⟩$ iterations.
NE_MISSING_CALL: Either the function was called out of sequence (following an initial call to the setup function and following completion of calls to the reverse communication function) or the communication arrays have become corrupted.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_RITZ_COUNT: Got a different count of the number of converged Ritz values than the value passed to it through the argument icomm: number counted $= ⟨ value ⟩$ , number expected $= ⟨ value ⟩$ . This usually indicates that a communication array has been altered or has become corrupted between calls to f12fbc and f12fcc.
NE_ZERO_EIGS_FOUND: The number of eigenvalues found to sufficient accuracy, as communicated through the argument icomm, is zero. You should experiment with different values of nev and ncv, or select a different computational mode or increase the maximum number of iterations prior to calling f12fbc.

7 Accuracy

The relative accuracy of a Ritz value,

λ

, is considered acceptable if its Ritz estimate

\leq Tolerance \times | λ |

. The default

Tolerance

used is the machine precision given by X02AJC.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

f12fcc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

f12fcc 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 function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

None.

10 Example

This example solves

A x = λ B x

in regular mode, where

A

and

B

are obtained from the standard central difference discretization of the one-dimensional Laplacian operator

\frac{d^{2} u}{d x^{2}}

[0, 1]

, with zero Dirichlet boundary conditions.

f12fc: FL CL CPP AD PY MB

NAG CL Interfacef12fcc (real_​symm_​proc)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG CL Interface
f12fcc (real_symm_proc)