nag_real_symm_sparse_eigensystem_sol (f12fcc) (PDF version)
f12 Chapter Contents
f12 Chapter Introduction
NAG C Library Manual

NAG Library Function Document

nag_real_symm_sparse_eigensystem_sol (f12fcc)

Note: this function uses optional arguments to define choices in the problem specification. If you wish to use default settings for all of the optional arguments, then the option setting routine nag_real_symm_sparse_eigensystem_option (f12fdc) need not be called. If, however, you wish to reset some or all of the settings please refer to Section 10 in nag_real_symm_sparse_eigensystem_option (f12fdc) for a detailed description of the specification of the optional arguments.

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_real_symm_sparse_eigensystem_sol (f12fcc) is a post-processing function in a suite of functions which includes nag_real_symm_sparse_eigensystem_init (f12fac), nag_real_symm_sparse_eigensystem_iter (f12fbc), nag_real_symm_sparse_eigensystem_option (f12fdc) and nag_real_symm_sparse_eigensystem_monit (f12fec). nag_real_symm_sparse_eigensystem_sol (f12fcc) must be called following a final exit from nag_real_symm_sparse_eigensystem_iter (f12fbc).

2  Specification

#include <nag.h>
#include <nagf12.h>
void  nag_real_symm_sparse_eigensystem_sol (Integer *nconv, double d[], double z[], double sigma, const double resid[], double v[], double comm[], Integer icomm[], NagError *fail)

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 Ax = λx , or of a generalized eigenvalue problem Ax = λBx  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 nag_real_symm_sparse_eigensystem_iter (f12fbc), nag_real_symm_sparse_eigensystem_sol (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.
nag_real_symm_sparse_eigensystem_sol (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.
nag_real_symm_sparse_eigensystem_sol (f12fcc), is a post-processing function that must be called following a successful final exit from nag_real_symm_sparse_eigensystem_iter (f12fbc). nag_real_symm_sparse_eigensystem_sol (f12fcc) uses data returned from nag_real_symm_sparse_eigensystem_iter (f12fbc) and options, set either by default or explicitly by calling nag_real_symm_sparse_eigensystem_option (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, Philidelphia

5  Arguments

1:     nconvInteger *Output
On exit: the number of converged eigenvalues as found by nag_real_symm_sparse_eigensystem_iter (f12fbc).
2:     d[dim]doubleOutput
Note: the dimension, dim, of the array d must be at least ncv (see nag_real_symm_sparse_eigensystem_init (f12fac)).
On exit: the first nconv locations of the array d contain the converged approximate eigenvalues.
3:     z[n×nev+1]doubleOutput
On exit: if the default option Vectors=RITZ (see nag_real_symm_sparse_eigensystem_option (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:     sigmadoubleInput
On entry: if one of the Shifted Inverse (see nag_real_symm_sparse_eigensystem_option (f12fdc)) modes has been selected then sigma contains the real shift used; otherwise sigma is not referenced.
5:     resid[dim]const doubleInput
Note: the dimension, dim, of the array resid must be at least n (see nag_real_symm_sparse_eigensystem_init (f12fac)).
On entry: must not be modified following a call to nag_real_symm_sparse_eigensystem_iter (f12fbc) since it contains data required by nag_real_symm_sparse_eigensystem_sol (f12fcc).
6:     v[dim]doubleInput/Output
Note: the dimension, dim, of the array v must be at least max1,ncv  (see nag_real_symm_sparse_eigensystem_init (f12fac)).
The ith element of the jth basis vector is stored in location v[n×j-1+i-1], for i=1,2,,n and j=1,2,,ncv.
On entry: the ncv sections of v, of length n, contain the Lanczos basis vectors for OP as constructed by nag_real_symm_sparse_eigensystem_iter (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]doubleCommunication Array
Note: the dimension, dim, of the array comm must be at least max1,lcomm (see nag_real_symm_sparse_eigensystem_init (f12fac)).
On initial entry: must remain unchanged from the prior call to nag_real_symm_sparse_eigensystem_init (f12fac).
On exit: contains data on the current state of the solution.
8:     icomm[dim]IntegerCommunication Array
Note: the dimension, dim, of the array icomm must be at least max1,licomm (see nag_real_symm_sparse_eigensystem_init (f12fac)).
On initial entry: must remain unchanged from the prior call to nag_real_symm_sparse_eigensystem_init (f12fac).
On exit: contains data on the current state of the solution.
9:     failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

6  Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
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.
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_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.
NE_ZERO_EIGS_FOUND
The number of eigenvalues found to sufficient accuracy, as communicated through the argument icomm, is zero.

7  Accuracy

The relative accuracy of a Ritz value, λ , is considered acceptable if its Ritz estimate Tolerance × λ . The default Tolerance used is the machine precision given by nag_machine_precision (X02AJC).

8  Further Comments

None.

9  Example

This example solves Ax = λBx  in regular mode, where A  and B  are obtained from the standard central difference discretization of the one-dimensional Laplacian operator d2u dx2  on 0,1 , with zero Dirichlet boundary conditions.

9.1  Program Text

Program Text (f12fcce.c)

9.2  Program Data

Program Data (f12fcce.d)

9.3  Program Results

Program Results (f12fcce.r)


nag_real_symm_sparse_eigensystem_sol (f12fcc) (PDF version)
f12 Chapter Contents
f12 Chapter Introduction
NAG C Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2012