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# NAG Toolbox: nag_sparseig_real_symm_monit (f12fe)

## Purpose

nag_sparseig_real_symm_monit (f12fe) can be used to return additional monitoring information during computation. It is in a suite of functions which includes nag_sparseig_real_symm_init (f12fa), nag_sparseig_real_symm_iter (f12fb), nag_sparseig_real_symm_proc (f12fc) and nag_sparseig_real_symm_option (f12fd).

## Syntax

[niter, nconv, ritz, rzest] = f12fe(icomm, comm)
[niter, nconv, ritz, rzest] = nag_sparseig_real_symm_monit(icomm, comm)

## Description

The suite of functions 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.
On an intermediate exit from nag_sparseig_real_symm_iter (f12fb) with ${\mathbf{irevcm}}=4$, nag_sparseig_real_symm_monit (f12fe) may be called to return monitoring information on the progress of the Arnoldi iterative process. The information returned by nag_sparseig_real_symm_monit (f12fe) is:
 – the number of the current Arnoldi iteration; – the number of converged eigenvalues at this point; – the real and imaginary parts of the converged eigenvalues; – the error bounds on the converged eigenvalues.
nag_sparseig_real_symm_monit (f12fe) does not have an equivalent function from the ARPACK package which prints various levels of detail of monitoring information through an output channel controlled via a argument value (see Lehoucq et al. (1998) for details of ARPACK routines). nag_sparseig_real_symm_monit (f12fe) should not be called at any time other than immediately following an ${\mathbf{irevcm}}=4$ return from nag_sparseig_real_symm_iter (f12fb).

## 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

## Parameters

### Compulsory Input Parameters

1:     $\mathrm{icomm}\left(:\right)$int64int32nag_int array
The dimension of the array icomm must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{licomm}}\right)$, where licomm is passed to the setup function  (see nag_sparseig_real_symm_init (f12fa))
The array icomm output by the preceding call to nag_sparseig_real_symm_iter (f12fb).
2:     $\mathrm{comm}\left(:\right)$ – double array
The dimension of the array comm must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,3×{\mathbf{n}}+{\mathbf{ncv}}×{\mathbf{ncv}}+8×{\mathbf{ncv}}+60\right)$ (see nag_sparseig_real_symm_init (f12fa))
The array comm output by the preceding call to nag_sparseig_real_symm_iter (f12fb).

None.

### Output Parameters

1:     $\mathrm{niter}$int64int32nag_int scalar
The number of the current Arnoldi iteration.
2:     $\mathrm{nconv}$int64int32nag_int scalar
The number of converged eigenvalues so far.
3:     $\mathrm{ritz}\left(:\right)$ – double array
The dimension of the array ritz will be ${\mathbf{ncv}}$ (see nag_sparseig_real_symm_init (f12fa))
The first nconv locations of the array ritz contain the real converged approximate eigenvalues.
4:     $\mathrm{rzest}\left(:\right)$ – double array
The dimension of the array rzest will be ${\mathbf{ncv}}$ (see nag_sparseig_real_symm_init (f12fa))
The first nconv locations of the array rzest contain the Ritz estimates (error bounds) on the real nconv converged approximate eigenvalues.

None.

## Accuracy

A Ritz value, $\lambda$, is deemed to have converged if its Ritz estimate $\le {\mathbf{Tolerance}}×\left|\lambda \right|$. The default Tolerance used is the machine precision given by nag_machine_precision (x02aj).

None.

## Example

This example solves $Kx=\lambda {K}_{G}x$ using the Buckling option (see nag_sparseig_real_symm_option (f12fd), where $K$ and ${K}_{G}$ are obtained by the finite element method applied to the one-dimensional discrete Laplacian operator $\frac{{\partial }^{2}u}{\partial {x}^{2}}$ on $\left[0,1\right]$, with zero Dirichlet boundary conditions using piecewise linear elements. The shift, $\sigma$, is a real number, and the operator used in the Buckling iterative process is $\mathrm{OP}=\text{inv}\left(K-\sigma {K}_{G}\right)×K$ and $B=K$.
```function f12fe_example

fprintf('f12fe example results\n\n');

n = int64(100);
nev = int64(4);
ncv = int64(10);

irevcm = int64(0);
resid = zeros(n,1);
v     = zeros(n,ncv);
x     = zeros(n,1);
mx    = zeros(n,1);
imon = 1;

sigma = 1;

% Setup and factorize A - sigma*B
h   = 1/double(n+1);
ad(1:n)  =  2/h - sigma*4*h/6;
adl(1:n) = -1/h - sigma*h/6;
adu(1:n) = adl(1:n);

[adl, ad, adu, adu2, ipiv, info] = f07cd( ...
adl, ad, adu);

% Initialisation Step
[icomm, comm, ifail] = f12fa( ...
n, nev, ncv);

% Set Optional Parameters
[icomm, comm, ifail] = f12fd( ...
'Generalized', icomm, comm);
[icomm, comm, ifail] = f12fd( ...
'Buckling', icomm, comm);

% Solve
while (irevcm ~= 5)
[irevcm, resid, v, x, mx, nshift, comm, icomm, ifail] = ...
f12fb( ...
irevcm, resid, v, x, mx, comm, icomm);
if (irevcm == -1)
% Solve (A-sigma*B)y = Ax
mx = f12fe_Ax(n, x);
[x, info] = f07ce( ...
'N', adl, ad, adu, adu2, ipiv, mx);
elseif (irevcm == 1)
% Solve (A-sigma*B)y = Ax, Ax in mx
[x, info] = f07ce( ...
'N', adl, ad, adu, adu2, ipiv, mx);
elseif (irevcm == 2)
% y = Ax
mx = f12fe_Ax(n, x);
elseif (irevcm == 4 && imon == 1)
[niter, nconv, ritz, rzest] = ...
f12fe(icomm, comm);

fprintf(['Iteration %2d, No. converged = %d, ', ...
'norm of estimates = %10.2e\n'], ...
niter, nconv, norm(rzest(1:nev),2));
end
end

% Post-process to compute eigenvalues/vectors
[nconv, d, z, v, comm, icomm, ifail] = ...
f12fc( ...
sigma, resid, v, comm, icomm);

fprintf('\nThe %d Eigenvalues closest to %7.2f are:\n',nconv, sigma);
disp(d(1:nconv));

function [y] = f12fe_Ax(n,x)

y = zeros(n,1);
h = 1/double(n+1);

dd = 2;
dl = -1;
du = -1;

y(1) = dd*x(1) + du*x(2);
for j=2:n-1
y(j) = dl*x(j-1) + dd*x(j) + du*x(j+1);
end
y(n) = dl*x(n-1) + dd*x(n);
y = y/h;
```
```f12fe example results

Iteration  1, No. converged = 0, norm of estimates =   2.05e-06
Iteration  2, No. converged = 2, norm of estimates =   6.08e-11
Iteration  3, No. converged = 3, norm of estimates =   5.27e-15

The 4 Eigenvalues closest to    1.00 are:
9.8704
39.4912
88.8909
158.1175

```

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