%% An overview of the example collection
% Mario Berljafa, Steven Elsworth, Stefan Guettel
%
% February 2019
%
% Tags: Overview
%% Welcome
% Welcome to this example collection, which intends to demonstrate some of
% the features of the MATLAB Rational Krylov Toolbox. Simply use the menu
% on the left-hand side to navigate through the collection. Each example is
% available as a MATLAB |m-file| and in PDF format (see the links in the
% above header). All examples are also
% included in the |rktoolbox.zip| file available from the
% website.
%
% RKT_BIGBREAK
%
% New examples will be added over time and contributions are more than
% welcome. If you would like to add an example to this collection please
% email your MATLAB file to stefan.guettel@manchester.ac.uk. You can use
% any |m-file| of this collection as a template.
%% A simple illustration
% Here is a simple example illustrating the fascinating convergence
% behaviour of rational Ritz values [1]. The matrix $A$ is diagonal with
% $100$ equispaced eigenvalues in the interval $[1,100]$. Using the
% rational Arnoldi method [3,4] implemented in |rat_krylov|, we compute
% Ritz values associated with rational Krylov spaces of increasing dimension
% with poles alternating between
% $0$ and $\infty$. We then visualize the distance of each Ritz value of order
% $j=1,\ldots,99$ to its closest eigenvalue:
N = 100; m = 99;
A = spdiags((1:m+1)', 0, N, N);
b = ones(N, 1);
xi = zeros(1, m); xi(1:2:end) = inf;
[V, K, H] = rat_krylov(A, b, xi);
Am = H(1:m, 1:m)/K(1:m, 1:m);
R = ones(N, m);
for j = 1:m
ritz = eig(Am(1:j,1:j));
R(round(ritz),j) = abs(ritz - round(ritz));
end
imagesc(R); colormap(hot(100)); colorbar
xlabel('order j'); ylabel('Ritz values');
title('distance of Ritz value to closest eigenvalue')
%% Block rational Krylov methods
% Ritz values can be slow to find repeated or tightly clustered eigenvalues.
% Block Krylov methods are often able to overcome this problem.
% Here we illustrate the difference between the Ritz value
% convergence in single-vector and block Krylov methods using
% the Wilkinson matrix [5], a symmetric tridiagonal matrix with pairs
% of nearly, but not exactly, equal eigenvalues. Using the
% block rational Arnoldi method [2], also implemented in |rat_krylov|,
% we now compute Ritz values associated with block polynomial Krylov spaces
% of block size $s=1$ and $s=2$, respectively.
% We then pair off the Ritz values to their closest eigenvalues in descending
% order and visualize the distance between them using color.
N = 100; m = 98; % m/s block Krylov iterations to perform
W = wilkinson(N); evs = eig(W);
for s = 1:2 % s is the block size
rng(0), b = rand(N, s); xi = inf*ones(1, m/s);
[~, K, H] = rat_krylov(W, b, xi);
Am = H(1:m, 1:m)/K(1:m, 1:m);
R = ones(N, m);
for j = s:s:m
ritz = sort(eig(Am(1:j,1:j)), 'descend'); evs_copy = evs;
for i = 1:length(ritz)
[y, ind] = min(abs(evs_copy - ritz(i)));
evs_copy(ind) = inf; % pair Ritz val with unique ev
R(ind, j-s+1:j) = y;
end
end
figure(); imagesc(R); colormap(hot(100)); colorbar
xlabel('order j'); ylabel('eigenvalue index');
title(['s = ', num2str(s), ''])
end
%%
% Most of the eigenvalues of the Wilkinson matrix appear in nearby pairs.
% The Ritz values computed with the single-vector Krylov method ($s=1$)
% manage to approximate one of the eigenvalues in early iterations,
% but struggle to converge to the nearby second eigenvalue.
% In the block case ($s=2$), pairs of eigenvalues are approximated more quickly.
%% What else is in the RKToolbox?
% In addition to Ruhe's (block) rational Krylov method with
% various advanced options (including user-defined inner products, exploitation
% of complex-conjugate shifts, orthogonalization, rerunning, and simulated
% parallelism), the RKToolbox currently contains
%
% * algorithms for the implicit and explicit relocation of the poles of
% a rational Krylov space,
% * a collection of utility functions and a gallery of
% special rational functions (e.g., Zolotarev approximants),
% * an implementation of RKFIT, a robust algorithm for approximate
% rational least squares approximation, including automated degree reduction,
% * the RKFUN class for numerical computations with rational
% functions, including support for MATLAB Variable Precision Arithmetic
% and the Advanpix Multiple Precision toolbox,
% * the RKFUNM class, a matrix-valued generalization of RKFUNs, together
% with the ability to sample and solve nonlinear eigenvalue problems using
% the NLEIGS and AAA algorithms, and
% * the RKFUNB class for numerical computations with rational
% matrix-valued functions.
%
% Please have a look at the other RKToolbox examples, which demonstrate
% some of these functionalities, and the guide. Happy RKToolbox-ing!
%% References
% [1] B. Beckermann, S. Guettel, and R. Vandebril. _On the convergence of
% rational Ritz values,_ SIAM J. Matrix Anal. Appl., 31(4):1740--1774, 2010.
%
% RKT_BIGBREAK
%
% [2] S. Elsworth and S. Guettel.
% _The block rational Arnoldi method,_
% MIMS Eprint 2019.2 (),
% Manchester Institute for Mathematical Sciences,
% The University of Manchester, UK, 2019.
%
% RKT_BIGBREAK
%
% [3] A. Ruhe. _Rational Krylov: A practical algorithm for large sparse
% nonsymmetric matrix pencils,_ SIAM J. Sci. Comput., 19(5):1535--1551,
% 1998.
%
% RKT_BIGBREAK
%
% [4] A. Ruhe. _The rational Krylov algorithm for nonsymmetric eigenvalue
% problems. III: Complex shifts for real matrices,_ BIT,
% 34(1):165--176, 1994.
%
% RKT_BIGBREAK
%
% [5] J. H. Wilkinson. _The Algebraic Eigenvalue Problem,_
% Oxford University Press, 1965.