# CD player model order reduction

### Steven Elsworth and Stefan Güttel, January 2019Download PDF or m-file

## Contents

## Introduction

Consider a linear time invariant multi-input multi-output system described by the state space equations

and

where denotes the state vector of length and denote the input and output vectors of length . The large sparse matrix is of size and are of size .

Block rational Krylov spaces [2] are a powerful tool for model order reduction provided a good set of poles is chosen. Here we explore different choices of poles, and compare how well the the reduced model behaves compared to the original system.

## CD player

We start by loading the CD player problem, and constructing a function handle for the transfer function of the system:

For each set of poles, we construct the approximate transfer function

We compare the two models by plotting the error

for over the range of .

if exist('CDplayer.mat') ~= 2 disp(['The required matrix for this problem can be downloaded from ' ... 'http://slicot.org/20-site/126-benchmark-examples-for-model-reduction']); return end load CDplayer H = @(s) C*((s*speye(size(A)) - A)\B);

## Infinite poles vs equally spaced poles

We compare a block polynomial Krylov approximation against a block rational Krylov approximation. The polynomial space is constructed with 10 poles set to infinity, whereas the rational space has 5 poles logarithmically spaced in the interval , complemented with their complex conjugates to obtain a real block rational Arnoldi decomposition. For both approaches, we plot the norm of the difference between the transfer function and the approximate tranfer function.

% block polynomial Krylov space xi = inf*ones(1,10); V = rat_krylov(A,B,xi); Am = V'*A*V; Cm = C*V; Bm = V'*B; Hm = @(s) Cm*((s*speye(size(Am)) - Am)\Bm); Xm = @(s) V*((s*eye(size(Am)) - Am)\(V'*B)); Gam = @(s) B - (s*speye(size(A)) - A)*Xm(s); errHm = []; s = 1i*logspace(0,6,500); for k = 1:length(s) errHm(k) = norm(H(1i*s(k)) - Hm(1i*s(k))); end loglog(imag(s), errHm, 'LineWidth',2); hold on xlabel('frequency $\omega$', 'Interpreter', 'latex') ylabel('$\|H(i \omega) - H_m(i \omega)\|_2$', 'Interpreter', 'latex') % block rational Krylov space xi = 1i*logspace(0,6,5); xi = util_cplxpair(xi, conj(xi)); param.real = 1; V = rat_krylov(A,B,xi); Am = V'*A*V; Cm = C*V; Bm = V'*B; Hm = @(s) Cm*((s*speye(size(Am)) - Am)\Bm); errHm = []; s = union(xi, 1i*logspace(0,6,500)); for k = 1:length(s) errHm(k) = norm(H(s(k)) - Hm(s(k))); end p = loglog(imag(xi), ones(1, length(xi)), 'kx', 'MarkerSize', 12); loglog(imag(s), errHm, 'LineWidth',2); axis([0, 1e6, 1e-6, 1e6]) xlabel('frequency $\omega$', 'Interpreter', 'latex') ylabel('$\|H(i \omega) - H_m(i \omega)\|_2$', 'Interpreter', 'latex') legend({'polynomial', 'poles', 'rational'})

## Adaptive pole selection

In this example we start with two poles at . We then select the following poles adaptively, in complex conjugate pairs, using the procedure described in Section 3.2 in [1]. This procedure proves to be quite effective, with the error curve dropping significantly from iteration to iteration. Note that we are using the *extension functionality* of the `rat_krylov` function, which allows to extend an existing block rational Arnoldi decomposition with new block basis vectors.

cand = 1i*logspace(0,6,500); xi = [ 1i , conj(1i) ]; param.real = 1; [V, K_, H_] = rat_krylov(A, B, xi, param); % initial run param.extend = 2; % allow for the iterative extension of BRAD figure() for iter = 1:5 Am = V'*A*V; Bm = V'*B; Cm = C*V; Hm = @(s) Cm*((s*speye(size(Am)) - Am)\Bm); Xm = @(s) V*((s*eye(size(Am)) - Am)\(V'*B)); Gam = @(s) B - (s*speye(size(A)) - A)*Xm(s); errHm = []; for k = 1:length(cand) nrmGam(k) = norm(Gam(cand(k))); errHm(k) = norm(H(cand(k)) - Hm(cand(k))); end mem(iter)=loglog(imag(cand),errHm, 'LineWidth',2); hold on, shg [val,ind] = max(nrmGam); loglog(imag(cand(ind)), errHm(ind), 'kx', 'MarkerSize', 12, 'LineWidth', 2) xi = [ cand(ind) , conj(cand(ind)) ]; [V,K_,H_] = rat_krylov(A, V, K_, H_, xi, param); % extend decomposition end legend(mem,{'Iter0: m = 2', 'Iter1: m = 4', 'Iter2: m = 6', ... 'Iter3: m = 8', 'Iter4: m = 10'}) xlabel('frequency $\omega$', 'Interpreter', 'latex') ylabel('$\|H(i \omega) - H_m(i \omega)\|_2$', 'Interpreter', 'latex') axis([0, 1e6, 1e-6, 1e6])

## References

[1] O. Abidi, M. Hached, and K. Jbilou. *Adaptive rational block Arnoldi methods for model reductions in large-scale MIMO dynamical systems,* New Trends Math. Sci., 4(2):227--239, 2016

[2] S. Elsworth and S. Güttel. *The block rational Arnoldi method,* MIMS Eprint 2019.2 (http://eprints.maths.manchester.ac.uk/2685/), Manchester Institute for Mathematical Sciences, The University of Manchester, UK, 2019.