# Pole optimization for exponential integration

### Mario Berljafa and Stefan Güttel, May 2015Download PDF or m-file

## Contents

## Introduction

This example is concered with the computation of a family of common-denominator rational approximants for the two-parameter function using RKFIT [2, 3]. This corresponds to the example from [3, Section 6.2]. Let us consider the problem of solving a linear constant-coefficient initial-value problem

at several time points . The exact solutions are given in terms of the matrix exponential as . A popular approach for approximating is to use rational functions of the form

constructed so that . Note that the poles of do not depend on and we have

the evaluation of which amounts to the solution of linear systems. Such common-pole approximants have great computational advantage, in particular, in combination with direct solvers (as the LU factorizations of can be reused for each ) and when the linear systems are assigned to parallel processors.

## Surrogate approach

In order to use RKFIT for finding "good" poles of the rational functions , we propose a surrogate approach similar to that in [4]. Let be a diagonal matrix whose eigenvalues are a "sufficiently dense" discretization of the positive semiaxis . In this example we take logspaced eigenvalues in the interval . Further, we define logspaced time points in the interval , and the matrices . We also define to assign equal weight to each eigenvalue of .

N = 500; ee = [0 , logspace(-6, 6, N-1)]; A = spdiags(ee(:), 0, N, N); b = ones(N, 1); t = logspace(-1, 1, 41); for j = 1:length(t) F{j} = spdiags(exp(-t(j)*ee(:)), 0, N, N); end

We then run the RKFIT algorithm for finding a family of rational functions of type with so that is minimized for all .

m = 12; k = -1; % type (11, 12) xi = inf(1,m); % initial poles at infinity param.k = k; % subdiagonal approximant param.maxit = 6; % at most 6 RKFIT iterations param.tol = 0; % exactly 6 iterations param.real = 1; % data is real-valued [xi, ratfun, misfit, out] = rkfit(F, A, b, xi, param);

## The RKFIT outputs

The first output argument of RKFIT is a vector `xi` collecting the poles of the rational Krylov space. The second output `ratfun` is a cell array each cell of which is a `rkfun`, a datatype representing a rational function. All rational functions in this cell array share the same denominator with roots . The next output parameter is a vector containing the computed relative misfit after each RKFIT iteration. The relative misfit is defined as (cf. eq. (1.5) in [3])

We can easily verify that the last entry of `misfit` indeed corresponds to this formula:

num = 0; den = 0; for j = 1:length(ratfun) num = num + norm(F{j}*b - ratfun{j}(A,b), 'fro')^2; den = den + norm(F{j}*b, 'fro')^2; end disp([misfit(end) sqrt(num/den)])

3.6468e-05 3.6468e-05

Here is a plot of the `misfit` vector, giving an idea of the RKFIT convergence:

figure semilogy(0:6, [out.misfit_initial, misfit]*sqrt(den), 'r-'); xlabel('iteration'); ylabel('absmisfit') title('RKFIT convergence')

## Verifing the accuracy

To evaluate the quality of the common-denominator rational approximants for all time points , we perform an experiment similar to that in [5, Figure 6.1] by approximating and comparing the result to MATLAB's `expm`. Here, is a finite-difference discretization of the scaled 2D Laplace operator on the domain with homogeneous Dirichlet boundary condition, and corresponds to the discretization of on that domain.

% Parts of the following code have been taken from [5]. J = 30; h = 2/J; s = (-1+h:h:1-h)'; % in [3,5] J = 50 is used [xx,yy] = meshgrid(s,s); % 2D grid x = xx(:); y = yy(:); % 2D grid stretched to 1D L = 0.02*gallery('poisson',J-1)/h^2; % 2D Laplacian v = (1-x.^2).*(1-y.^2).*exp(x); % initial condition v = v/norm(v); for j = 1:length(t) exac(:,j) = expm(-t(j)*L)*v; rat = ratfun{j}(L,v); err_rat(j) = norm(rat - exac(:,j)); bnd(j) = norm(ratfun{j}(A,b) - F{j}*b,inf); end

We now plot the error for each time point (curve with red circles), together with the approximate upper error bound (black curve), which can be easily computed by direct evaluation. We find that the error is indeed approximately uniform and smaller than over the time interval .

figure loglog(t, bnd, 'k-') hold on loglog(t, err_rat, 'r-o') xlabel('time t'); ylabel('2-norm error') legend('RKFIT Bound', 'RKFIT PFE', 'Location', 'NorthWest') title('approximating exp(-tL)u_0 for many t') grid on axis([0.1, 10, 1e-7, 1e6])

## Conversion to partial fraction form

When evaluating the rational functions on a parallel computer, it is convenient to have their partial fraction expansions at hand. The `rkfun` class provides a method called `residue` for this purpose. This method supports the use of MATLAB's variable precision (VPA) capabilities, or the Advanpix Multiple Precision (MP) toolbox [1].

For example, here are the residues and poles of the first rational function corresponding to :

try mp(1); catch e, try mp=@(x)vpa(x); mp(1); catch e, mp=@(x)x; warning('MP & VPA are unavailable. Using double.'); end, end [resid, xi, absterm, cnd, pf] = residue(mp(ratfun{1})); disp(double([resid , xi]))

1.1058e-01 - 4.2664e-03i -3.9348e-01 + 1.9680e-01i 1.1058e-01 + 4.2664e-03i -3.9348e-01 - 1.9680e-01i 1.2375e-01 + 9.7078e-03i -2.7724e-01 + 6.4704e-01i 1.2375e-01 - 9.7078e-03i -2.7724e-01 - 6.4704e-01i 2.7683e-01 - 2.6172e-02i -1.3284e-01 + 1.6788e+00i 2.7683e-01 + 2.6172e-02i -1.3284e-01 - 1.6788e+00i 5.0072e-01 - 4.3971e-01i 5.8293e-01 + 4.5857e+00i 5.0072e-01 + 4.3971e-01i 5.8293e-01 - 4.5857e+00i -2.1039e-01 - 1.1989e+00i 4.0705e+00 + 1.1486e+01i -2.1039e-01 + 1.1989e+00i 4.0705e+00 - 1.1486e+01i -7.7942e-01 + 2.9661e-01i 1.7954e+01 + 2.5464e+01i -7.7942e-01 - 2.9661e-01i 1.7954e+01 - 2.5464e+01i

## Comparison with contour-based approach

We now compare RKFIT with the accuracy of the contour-based rational approximants derived in [5]. As discussed there, this approach leads to approximants which are very accurate near , but their accuracy degrades rapidly as one moves away from this parameter.

% Contour integral code from [5]. NN = 12; theta = pi*(1:2:NN-1)/NN; % quad pts in (0, pi) z = NN*(.1309-.1194*theta.^2); % quad pts on contour z = z + NN*.2500i*theta; w = NN*(-.1194*2*theta+.2500i); % derivatives for j = 1:length(t) c = (1i/NN)*exp(t(j)*z).*w; % quadrature weights appr = zeros(size(v)); for k = 1:NN/2, % sparse linear solves appr = appr - c(k)*((z(k)*speye(size(L))+L)\v); end appr = 2*real(appr); % exploit symmetry err_cont(j) = norm(appr-exac(:,j)); end loglog(t,err_cont,'b--s') legend('RKFIT Bound', 'RKFIT PFE', 'Contour PFE', ... 'Location', 'NorthWest')

## Plot of the poles

Finally, the poles of the rational functions are shown in the following plot. We can see that the "optimal" RKFIT poles do not seem to lie on a parabolic contour.

figure hh1 = plot(xi, 'ro'); axis([-3, 12, -13, 13]) hold on % Also plot the contour. theta = linspace(0, 2*pi, 300); zz = -NN*(.1309-.1194*theta.^2+.2500i*theta); plot(zz, 'b-') plot(conj(zz), 'b-') hh2 = plot(-[z, conj(z)], 'bs'); plot([0, 1e3], [0, 0], 'k-', 'LineWidth', 3) xlabel('real'), ylabel('imag') title('poles of rational approximants for exp(-tz)') grid on legend([hh1, hh2], 'RKFIT', 'Contour', 'Location', 'NorthWest')

## References

[1] Advanpix LLC., *Multiprecision Computing Toolbox for MATLAB,* ver 3.8.3.8882, Tokyo, Japan, 2015. http://www.advanpix.com/.

[2] M. Berljafa and S. Güttel. *A Rational Krylov Toolbox for MATLAB,* MIMS EPrint 2014.56 (http://eprints.ma.man.ac.uk/2390/), Manchester Institute for Mathematical Sciences, The University of Manchester, UK, 2014.

[3] M. Berljafa and S. Güttel. *The RKFIT algorithm for nonlinear rational approximation,* SIAM J. Sci. Comput., 39(5):A2049--A2071, 2017.

[4] R.-U. Börner, O. G. Ernst, and S. Güttel. *Three-dimensional transient electromagnetic modeling using rational Krylov methods,* Geophys. J. Int., 202(3):2025--2043, 2015. Available also as MIMS EPrint 2014.36 (http://eprints.ma.man.ac.uk/2219/).

[5] L. N. Trefethen, J. A. C. Weideman, and T. Schmelzer. *Talbot quadratures and rational approximations,* BIT, 46(3):653--670, 2006.