AAA rational approximation on a continuum

TL;DR

This paper introduces a continuum AAA algorithm that adaptively discretizes domains for fast, high-accuracy rational approximation, even near singularities, with efficient implementation and minimax improvement options.

Contribution

It presents a novel adaptive discretization method for AAA rational approximation on continuous domains, enabling faster and more accurate computations near singularities.

Findings

Fast computation of high-accuracy approximations on various domains

Effective handling of singularities with clustered sample points

Provision of open-source MATLAB, Octave, and Julia codes

Abstract

AAA rational approximation has normally been carried out on a discrete set, typically hundreds or thousands of points in a real interval or complex domain. Here we introduce a continuum AAA algorithm that discretizes a domain adaptively as it goes. This enables fast computation of high-accuracy rational approximations on domains such as the unit interval, the unit circle, and the imaginary axis, even in some cases where resolution of singularities requires exponentially clustered sample points, support points, and poles. Prototype MATLAB (or Octave) and Julia codes aaax, aaaz, and aaai are provided for these three special domains; the latter two are equivalent by a Moebius transformation. Execution is very fast since the matrices whose SVDs are computed have only three times as many rows as columns. The codes include a AAA-Lawson option for improvement of a AAA approximant to minimax, so long as the accuracy is well above machine precision. The result returned is pole-free in the approximation domain.