Fast computation of the matrix exponential for a Toeplitz matrix

TL;DR

This paper introduces fast algorithms for computing the exponential of Toeplitz matrices, leveraging their structure to achieve quadratic complexity under certain spectral conditions, with applications in financial modeling.

Contribution

It presents novel, efficient algorithms for Toeplitz matrix exponentials that improve computational speed using spectral analysis and rational approximation theory.

Findings

Quadratic complexity algorithms for real or sectorial spectra

Effective even outside spectral constraints

Application to option pricing models

Abstract

The computation of the matrix exponential is a ubiquitous operation in numerical mathematics, and for a general, unstructured $n\times n$ matrix it can be computed in $\mathcal{O}(n^3)$ operations. An interesting problem arises if the input matrix is a Toeplitz matrix, for example as the result of discretizing integral equations with a time invariant kernel. In this case it is not obvious how to take advantage of the Toeplitz structure, as the exponential of a Toeplitz matrix is, in general, not a Toeplitz matrix itself. The main contribution of this work are fast algorithms for the computation of the Toeplitz matrix exponential. The algorithms have provable quadratic complexity if the spectrum is real, or sectorial, or more generally, if the imaginary parts of the rightmost eigenvalues do not vary too much. They may be efficient even outside these spectral constraints. They are based on the scaling and squaring framework, and their analysis connects classical results from rational approximation theory to matrices of low displacement rank. As an example, the developed methods are applied to Merton's jump-diffusion model for option pricing.