The infinite Arnoldi exponential integrator for linear inhomogeneous ODEs

TL;DR

This paper introduces an infinite Arnoldi exponential integrator for linear inhomogeneous ODEs, extending Krylov methods to infinite-dimensional settings with proven convergence and practical finite-dimensional implementation.

Contribution

It develops a novel infinite-dimensional Krylov-based algorithm for inhomogeneous linear ODEs, generalizing existing methods for homogeneous cases and demonstrating convergence.

Findings

Algorithm effectively approximates matrix exponential products.

Convergence is proven under natural conditions.

Examples show applicability to PDE discretizations.

Abstract

Exponential integrators that use Krylov approximations of matrix functions have turned out to be efficient for the time-integration of certain ordinary differential equations (ODEs). This holds in particular for linear homogeneous ODEs, where the exponential integrator is equivalent to approximating the product of the matrix exponential and a vector. In this paper, we consider linear inhomogeneous ODEs, $y'(t)=Ay(t)+g(t)$, where the function $g(t)$ is assumed to satisfy certain regularity conditions. We derive an algorithm for this problem which is equivalent to approximating the product of the matrix exponential and a vector using Arnoldi's method. The construction is based on expressing the function $g(t)$ as a linear combination of given basis functions $[\phi_i]_{i=0}^\infty$ with particular properties. The properties are such that the inhomogeneous ODE can be restated as an infinite-dimensional linear homogeneous ODE. Moreover, the linear homogeneous infinite-dimensional ODE has properties that directly allow us to extend a Krylov method for finite-dimensional linear ODEs. Although the construction is based on an infinite-dimensional operator, the algorithm can be carried out with operations involving matrices and vectors of finite size. This type of construction resembles in many ways the infinite Arnoldi method for nonlinear eigenvalue problems. We prove convergence of the algorithm under certain natural conditions, and illustrate properties of the algorithm with examples stemming from the discretization of partial differential equations.