Error Control for Exponential Integration of the Master Equation

TL;DR

This paper introduces new error estimation techniques for exponential integration of the master equation, enabling adaptive algorithms that improve accuracy without excessive computational cost.

Contribution

It presents a novel error estimation method compatible with Krylov approximations, applicable to both time-dependent and independent propensity functions.

Findings

Effective error estimates for exponential integrators.

Adaptive simulation algorithm improves accuracy and efficiency.

Numerical examples demonstrate practical benefits.

Abstract

Error estimates for the numerical solution of the master equation are presented. Estimates are based on adjoint methods. We find that a good estimate can often be computed without spending computational effort on a dual problem. Estimates are applicable to both settings with time-independent, and time-dependent propensity functions. The Finite State Projection algorithm reduces the dimensionality of the problem and time propagation is based on an Arnoldi exponential integrator, which in the time-dependent setting is combined with a Magnus method. Local error estimates are devised for the truncation of both the Magnus expansion and the Krylov subspace in the Arnoldi algorithm. An issue with existing methods is that error estimates for truncation of the state space depend on measuring a loss of probability mass in a way that is not usually compatible with the approximation of the exponential. We suggest an alternative error estimate that is compatible with a Krylov approximation to the matrix exponential. Finally, we apply the new error estimates to develop an adaptive simulation algorithm. Numerical examples demonstrate the benefits of the approach.