Convergence and stability of a micro-macro acceleration method: Linear slow-fast stochastic differential equations with additive noise

We analyze the convergence and stability of a micro-macro acceleration algorithm for Monte Carlo simulations of linear stiff stochastic differential equations with a time scale separation between the fast evolution of the individual stochastic realizations and some slow macroscopic state variables of the process. The micro-macro acceleration method performs a short simulation of a large ensemble of individual fast paths, before extrapolating the macroscopic state variables of interest over a larger time step. After extrapolation, the method constructs a new probability distribution that is consistent with the extrapolated macroscopic state variables, while minimizing Kullback-Leibler divergence with respect to the distribution available at the end of the Monte Carlo simulation. In the current work, we study the convergence and stability of this method on linear stochastic differential equations with additive noise, when only extrapolating the mean of the slow component. For this case, we prove convergence to the microscopic dynamics when the initial distribution is Gaussian and present a stability result for non-Gaussian initial laws. (C) 2019 Elsevier B.V. All rights reserved.

Automated summary

The study examines the convergence and stability of a micro-macro acceleration algorithm for Monte Carlo simulations of linear stiff stochastic differential equations with time scale separation. The method involves short simulations of individual fast paths, extrapolation of macroscopic state variables, and constructing a new probability distribution to minimize Kullback-Leibler divergence. The convergence to microscopic dynamics and stability results for Gaussian and non-Gaussian initial laws are discussed in the context of linear stochastic differential equations with additive noise.