Variance-reduced multiscale simulation of slow-fast stochastic differential equations

We study a variance reduction strategy based on control variables for simulating the averaged macroscopic behavior of a stochastic slow-fast system. We assume that this averaged behavior can be written in terms of a few slow degrees of freedom, and that the fast dynamics is ergodic for every fixed value of the slow variable. The time derivative for the averaged dynamics can then be approximated by a Markov chain Monte Carlo method. The variance-reduced scheme that is introduced here uses the previous time instant as a control variable. We analyze the variance and bias of the proposed estimator and illustrate its performance when applied to a linear and nonlinear model problem.