Adaptive stepsize algorithms for Langevin dynamics

We discuss the design of an invariant measure-preserving transformation for the numerical treatment of Langevin dynamics based on a rescaling of time, with the goal of sampling from an invariant measure.

Given an appropriate monitor function which characterizes the numerical difficulty of the problem as a function of the state of the system, this method allows stepsizes to be reduced only when necessary, facilitating efficient recovery of long-time behavior. We study both overdamped and underdamped Langevin dynamics.

We investigate how an appropriate correction term that ensures preservation of the invariant measure should be incorporated into a numerical splitting scheme.

Finally, we demonstrate the use of the technique on several model systems, including a Bayesian sampling problem with a steep prior.