A large deviations analysis of certain qualitative properties of parallel tempering and infinite swapping algorithms

TL;DR

This paper applies large deviations theory to analyze the infinite swapping limit in parallel tempering algorithms, providing new diagnostics and insights into sampling efficiency and landscape asymmetries.

Contribution

It introduces a novel large deviations framework for empirical measures to analyze infinite swapping and proposes a diagnostic for convergence based on temperature assignments.

Findings

The diagnostic converges only if infinite swapping converges.

Asymmetries in the potential landscape affect sampling efficiency.

The rate function helps identify regions prone to poor sampling.

Abstract

Parallel tempering, or replica exchange, is a popular method for simulating complex systems. The idea is to run parallel simulations at different temperatures, and at a given swap rate exchange configurations between the parallel simulations. From the perspective of large deviations it is optimal to let the swap rate tend to infinity and it is possible to construct a corresponding simulation scheme, known as infinite swapping. In this paper we propose a novel use of large deviations for empirical measures for a more detailed analysis of the infinite swapping limit in the setting of continuous time jump Markov processes. Using the large deviations rate function and associated stochastic control problems we consider a diagnostic based on temperature assignments, which can be easily computed during a simulation. We show that the convergence of this diagnostic to its a priori known limit is a necessary condition for the convergence of infinite swapping. The rate function is also used to investigate the impact of asymmetries in the underlying potential landscape, and where in the state space poor sampling is most likely to occur.