Feedback-optimized parallel tempering Monte Carlo

TL;DR

This paper presents a feedback-optimized parallel tempering Monte Carlo method that adaptively adjusts temperature sets to minimize round-trip times and improve sampling efficiency, especially near phase transitions.

Contribution

The authors introduce a novel feedback-based algorithm for optimizing temperature distributions in parallel tempering, enhancing efficiency over traditional fixed schemes.

Findings

Optimized temperature sets increase at phase transition points.

The method reduces round-trip times significantly.

Acceptance rates become temperature-dependent in the optimized ensemble.

Abstract

We introduce an algorithm to systematically improve the efficiency of parallel tempering Monte Carlo simulations by optimizing the simulated temperature set. Our approach is closely related to a recently introduced adaptive algorithm that optimizes the simulated statistical ensemble in generalized broad-histogram Monte Carlo simulations. Conventionally, a temperature set is chosen in such a way that the acceptance rates for replica swaps between adjacent temperatures are independent of the temperature and large enough to ensure frequent swaps. In this paper, we show that by choosing the temperatures with a modified version of the optimized ensemble feedback method we can minimize the round-trip times between the lowest and highest temperatures which effectively increases the efficiency of the parallel tempering algorithm. In particular, the density of temperatures in the optimized temperature set increases at the "bottlenecks'' of the simulation, such as phase transitions. In turn, the acceptance rates are now temperature dependent in the optimized temperature ensemble. We illustrate the feedback-optimized parallel tempering algorithm by studying the two-dimensional Ising ferromagnet and the two-dimensional fully-frustrated Ising model, and briefly discuss possible feedback schemes for systems that require configurational averages, such as spin glasses.