Resampling schemes in population annealing -- numerical results

TL;DR

This paper compares different resampling schemes in population annealing algorithms through numerical experiments on the 2D Ising model, aiming to optimize equilibrium simulations of complex thermodynamic systems.

Contribution

It provides a systematic numerical comparison of resampling schemes in population annealing, addressing a gap in understanding their impact on simulation performance.

Findings

Certain resampling schemes outperform others in efficiency

Resampling choice significantly affects equilibrium sampling accuracy

Optimal resampling strategies depend on system parameters

Abstract

Population annealing (PA) is a population-based algorithm that is designed for equilibrium simulations of thermodynamic systems with a rough free energy landscape. It is known to be more efficient in doing so than standard Markov chain Monte Carlo alone. The algorithm has a number of parameters that can be fine-tuned to improve performance. While there is some theoretical and numerical work regarding most of these parameters, there appears to be a gap in the literature concerning the role of resampling in PA. Here, we present a numerical comparison of a number of resampling schemes for PA simulations of the 2D Ising model.