Analysis and Optimization of Population Annealing

TL;DR

This paper analyzes and optimizes population annealing, a parallelizable Monte Carlo method, improving its efficiency and accuracy in simulating complex systems like the 3D Edwards-Anderson spin glass.

Contribution

It derives relations to optimize population annealing and demonstrates significant efficiency improvements through large-scale simulations.

Findings

Reduced computational work with optimized methods

More accurate measurements of spin glass observables

Enhanced understanding of population annealing performance

Abstract

Population annealing is an easily parallelizable sequential Monte Carlo algorithm that is well-suited for simulating the equilibrium properties of systems with rough free energy landscapes. In this work we seek to understand and improve the performance of population annealing. We derive several useful relations between quantities that describe the performance of population annealing and use these relations to suggest methods to optimize the algorithm. These optimization methods were tested by performing large-scale simulations of the 3D Edwards-Anderson (Ising) spin glass and measuring several observables. The optimization methods were found to substantially decrease the amount of computational work necessary as compared to previously used, unoptimized versions of population annealing. We also obtain more accurate values of several important observables for the 3D Edwards-Anderson model.