Analysis and Optimization of Population Annealing

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.

13 pages, 6 figures; journal version

References in corpus (6)

• Universality in three-dimensional Ising spin glasses: A Monte Carlo study
• Comparing Monte Carlo methods for finding ground states of Ising spin glasses: population annealing, simulated annealing and parallel tempering
• Constant-pressure nested sampling with atomistic dynamics
• Optimization of population annealing Monte Carlo for large-scale spin-glass simulations
• Correlations between the dynamics of parallel tempering and the free-energy landscape in spin glasses
• Dynamics of the Wang-Landau algorithm and complexity of rare events for the three-dimensional bimodal Ising spin glass

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• Normalizing flows as an enhanced sampling method for atomistic supercooled liquids
• Equilibrium Microcanonical Annealing for First-Order Phase Transitions
• Weighted averages in population annealing: analysis and general framework
• Melting of a two-dimensional monodisperse cluster crystal to a cluster liquid
• Optimal schedules for annealing algorithms
• Sparse Autoregressive Neural Networks for Classical Spin Systems
• Evidence of many thermodynamic states of the three-dimensional Ising spin glass
• Information-geometric optimization with natural selection
• Ground state interface exponents of the diluted Sherrington-Kirkpatrick spin glass
• Optimization and benchmarking of the thermal cycling algorithm
• Population annealing with topological defect driven nonlocal updates for spin systems with quenched disorder
• Sampling and statistical physics via symmetry
• Neural Mode Jump Monte Carlo