Analysis and optimization of population annealing

Physical Review. E
Christopher Amey, Jonathan Machta

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 three-dimensional (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.

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Feb 16, 2013·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Burcu YucesoyHelmut G Katzgraber
Aug 15, 2015·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Wenlong WangHelmut G Katzgraber
Jan 15, 2016·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Wenlong WangHelmut G Katzgraber
Jul 16, 2017·Physical Review. E·Jared Callaham, Jonathan Machta
Jan 20, 2018·Physical Review. E·Robert J N BaldockGábor Csányi

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Citations

Jan 23, 2020·Physical Review. E·Nathan Rose, Jonathan Machta
May 22, 2019·Physical Review. E·Wenlong WangEgor Babaev
Dec 9, 2020·Entropy·Jakub OtwinowskiArmita Nourmohammad
Feb 21, 2021·The Journal of Chemical Physics·Luigi SbailòFrank Noé
Jun 18, 2021·Physical Review. E·Martin WeigelWolfhard Janke

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