Population Annealing with Weighted Averages: A Monte Carlo Method for Rough Free Energy Landscapes

Details Population Annealing with Weighted Averages: A Monte Carlo Method for Rough Free Energy Landscapes Population Annealing with Weighted Averages: A Monte Carlo Method for Rough Free Energy Landscapes

2010-06-01

arxiv.org/abs/1006.0252v2

Phys. Rev. E 82, 026704 (2010)

Physics

Condensed Matter

Statistical Mechanics

9 pages, 5 figures; version 2 has improved figure 5 and new title

Scientific paper

10.1103/PhysRevE.82.026704

The population annealing algorithm introduced by Hukushima and Iba is described. Population annealing combines simulated annealing and Boltzmann weighted differential reproduction within a population of replicas to sample equilibrium states. Population annealing gives direct access to the free energy. It is shown that unbiased measurements of observables can be obtained by weighted averages over many runs with weight factors related to the free energy estimate from the run. Population annealing is well suited to parallelization and may be a useful alternative to parallel tempering for systems with rough free energy landscapes such as spin glasses. The method is demonstrated for spin glasses.

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