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article · Scholarworks (University of Massachusetts Amherst) · 2023

Efficiency of Parallel Tempering for Ising Systems

Stephan Burkhardt

Cited by 0Open access

Abstract

The efficiency of parallel tempering Monte Carlo is studied for a two-dimensional Ising system of length L with N=L^2 spins. An external field is used to introduce a difference in free energy between the two low temperature states.\nIt is found that the number of replicas R_opt that optimizes the parallel tempering algorithm scales as the square root of the system size N. For two symmetric low temperature states, the time needed for equilibration is observed to grow as L^2.18. If a significant difference in free energy is present between the two states, this changes to L^1.02.\nIt is therefore established that parallel tempering is sped up by a factor of roughly L if an asymmetry is introduced between the low temperature states. This confirms previously made predictions for the efficiency of parallel tempering. These findings should be especially relevant when using parallel tempering for systems like spin glasses, where no information about the degeneracy of low temperature states is available prior to the simulation.

Cite this paper

APA 7

Burkhardt, S. (2023). Efficiency of Parallel Tempering for Ising Systems. Scholarworks (University of Massachusetts Amherst). https://doi.org/10.7275/1467366

MLA 9

Burkhardt, Stephan. “Efficiency of Parallel Tempering for Ising Systems.” Scholarworks (University of Massachusetts Amherst), 2023, https://doi.org/10.7275/1467366.

Chicago

Burkhardt, Stephan. 2023. “Efficiency of Parallel Tempering for Ising Systems.” Scholarworks (University of Massachusetts Amherst). https://doi.org/10.7275/1467366.

Harvard

Burkhardt, S. (2023) ‘Efficiency of Parallel Tempering for Ising Systems’, Scholarworks (University of Massachusetts Amherst). Available at: https://doi.org/10.7275/1467366.

BibTeX
@article{burkhardt2023efficiency,
  title = {{Efficiency of Parallel Tempering for Ising Systems}},
  author = {Burkhardt, Stephan},
  journal = {Scholarworks (University of Massachusetts Amherst)},
  year = {2023},
  doi = {10.7275/1467366}
}

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