Binary tree summation Monte Carlo method for Potts models
Jian-Sheng Wang, Oner Kozan, and Robert H. Swendsen
TL;DR
This paper introduces a novel Monte Carlo sampling algorithm for Potts models utilizing the Fortuin-Kasteleyn transformation, enabling efficient computation of thermodynamic properties across all temperatures from a single run.
Contribution
The paper presents a new binary tree summation Monte Carlo method that produces independent samples and efficiently computes thermodynamic averages for Potts models.
Findings
Accurate results for the 2D Ising model comparison
Single-run computation of partition function across temperatures
Method produces independent samples and sums configurations
Abstract
We give a new sampling algorithm for the Potts model based on the Fortuin-Kasteleyn transformation. The method produces independent samples and sums up a large number of configurations for each sweep. The partition function and thermodynamic averages for all values of the temperature can be computed from a single run. We compare the results with exact 2D Ising model.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTheoretical and Computational Physics · Stochastic processes and statistical mechanics · Markov Chains and Monte Carlo Methods