Multibondic Cluster Algorithm
Wolfhard Janke, Stefan Kappler

TL;DR
This paper introduces a novel multibondic cluster algorithm for q-state Potts models that combines cluster updates with reweighting, achieving near-optimal autocorrelation scaling for simulating first-order phase transitions.
Contribution
It proposes a new algorithm integrating cluster updates with reweighting in the Fortuin-Kasteleyn representation, improving simulation efficiency for first-order phase transitions.
Findings
Autocorrelation times grow as V^1, indicating optimal random walk behavior.
Numerical tests for q=7, 10, 20 demonstrate the algorithm's effectiveness.
The method enhances simulation performance for first-order phase transitions.
Abstract
Inspired by the multicanonical approach to simulations of first-order phase transitions we propose for -state Potts models a combination of cluster updates with reweighting of the bond configurations in the Fortuin-Kastelein-Swendsen-Wang representation of this model. Numerical tests for the two-dimensional models with and show that the autocorrelation times of this algorithm grow with the system size as , where the exponent takes the optimal random walk value of .
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