A new efficient Cluster Algorithm for the Ising Model
Matthias Nyfeler, Michele Pepe, and Uwe-Jens Wiese (University of, Bern)
TL;DR
This paper introduces a novel, efficient cluster algorithm for the Ising model based on D-theory, which differs from traditional methods and enables high-precision correlation function measurements across a wide range.
Contribution
The paper presents a new cluster algorithm derived from D-theory, offering an alternative to standard algorithms like Swendsen-Wang and related to worm algorithms.
Findings
High-precision correlation function measurements achieved
Algorithm demonstrates efficiency over large scales
Different construction from standard cluster algorithms
Abstract
Using D-theory we construct a new efficient cluster algorithm for the Ising model. The construction is very different from the standard Swendsen-Wang algorithm and related to worm algorithms. With the new algorithm we have measured the correlation function with high precision over a surprisingly large number of orders of magnitude.
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