Simulation of Spin Models in Multicanonical Ensemble with Collective Updates

TL;DR

This paper introduces a Monte Carlo simulation method using collective updates in energy space, improving efficiency especially for long-range interaction models by combining with cluster algorithms.

Contribution

It presents a novel multicanonical Monte Carlo method with continuous bond probabilities, enhancing simulation efficiency for complex spin models.

Findings

Dynamic exponents close to ideal random walk values

Significant reduction in computational load for long-range models

Effective integration with Luijten-Bloete cluster algorithm

Abstract

We propose a Monte Carlo method which performs a random walk in energy space using cluster-like collective updates. By imposing that bond probabilities depend continuously on the microcanonical temperature, we obtain dynamic exponents close to their ideal random walk values. The method proves remarkably powerful when applied to models governed by long-range interactions, where it straightforwardly combines with the efficient Luijten-Bloete cluster algorithm to yield a dramatic reduction in the computation load.