Monte Carlo Algorithms For the Fully Frustrated XY Model

TL;DR

This paper explores local update algorithms, including overrelaxation, for the fully frustrated XY model, showing they can reduce autocorrelation times without changing the critical exponent.

Contribution

It introduces overrelaxation sweeps into local algorithms for the model, demonstrating improved efficiency in sampling configurations.

Findings

Autocorrelation times are significantly reduced.

The dynamical critical exponent remains around two.

Overrelaxation improves sampling efficiency.

Abstract

We investigate local update algorithms for the fully frustrated XY model on a square lattice. In addition to the standard updating procedures like the Metropolis or heat bath algorithm we include overrelaxation sweeps, implemented through single spin updates that preserve the energy of the configuration. The dynamical critical exponent (of order two) stays more or less unchanged. However, the integrated autocorrelation times of the algorithm can be significantly reduced.