Optimal Modification Factor and Convergence of the Wang-Landau Algorithm

TL;DR

This paper introduces an optimal strategy for the Wang-Landau algorithm that guarantees convergence rates comparable to traditional Monte Carlo methods, with a proven lower bound on the error decay rate.

Contribution

It proposes a new modification factor strategy for the Wang-Landau algorithm that optimizes convergence speed and provides theoretical error bounds.

Findings

Convergence rate matches that of conventional Monte Carlo methods.

Error cannot decrease faster than 1/t, establishing a lower bound.

The strategy aligns with recent findings on the 1/t Wang-Landau algorithm.

Abstract

We propose a strategy to achieve the fastest convergence in the Wang-Landau algorithm with varying modification factors. With this strategy, the convergence of a simulation is at least as good as the conventional Monte Carlo algorithm, i.e. the statistical error vanishes as $1/\sqrt{t}$, where $t$ is a normalized time of the simulation. However, we also prove that the error cannot vanish faster than $1/t$. Our findings are consistent with the $1/t$ Wang-Landau algorithm discovered recently, and we argue that one needs external information in the simulation to beat the conventional Monte Carlo algorithm.