Event-chain Monte Carlo and the true self-avoiding walk

TL;DR

This paper investigates the large-scale behavior of event chain Monte Carlo algorithms in one dimension, exploring their connection to the true self-avoiding walk and how stress and interactions affect their efficiency.

Contribution

It provides new insights into the dynamics of event chain Monte Carlo algorithms and their relation to self-avoiding walks, focusing on global balance without local balance.

Findings

Stress influences algorithm dynamics

Interactions affect equilibration and sampling

Connection to true self-avoiding walk elucidated

Abstract

We study the large-scale dynamics of event chain Monte Carlo algorithms in one dimension, and their relation to the true self-avoiding walk. In particular, we study the influence of stress, and different forms of interaction on the equilibration and sampling properties of algorithms with global balance, but no local balance.