Event-Chain Monte Carlo (ECMC) methods generate continuous-time and non-reversible Markov processes, which often display significant accelerations compared to their reversible counterparts. However, their generalization to any system may appear less straightforward. In this work, our aim is to distinctly define the essential symmetries that such ECMC algorithms must adhere to, differentiating between necessary and sufficient conditions. This exploration intends to delineate the balance between requirements that could be overly limiting in broad applications and those that are fundamentally essential. To do so, we build on the recent analytical description of such methods as generating piecewise deterministic Markov processes. Therefore, starting with translational flows, we establish the necessary rotational invariance of the probability flows, along with determining the minimum event rate. This rate is identified with the corresponding infinitesimal Metropolis rejection rate. Obeying such conditions ensures the correct invariance for any ECMC scheme. Subsequently, we extend these findings to encompass schemes involving deterministic flows that are more general than mere translational ones. Specifically, we define two classes of interest of general flows: the ideal and uniform-ideal ones. They, respectively, suppress or reduce the event rates. From there, we implement a comprehensive non-reversible sampling of a system of hard dimers by introducing rotational flows, which are uniform-ideal. This implementation results in a speed-up of up to ∼3 compared to the state-of-the-art ECMC/Metropolis hybrid scheme.
REFERENCES
In more details, the changes are and .
