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Event-Chain Monte Carlo methods generate continuous-time and non-reversible Markov processes which often display important accelerations compared to their reversible counterparts. However their generalization to any system may appear less straightforward. In this work, we build on the recent analytical characterization of such methods as generating Piecewise Deterministic Markov Processes (PDMP) to clearly decipher the necessary symmetries the PDMP must obey from the sufficient ones which may prove to be too restrictive in a general setting. Thus, we derive a necessary rotational invariance of the probability flows and the minimum event rate, which identifies with the corresponding infinitesimal rejection rate. Such conditions always yield a correct ECMC scheme. We then generalize such results to the case of more general deterministic flows than the translational ones. In particular, we define two classes of interest of general flows, the ideal and uniform-ideal ones, which respectively suppresses or reduces the event rates. From there, we implement a complete non-reversible sampling of a systems of hard dimers, thanks to the introduction of rotational flows, which are uniform-ideal and shows a speed-up of up to ~3 compared to the state-of-the-art ECMC/Metropolis hybrid scheme. |
