Event-chain Monte Carlo: foundations, applications, and prospects

Abstract

This review treats the mathematical and algorithmic foundations of non-reversible Markov chains in the context of event-chain Monte Carlo (ECMC), a continuous-time lifted Markov chain that employs the factorized Metropolis algorithm. It analyzes a number of model applications, and then reviews the formulation as well as the performance of ECMC in key models in statistical physics. Finally, the review reports on an ongoing initiative to apply the method to the sampling problem in molecular simulation, that is, to real-world models of peptides, proteins, and polymers in aqueous solution.

35 pages, no figures

References in corpus (7)

• Monte Carlo study of an improved clock model in three dimensions
• Efficient Equilibration of Hard Spheres with Newtonian Event Chains
• TASEP on a ring in sub-relaxation time scale
• Parallelized event chain algorithm for dense hard sphere and polymer systems
• Event-Chain Monte-Carlo Simulations of Dense Soft Matter Systems
• Chaining of hard disks in nematic needles: particle-based simulation of colloidal interactions in liquid crystals
• Anomalous diffusion analysis of the lifting events in the event-chain Monte Carlo for the classical XY models

Cited by in corpus (6)

• Modern computational studies of the glass transition
• Fast, hierarchical, and adaptive algorithm for Metropolis Monte Carlo simulations of long-range interacting systems
• Hard-disk dipoles and non-reversible Markov chains
• Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning
• Large-scale dynamics of event-chain Monte Carlo
• Sparse hard-disk packings and local Markov chains