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.

arXiv comment35 pages, no figures

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Cited by in corpus (9)

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• 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
• Control of probability flow in Markov chain Monte Carlo -- Nonreversibility and lifting
• Direction-sweep Markov chains
• Convergence Rates of Attractive-Repulsive MCMC Algorithms