# Event-chain Monte Carlo with factor fields

## TL;DR

This paper introduces an improved event-chain Monte Carlo algorithm with factor fields for 1D particle systems, significantly reducing autocorrelation times and enhancing simulation efficiency for models like Lennard-Jones and hard spheres.

## Contribution

The paper proposes and validates the use of factor fields in ECMC for 1D models, reducing autocorrelation times from quadratic to square root scaling with system size.

## Key findings

- Autocorrelation times grow with the square root of system size using ECMC with factor fields.
- Mixing times scale linearly with system size.
- Validated on Lennard-Jones, harmonic, and hard sphere models.

## Abstract

We study the dynamics of one-dimensional (1D) interacting particles simulated with the event-chain Monte Carlo algorithm (ECMC). We argue that previous versions of the algorithm suffer from a mismatch in the factor potential between different particle pairs (factors) and show that in 1D models, this mismatch is overcome by factor fields. ECMC with factor fields is motivated, in 1D, for the harmonic model, and validated for the Lennard-Jones model as well as for hard spheres. In 1D particle systems with short-range interactions, autocorrelation times generally scale with the second power of the system size for reversible Monte Carlo dynamics, and with its first power for regular ECMC and for molecular-dynamics. We show, using numerical simulations, that they grow only with the square root of the systems size for ECMC with factor fields. Mixing times, which bound the time to reach equilibrium from an arbitrary initial configuration, grow with the first power of the system size.