Event-chain Monte Carlo with factor fields

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.

10 pages, 9 figures

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Cited by in corpus (10)

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• Molecular simulation from modern statistics: Continuous-time, continuous-space, exact
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• Lifted TASEP: a Bethe ansatz integrable paradigm for non-reversible Markov chains
• Resolving the two-dimensional ANNNI model using transfer matrices
• Velocity trapping in the lifted TASEP and the true self-avoiding random walk
• Emergent electrostatics in planar XY spin models: the bridge connecting topological order with broken $U(1)$ symmetry
• Bosonized one-dimensional quantum systems through enhanced event-chain Monte Carlo
• Lifted TASEP: long-time dynamics,generalizations, and continuum limit