English

Resonances in reflective Hamiltonian Monte Carlo

Machine Learning 2026-03-20 v1 Statistical Mechanics Machine Learning Dynamical Systems

Abstract

In high dimensions, reflective Hamiltonian Monte Carlo with inexact reflections exhibits slow mixing when the particle ensemble is initialised from a Dirac delta distribution and the uniform distribution is targeted. By quantifying the instantaneous non-uniformity of the distribution with the Sinkhorn divergence, we elucidate the principal mechanisms underlying the mixing problems. In spheres and cubes, we show that the collective motion transitions between fluid-like and discretisation-dominated behaviour, with the critical step size scaling as a power law in the dimension. In both regimes, the particles can spontaneously unmix, leading to resonances in the particle density and the aforementioned problems. Additionally, low-dimensional toy models of the dynamics are constructed which reproduce the dominant features of the high-dimensional problem. Finally, the dynamics is contrasted with the exact Hamiltonian particle flow and tuning practices are discussed.

Keywords

Cite

@article{arxiv.2504.12374,
  title  = {Resonances in reflective Hamiltonian Monte Carlo},
  author = {Namu Kroupa and Gábor Csányi and Will Handley},
  journal= {arXiv preprint arXiv:2504.12374},
  year   = {2026}
}