Local Geometric Mixing via Dobrushin Contraction with Applications to Diffusion Path Monte Carlo and the Proximal Sampler

Abstract:Local geometric mixing localizes geometric mixing by requiring geometric convergence to equilibrium in total variation only over finitely many transitions. It accommodates local convergence rates and captures rapid local equilibration, even when global mixing is much slower. We establish and discuss local geometric mixing bounds through Dobrushin contraction. We then apply this approach to Diffusion Path Monte Carlo, a recently proposed Markov chain Monte Carlo method, aimed at leveraging advances in score-based modeling, whose ideal transitions coincide with those of the Proximal Sampler. Our analysis covers both the ideal method and its implementable Metropolis-adjusted counterpart, providing mixing guarantees under minimal assumptions. For the ideal method, these guarantees complement recent spectral gap estimates, which we develop into mixing time bounds.

Probability (math.PR); Machine Learning (cs.LG); Computation (stat.CO); Machine Learning (stat.ML)

arXiv:2609.28338 [math.PR]

(or arXiv:2609.28338v1 [math.PR] for this version)

https://doi.org/10.48550/arXiv.2609.28338 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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