Cutoff for mixtures of permuted Markov chains: reversible case

TL;DR

This paper studies the cutoff phenomenon in reversible Markov chains in random environments, extending previous results and introducing new concentration techniques for symmetric groups.

Contribution

It proves cutoff at entropic time for a broad class of reversible Markov chains with high probability, using novel concentration results.

Findings

Chains exhibit cutoff at entropic time log n/h with high probability

Most proofs do not require reversibility, broadening applicability

Introduces a new concentration result for low-degree functions on the symmetric group

Abstract

We investigate the mixing properties of a model of reversible Markov chains in random environment, which notably contains the simple random walk on the superposition of a deterministic graph and a second graph whose vertex set has been permuted uniformly at random. It generalizes in particular a result of Hermon, Sly and Sousi, who proved the cutoff phenomenon at entropic time for the simple random walk on a graph with an added uniform matching. Under mild assumptions on the base Markov chains, we prove that with high probability the resulting chain exhibits the cutoff phenomenon at entropic time log n/h, h being some constant related to the entropy of the chain. We note that the results presented here are the consequence of a work conducted for a more general model that does not assume reversibility, which will be the object of a companion paper. Thus, most of our proofs do not actually require reversibility, which constitutes an important technical contribution. Finally, our argument relies on a novel concentration result for "low-degree" functions on the symmetric group, established specifically for our purpose but which could be of independent interest.