paper

Sampling Colorings Close to the Maximum Degree: Non-Markovian Coupling and Local Uniformity

arXiv:2604.11938

Abstract

Sampling graph colorings via local Markov chains is a central problem in approximate counting and Markov chain Monte Carlo (MCMC). We address the problem of sampling a random -coloring of a graph with maximum degree . The simplest algorithmic approach is to establish rapid mixing of the single-site update chain known as the Metropolis Glauber dynamics, which at each step chooses a random vertex and proposes a random color , recoloring to if the resulting coloring remains proper. It is a long-standing open problem to prove that the Glauber dynamics has polynomial mixing time on all graphs whenever . We prove that for every and all , if then the Glauber dynamics has optimal mixing time of on any graph of girth and maximum degree . Our approach builds on a non-Markovian coupling introduced by Hayes and Vigoda (2003) for the large-degree regime and girth , in which updates at time may depend on and modify proposed updates at future times. A complete analysis of this framework requires resolving substantial technical obstacles that remain in the original argument, and extending it to the constant-degree regime introduces further difficulties, since non-Markovian updates may fail with constant probability. We overcome these obstacles by developing and analyzing a refined local non-Markovian coupling, and by establishing new local-uniformity results for the Metropolis dynamics, extending prior results for the heat-bath chain due to Hayes (2013). Together, these ingredients provide a complete analysis of the non-Markovian coupling framework in the large-degree regime, while simultaneously strengthening it substantially to obtain optimal mixing all the way down to the constant-degree setting.

This version: improved the girth constraint from 11 to 7 by minor modifications of the arguments in v1