Sampling independent sets in the discrete torus
David Galvin

TL;DR
This paper investigates the mixing time of Glauber dynamics for the hard-core model on high-dimensional discrete tori, showing slow convergence for certain activity levels and revealing phase coexistence phenomena.
Contribution
It extends previous results by establishing exponential slow mixing for a broader range of activity parameters and introduces combinatorial enumeration methods to analyze phase coexistence.
Findings
Glauber dynamics mixes slowly for activity x > cd^{-1/4}log^{3/4}d in high dimensions.
The probability of balanced independent sets (equal vertices in bipart partition) is exponentially small.
Results imply phase coexistence and large fluctuations in independent set sizes.
Abstract
The even discrete torus is the graph T_{L,d} on vertex set {0,...,L-1}^d (L even) with two vertices adjacent if they differ by 1 (mod L) on one coordinate. The hard-core measure with activity x on T_{L,d} is the distribution pi_x on the independent sets (sets of vertices spanning no edges) of T_{L,d} in which a set I is chosen with probability proportional to x^|I|. This distribution occurs in problems from statistical physics and communication networks. We study Glauber dynamics, a single-site update Markov chain on the set of independent sets of T_{L,d} whose stationary distribution is pi_x. We show that for x > cd^{-1/4}log^{3/4}d (and d large) the convergence to stationarity is exponentially slow in L^{d-1}. This improves a result of Borgs et al., who had shown slow mixing for x > c^d. Our proof, which extends to r-local chains (chains which alter the state of at most a…
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