Spectral Independence in High-Dimensional Expanders and Applications to the Hardcore Model

TL;DR

This paper establishes a connection between spectral independence and high-dimensional expansion, leading to rapid mixing of Glauber dynamics for sampling independent sets in the hardcore model, improving previous algorithms.

Contribution

It proves that spectral independence implies high-dimensional expansion, enabling polynomial-time sampling algorithms for the hardcore model near the uniqueness threshold.

Findings

Spectral independence implies local spectral expansion in high-dimensional complexes.

Glauber dynamics mixes rapidly for the hardcore model up to the uniqueness threshold.

Improves the running time of sampling algorithms from quasi-polynomial to polynomial.

Abstract

We say a probability distribution $\mu$ is spectrally independent if an associated correlation matrix has a bounded largest eigenvalue for the distribution and all of its conditional distributions. We prove that if $\mu$ is spectrally independent, then the corresponding high dimensional simplicial complex is a local spectral expander. Using a line of recent works on mixing time of high dimensional walks on simplicial complexes \cite{KM17,DK17,KO18,AL19}, this implies that the corresponding Glauber dynamics mixes rapidly and generates (approximate) samples from $\mu$. As an application, we show that natural Glauber dynamics mixes rapidly (in polynomial time) to generate a random independent set from the hardcore model up to the uniqueness threshold. This improves the quasi-polynomial running time of Weitz's deterministic correlation decay algorithm \cite{Wei06} for estimating the hardcore partition function, also answering a long-standing open problem of mixing time of Glauber dynamics \cite{LV97,LV99,DG00,Vig01,EHSVY16}.