Sampling from the Potts model at low temperatures via Swendsen-Wang dynamics

Antonio Blanca, Reza Gheissari

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Sampling from the q-state ferromagnetic Potts model is a fundamental question in statistical physics, probability theory, and theoretical computer science. On general graphs, this problem is computationally hard, and this hardness holds at arbitrarily low temperatures. At the same time, in recent years, there has been significant progress showing the existence of low-temperature sampling algorithms in various specific families of graphs. Our aim in this paper is to understand the minimal structural properties of general graphs that enable polynomial-time sampling from the q-state ferromagnetic Potts model at low temperatures. We study this problem from the perspective of the widely-used Swendsen-Wang dynamics and the closely related random-cluster dynamics. These are non-local Markov chains that have long been believed to converge rapidly to equilibrium at low temperatures in many graphs. However, the hardness of the sampling problem likely indicates that this is not even the case for all bounded degree graphs. Our results demonstrate that a key graph property behind fast or slow convergence time for these dynamics is whether the independent edge-percolation on the graph admits a strongly supercritical phase. By this, we mean that at large p< 1, it has a large linear-sized component, and the graph complement of that component is comprised of only small components Specifically, we prove that such a condition implies fast mixing of the Swendsen-Wang and random-cluster dynamics on two general families of bounded-degree graphs: (a) graphs of at most stretched-exponential volume growth and (b) locally treelike graphs. In the other direction, we show that, even among graphs in those families, these Markov chains can converge exponentially slowly at arbitrarily low temperatures if the edge-percolation condition does not hold. In the process, we develop new tools for the analysis of non-local Markov chains, including a framework to bound the speed of disagreement propagation in the presence of long-range correlations, an understanding of spatial mixing properties on trees with random boundary conditions, and an analysis of burn-in phases at low temperatures.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE 64th Annual Symposium on Foundations of Computer Science, FOCS 2023
PublisherIEEE Computer Society
Number of pages15
ISBN (Electronic)9798350318944
StatePublished - 2023
Event64th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2023 - Santa Cruz, United States
Duration: Nov 6 2023Nov 9 2023

Publication series

NameProceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS
ISSN (Print)0272-5428


Conference64th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2023
Country/TerritoryUnited States
CitySanta Cruz

All Science Journal Classification (ASJC) codes

  • General Computer Science

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