eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Leibniz International Proceedings in Informatics
1868-8969
2021-09-15
47:1
47:15
10.4230/LIPIcs.APPROX/RANDOM.2021.47
article
The Critical Mean-Field Chayes-Machta Dynamics
Blanca, Antonio
1
Sinclair, Alistair
2
Zhang, Xusheng
1
Pennsylvania State University, University Park, PA, USA
University of California at Berkeley, CA, USA
The random-cluster model is a unifying framework for studying random graphs, spin systems and electrical networks that plays a fundamental role in designing efficient Markov Chain Monte Carlo (MCMC) sampling algorithms for the classical ferromagnetic Ising and Potts models. In this paper, we study a natural non-local Markov chain known as the Chayes-Machta dynamics for the mean-field case of the random-cluster model, where the underlying graph is the complete graph on n vertices. The random-cluster model is parametrized by an edge probability p and a cluster weight q. Our focus is on the critical regime: p = p_c(q) and q ∈ (1,2), where p_c(q) is the threshold corresponding to the order-disorder phase transition of the model. We show that the mixing time of the Chayes-Machta dynamics is O(log n ⋅ log log n) in this parameter regime, which reveals that the dynamics does not undergo an exponential slowdown at criticality, a surprising fact that had been predicted (but not proved) by statistical physicists. This also provides a nearly optimal bound (up to the log log n factor) for the mixing time of the mean-field Chayes-Machta dynamics in the only regime of parameters where no non-trivial bound was previously known. Our proof consists of a multi-phased coupling argument that combines several key ingredients, including a new local limit theorem, a precise bound on the maximum of symmetric random walks with varying step sizes, and tailored estimates for critical random graphs. In addition, we derive an improved comparison inequality between the mixing time of the Chayes-Machta dynamics and that of the local Glauber dynamics on general graphs; this results in better mixing time bounds for the local dynamics in the mean-field setting.
https://drops.dagstuhl.de/storage/00lipics/lipics-vol207-approx-random2021/LIPIcs.APPROX-RANDOM.2021.47/LIPIcs.APPROX-RANDOM.2021.47.pdf
Markov Chains
Mixing Times
Random-cluster Model
Ising and Potts Models
Mean-field
Chayes-Machta Dynamics
Random Graphs