The Critical Mean-field Chayes-Machta Dynamics

Antonio Blanca, Alistair Sinclair, Xusheng Zhang

The random-cluster model is a unifying framework for studying random graphs, spin systems and random networks. The model is closely related to the classical ferromagnetic Ising and Potts models and is often viewed as a generalization of these 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 $q \in (1,2)$, 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 $O(\log n \cdot \log \log n)$ 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 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 previous bound was 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.

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