Title
Swendsen-Wang Algorithm on the Mean-Field Potts Model.
Abstract
We study the q-state ferromagnetic Potts model on the n-vertex complete graph known as the mean-field (Curie-Weiss) model. We analyze the Swendsen-Wang algorithm which is a Markov chain that utilizes the random cluster representation for the ferromagnetic Potts model to recolor large sets of vertices in one step and potentially overcomes obstacles that inhibit single-site Glauber dynamics. The case q=2 (the Swendsen-Wang algorithm for the ferromagnetic Ising model) undergoes a slow-down at the uniqueness/non-uniqueness critical temperature for the infinite Delta-regular tree (Long et al., 2014) but yet still has polynomial mixing time at all (inverse) temperatures betau003e0 (Cooper et al., 2000). In contrast for qu003e=3 there are two critical temperatures 0 =beta_rc. These results complement refined results of Cuff et al. (2012) on the mixing time of the Glauber dynamics for the ferromagnetic Potts model. The most interesting aspect of our analysis is at the critical temperature beta=beta_u, which requires a delicate choice of a potential function to balance the conflating factors for the slow drift away from a fixed point (which is repulsive but not Jacobian repulsive): close to the fixed point the variance from the percolation step dominates and sufficiently far from the fixed point the dynamics of the size of the dominant color class takes over.
Year
Venue
Field
2015
APPROX-RANDOM
Discrete mathematics,Chiral Potts curve,Complete graph,Glauber,Combinatorics,Swendsen–Wang algorithm,Vertex (geometry),Mean field theory,Ising model,Mathematics,Potts model
DocType
Volume
Citations 
Journal
abs/1502.06593
2
PageRank 
References 
Authors
0.42
7
3
Name
Order
Citations
PageRank
andreas galanis16815.13
Daniel Stefankovic224328.65
Eric Vigoda374776.55