Planting and MCMC Sampling from the Potts model
arXiv:2410.14409
Abstract
We consider the problem of sampling from the ferromagnetic -state Potts model on the random -regular graph with parameter . A key difficulty that arises in sampling from the model is the existence of a metastability window $(β_u,β_u')$ where the distribution has two competing modes, the so-called disordered and ordered phases, causing MCMC-based algorithms to be slow mixing from worst-case initialisations. To this end, Helmuth, Jenssen and Perkins designed a sampling algorithm that works for all when is large, using cluster expansion methods; more recently, their analysis technique has been adapted to show that random-cluster dynamics mixes fast when initialised more judiciously. However, a bottleneck behind cluster-expansion arguments is that they inherently only work for large , whereas it is widely conjectured that sampling is possible for all . The only result so far that applies to general is by Blanca and Gheissari who showed that the random-cluster dynamics mixes fast for . For , certain correlation phenomena emerge because of the metastability which have been hard to handle, especially for small and . Our main contribution is to perform a delicate analysis of the Potts distribution and the random-cluster dynamics that goes beyond the threshold . We use planting as the main tool in our proofs, and combine it with the analysis of random-cluster dynamics. We are thus able to show that the random-cluster dynamics initialised from all-out mixes fast for all integers beyond the uniqueness threshold ; our analysis works all the way up to the threshold $β_c\in (β_u,β_u')$ where the dominant mode switches from disordered to ordered. We also obtain an algorithm in the ordered regime that refines significantly the range of .
Abstract shortened to meet arXiv requirements