We develop an efficient algorithmic approach for approximate counting and sampling in the
low-temperature regime of a broad class of statistical physics models on finite subsets of the …

Efficient sampling of complex high-dimensional probability distributions is a central task in
computational science. Machine learning methods like autoregressive neural networks …

We show for the first time that the mixing time of Glauber (single edge update) dynamics for
the random cluster model at q= 2 is bounded by a polynomial in the size of the underlying …

For Δ≥ 5 and q large as a function of Δ, we give a detailed picture of the phase transition of
the random cluster model on random Δ-regular graphs. In particular, we determine the …

We study the performance of Markov chains for the q-state ferromagnetic Potts model on
random regular graphs. While the cases of the grid and the complete graph are by now well …

For d≥ 2 and all q≥ q 0 (d) we give an efficient algorithm to approximately sample from the
q-state ferromagnetic Potts and random cluster models on the torus (ℤ/n ℤ) d for any inverse …

For general ferromagnetic Ising models whose coupling matrix has bounded spectral radius,
we show that the log‐Sobolev constant satisfies a simple bound expressed only in terms of …

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 …

An important paradigm in the understanding of mixing times of Glauber dynamics for spin
systems is the correspondence between spatial mixing properties of the models and bounds …

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 …