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 …

We give algorithms for approximating the partition function of the ferromagnetic Potts model
on d-regular expanding graphs. We require much weaker expansion than in previous works; …

We study the following combinatorial counting and sampling problems: can we sample from
the Erdős-Rényi random graph G(n,p) conditioned on triangle-freeness? Can we …

A common obstruction to efficient sampling from high-dimensional distributions with Markov
chains is the multimodality of the target distribution because they may get trapped far from …

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 …

We present a detailed probabilistic and structural analysis of the set of weighted
homomorphisms from the discrete torus Z mn, where m is even, to any fixed graph: we show …

We give an efficient perfect sampling algorithm for weighted, connected induced subgraphs
(or graphlets) of rooted, bounded degree graphs. Our algorithm utilizes a vertex-percolation …

We develop an approximation algorithm for the partition function of the ferromagnetic Potts
model on graphs with a small-set expansion condition, and as a step in the argument we …

In this article, we consider the algorithmic problem of sampling from the Potts model and
computing its partition function at low temperatures. Instead of directly working with spin …