Efficient sampling and counting algorithms for the Potts model on ℤᵈ at all temperatures

DOI10.1145/3357713.3384271OpenAlexW3034266416MaRDI QIDQ5144959

Tyler Helmuth, Will Perkins, Jennifer T. Chayes, Prasad Tetali, Christian Borgs

Publication date: 19 January 2021

Published in: Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1909.09298

zbMATH Keywords

samplingPotts modelcluster expansionapproximate counting

Mathematics Subject Classification ID

Theory of computing (68Qxx)

Related Items (12)

Fast mixing via polymers for random graphs with unbounded degree ⋮ Efficient sampling and counting algorithms for the Potts model on ℤ^d at all temperatures ⋮ Sampling from the low temperature Potts model through a Markov chain on flows ⋮ Spatial mixing and the random‐cluster dynamics on lattices ⋮ Large scale stochastic dynamics. Abstracts from the workshop held September 11--17, 2022 ⋮ Homomorphisms from the torus ⋮ Low-temperature Ising dynamics with random initializations ⋮ Finite-size scaling, phase coexistence, and algorithms for the random cluster model on random graphs ⋮ Approximation Algorithms for the Random Field Ising Model ⋮ Sampling from Potts on random graphs of unbounded degree via random-cluster dynamics ⋮ Efficient algorithms for approximating quantum partition functions ⋮ Polymer dynamics via cliques: new conditions for approximations

This page was built for publication: Efficient sampling and counting algorithms for the Potts model on ℤᵈ at all temperatures