Approximate Colorwise Tensorization of Entropy and Optimal Mixing of the Wang-Swendsen-Kotecký Dynamics

Abstract

We study the mixing time of Wang-Swendsen-Kotecký (WSK) dynamics for uniformly sampling proper $q$-colorings. The WSK dynamics is widely used in statistical physics for sampling from the antiferromagnetic Potts model and can be considered a global counterpart of the flip dynamics, which currently yields the state-of-the-art bounds for sampling colorings in general graphs (Carlson and Vigoda, SODA 2025). However, despite its importance, the tools for analyzing such dynamics remain limited. We develop new tools that enable us to analyze the mixing time of the WSK dynamics through the lens of relative entropy contraction. We introduce new criteria for multi-spin distributions: approximate colorwise tensorization of entropy (ACTE) and approximate colorwise subadditivity of entropy (ACSE). These criteria provide a colorwise counterpart to standard vertex-wise entropy factorization principles, and expose a form of color symmetry beyond coordinate-wise analyses. We also develop new inductive approaches for establishing such criteria on specific types of graphs, which can be viewed as local-to-global arguments for proving high-dimensional functional inequalities in a graph-theoretic sense. As concrete applications, we establish an optimal $O_q(\log n)$ mixing time for the WSK dynamics on chordal and outerplanar graphs, down to the optimal number of colors. Because trees and line graphs of trees are chordal, the result covers both vertex and edge colorings of trees. Our results work in a regime that bypasses the irreducibility threshold for Glauber dynamics while also improving the best known mixing time bounds (Carlson, Chen, Feng and Vigoda, SODA 2025).

Chunyang Wang

Postdoc

I am Chunyang Wang (王淳扬), currently a project researcher (postdoc) at the National Institute of Informatics (NII), hosted by Prof. Yuichi Yoshida. My research interests broadly lie in theoretical computer science, especially algorithms for counting and sampling and algorithmic stability.

Yuichi Yoshida

Professor

I am a professor at the National Institute of Informatics. My research interests include sublinear-time algorithms, approximation algorithms, constraint satisfaction problems, spectral graph theory, and algorithmic stability.

Zihan Zhang

Ph.D. Student

I am a Ph.D. student at the National Institute of Informatics, advised by Prof. Yuichi Yoshida. My research interests include approximate counting, sensitivity of algorithms, and Markov chains.