Improved bounds for randomly sampling colorings via linear programming

Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)
147 Downloads (Pure)


A well-known conjecture in computer science and statistical physics is that Glauber dynamics on the set of k-colorings of a graph G on n vertices with maximum degree ∆ is rapidly mixing for k ≥ ∆ + 2. In FOCS 1999, Vigoda [43] showed that the flip dynamics (and therefore also Glauber dynamics) is rapidly mixing for any k > 11 6 ∆. It turns out that there is a natural barrier at 11 6 , below which there is no one-step coupling that is contractive with respect to the Hamming metric, even for the flip dynamics. We use linear programming and duality arguments to fully characterize the obstructions to going beyond 11 6 . These extremal configurations turn out to be quite brittle, and in this paper we use this to give two proofs that the Glauber dynamics is rapidly mixing for any k ≥ ( 11 60)∆ for some absolute constant 0 > 0. This is the first improvement to Vigoda’s result that holds for general graphs. Our first approach analyzes a variable-length coupling in which these configurations break apart with high probability before the coupling terminates, and our other approach analyzes a one-step path coupling with a new metric that counts the extremal configurations. Additionally, our results extend to list coloring, a widely studied generalization of coloring, where the previously best known results required k > 2∆.

Original languageEnglish
Title of host publicationProceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms
EditorsTimothy M. Chan
PublisherSociety for Industrial and Applied Mathematics (SIAM)
Number of pages19
ISBN (Electronic)9781611975482
Publication statusPublished - 6 Jan 2019

ASJC Scopus subject areas

  • Software
  • General Mathematics


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