Rapid mixing from spectral independence beyond the Boolean domain
Weiming Feng,
Heng Guo,
Yitong Yin,
Chihao Zhang
April, 2022
Abstract
We extend the notion of spectral independence (introduced by Anari, Liu, and Oveis Gharan [ALO20]) from the Boolean domain to general discrete domains. This property characterises distributions with limited correlations, and implies that the corresponding Glauber dynamics is rapidly mixing. As a concrete application, we show that Glauber dynamics for sampling proper $q$-colourings mixes in polynomial-time for the family of triangle-free graphs with maximum degree $\Delta$ provided $q\geq ({\alpha}^\star+\delta)$ $\Delta$ where $\alpha^\star\approx 1.763$ is the unique solution to $\alpha^\star=\exp(1/\alpha^\star)$ and $\delta>0$ is any constant. This is the first efficient algorithm for sampling proper $q$-colourings in this regime with possibly unbounded $\Delta$. Our main tool of establishing spectral independence is the recursive coupling by Goldberg, Martin, and Paterson [GMP05].
Publication
ACM Transactions on Algorithms
Junior Fellow
I am a Junior Fellow at the Institute for Theoretical Studies, ETH Zürich. My research interest lies in theoretical computer science. Currently, I focus on sampling and counting algorithms.
Heng Guo
Reader
I am a reader in algorithms and complexity in the School of informatics, University of Edinburgh. My research focuses on algorithms from a complexity perspective.
Yitong Yin
Professor
I am a professor in the Theory Group in the Department of Computer Science and Technology at Nanjing University. I am interested in Theoretical Computer Science.
Chihao Zhang
Associate Professor
I am an Associate Professor in John Hopcroft Center for Computer Science at Shanghai Jiao Tong University since 2018. I work in the area of theoretical computer science.