Rapid Mixing of Glauber Dynamics up to Uniqueness via Contraction
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Abstract (arXiv)
For general antiferromagnetic 2-spin systems, including the hardcore model and the antiferromagnetic Ising model, there is an mathsfFPTAS for the partition function on graphs of maximum degree Delta when the infinite regular tree lies in the uniqueness region by Li et al. (2013). Moreover, in the tree non-uniqueness region, Sly (2010) showed that there is no mathsfFPRAS to estimate the partition function unless mathsfNP=mathsfRP. The algorithmic results follow from the correlation decay approach due to Weitz (2006) or the polynomial interpolation approach developed by Barvinok (2016). However the running time is only polynomial for constant Delta. For the hardcore model, recent work of Anari et al. (2020) establishes rapid mixing of the simple single-site Markov chain known as the Glauber dynamics in the tree uniqueness region. Our work simplifies their analysis of the Glauber dynamics by considering the total pairwise influence of a fixed vertex v on other vertices, as opposed to the total influence on v, thereby extending their work to all 2-spin models and improving the mixing time. More importantly our proof ties together the three disparate algorithmic approaches: we show that contraction of the tree recursions with a suitable potential function, which is the primary technique for establishing efficiency of Weitz's correlation decay approach and Barvinok's polynomial interpolation approach, also establishes rapid mixing of the Glauber dynamics. We emphasize that this connection holds for all 2-spin models (both antiferromagnetic and ferromagnetic), and existing proofs for correlation decay or polynomial interpolation immediately imply rapid mixing of Glauber dynamics. Our proof utilizes that the graph partition function divides that of Weitz's self-avoiding walk trees, leading to new tools for analyzing influence of vertices.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2020.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
4 papers.
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- 2026Rapid mixing in positively weighted restricted Boltzmann machinescited by 0
- 2026Rapid mixing in positively weighted restricted Boltzmann machines1 Apr 2026
- 2023Learning Hard-Constrained Models with One Sample6 Nov 2023
- 2022Complexity of High-Dimensional Identity Testing with Coordinate Conditional Sampling19 Jul 2022
- 2022Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods17 Feb 2022