RANDOM CLUSTER DYNAMICS FOR THE ISING MODEL IS RAPIDLY MIXING

• 成果类型: Article
• 署名作者: Guo, Heng; Jerrum, Mark
• 署名单位: University of Edinburgh; University of London; Queen Mary University London
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/17-AAP1335
• 发表日期: 2018
• 页码: 1292-1313
• 关键词: swendsen-wang

摘要

We show that the mixing time of Glauber (single edge update) dynamics for the random cluster model at q = 2 on an arbitrary n-vertex graph is bounded by a polynomial in n. As a consequence, the Swendsen Wang algorithm for the ferromagnetic Ising model at any temperature also has a polynomial mixing time bound.