{ "id": "1206.3165", "version": "v1", "published": "2012-06-14T16:20:38.000Z", "updated": "2012-06-14T16:20:38.000Z", "title": "Slow mixing of Glauber Dynamics for the hard-core model on regular bipartite graphs", "authors": [ "David Galvin", "Prasad Tetali" ], "comment": "18 pages. This paper appeared in Random Structures and Algorithms in 2006, and is an expanded version of the abstract Slow mixing of Glauber dynamics for the hard-core model on the hypercube that appeared in the proceedings of SODA 2004", "categories": [ "math.CO", "cs.DM" ], "abstract": "Let $\\gS=(V,E)$ be a finite, $d$-regular bipartite graph. For any $\\lambda>0$ let $\\pi_\\lambda$ be the probability measure on the independent sets of $\\gS$ in which the set $I$ is chosen with probability proportional to $\\lambda^{|I|}$ ($\\pi_\\lambda$ is the {\\em hard-core measure with activity $\\lambda$ on $\\gS$}). We study the Glauber dynamics, or single-site update Markov chain, whose stationary distribution is $\\pi_\\lambda$. We show that when $\\lambda$ is large enough (as a function of $d$ and the expansion of subsets of single-parity of $V$) then the convergence to stationarity is exponentially slow in $|V(\\gS)|$. In particular, if $\\gS$ is the $d$-dimensional hypercube $\\{0,1\\}^d$ we show that for values of $\\lambda$ tending to 0 as $d$ grows, the convergence to stationarity is exponentially slow in the volume of the cube. The proof combines a conductance argument with combinatorial enumeration methods.", "revisions": [ { "version": "v1", "updated": "2012-06-14T16:20:38.000Z" } ], "analyses": { "subjects": [ "68R10", "68W25", "05C69" ], "keywords": [ "regular bipartite graph", "glauber dynamics", "hard-core model", "slow mixing", "single-site update markov chain" ], "note": { "typesetting": "TeX", "pages": 18, "language": "en", "license": "arXiv", "status": "editable", "adsabs": "2012arXiv1206.3165G" } } }