{ "id": "0806.1012", "version": "v6", "published": "2008-06-05T16:44:20.000Z", "updated": "2009-01-06T11:30:53.000Z", "title": "Negative Entropy, Zero temperature and stationary Markov Chains on the interval", "authors": [ "Artur O. Lopes", "Joana Mohr", "Rafael R. Souza", "Philippe Thieullen" ], "categories": [ "math.DS", "math.PR" ], "abstract": "We analyze some properties of maximizing stationary Markov probabilities on the Bernoulli space $[0,1]^\\mathbb{N}$, More precisely, we consider ergodic optimization for a continuous potential $A$, where $A: [0,1]^\\mathbb{N}\\to \\mathbb{R}$ which depends only on the two first coordinates. We are interested in finding stationary Markov probabilities $\\mu_\\infty$ on $ [0,1]^\\mathbb{N}$ that maximize the value $ \\int A d \\mu,$ among all stationary Markov probabilities $\\mu$ on $[0,1]^\\mathbb{N}$. This problem correspond in Statistical Mechanics to the zero temperature case for the interaction described by the potential $A$. The main purpose of this paper is to show, under the hypothesis of uniqueness of the maximizing probability, a Large Deviation Principle for a family of absolutely continuous Markov probabilities $\\mu_\\beta$ which weakly converges to $\\mu_\\infty$. The probabilities $\\mu_\\beta$ are obtained via an information we get from a Perron operator and they satisfy a variational principle similar to the pressure. Under the hypothesis of $A$ being $C^2$ and the twist condition, that is, $\\frac{\\partial^2 A}{\\partial_x \\partial_y} (x,y) \\neq 0$, for all $(x,y) \\in [0,1]^2$, we show the graph property.", "revisions": [ { "version": "v6", "updated": "2009-01-06T11:30:53.000Z" } ], "analyses": { "subjects": [ "37A35", "37A50", "37A60", "60J10" ], "keywords": [ "stationary markov chains", "negative entropy", "probability", "variational principle similar", "large deviation principle" ], "note": { "typesetting": "TeX", "pages": 0, "language": "en", "license": "arXiv", "status": "editable", "adsabs": "2008arXiv0806.1012L" } } }