{ "id": "1911.09377", "version": "v1", "published": "2019-11-21T10:06:22.000Z", "updated": "2019-11-21T10:06:22.000Z", "title": "The asymptotics of the clustering transition for random constraint satisfaction problems", "authors": [ "Louise Budzynski", "Guilhem Semerjian" ], "comment": "35 pages, 8 figures; Submission to SciPost", "categories": [ "cond-mat.dis-nn", "cs.DM", "math.PR" ], "abstract": "Random Constraint Satisfaction Problems exhibit several phase transitions when their density of constraints is varied. One of these threshold phenomena, known as the clustering or dynamic transition, corresponds to a transition for an information theoretic problem called tree reconstruction. In this article we study this threshold for two CSPs, namely the bicoloring of $k$-uniform hypergraphs with a density $\\alpha$ of constraints, and the $q$-coloring of random graphs with average degree $c$. We show that in the large $k,q$ limit the clustering transition occurs for $\\alpha = \\frac{2^{k-1}}{k} (\\ln k + \\ln \\ln k + \\gamma_{\\rm d} + o(1))$, $c= q (\\ln q + \\ln \\ln q + \\gamma_{\\rm d}+ o(1))$, where $\\gamma_{\\rm d}$ is the same constant for both models. We characterize $\\gamma_{\\rm d}$ via a functional equation, solve the latter numerically to estimate $\\gamma_{\\rm d} \\approx 0.871$, and obtain an analytic lowerbound $\\gamma_{\\rm d} \\ge 1 + \\ln (2 (\\sqrt{2}-1)) \\approx 0.812$. Our analysis unveils a subtle interplay of the clustering transition with the rigidity (naive reconstruction) threshold that occurs on the same asymptotic scale at $\\gamma_{\\rm r}=1$.", "revisions": [ { "version": "v1", "updated": "2019-11-21T10:06:22.000Z" } ], "analyses": { "keywords": [ "random constraint satisfaction problems", "clustering transition", "asymptotic", "information theoretic problem", "average degree" ], "note": { "typesetting": "TeX", "pages": 35, "language": "en", "license": "arXiv", "status": "editable" } } }