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Relationship between clustering and algorithmic phase transitions in the random k-XORSAT model and its NP-complete extensions

F Altarelli1,2, R Monasson2 and F Zamponi2,3

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We study the performances of stochastic heuristic search algorithms on Uniquely Extendible Constraint Satisfaction Problems with random inputs. We show that, for any heuristic preserving the Poissonian nature of the underlying instance, the (heuristic-dependent) largest ratio αa of constraints per variables for which a search algorithm is likely to find solutions is smaller than the critical ratio αd above which solutions are clustered and highly correlated. In addition we show that the clustering ratio can be reached when the number k of variables per constraints goes to infinity by the so-called Generalized Unit Clause heuristic.


PACS

05.40.-a Fluctuation phenomena, random processes, noise, and Brownian motion

05.20.-y Classical statistical mechanics

05.70.Fh Phase transitions: general studies

02.50.Ey Stochastic processes

Subjects

Computational physics

Statistical physics and nonlinear systems

Dates

Issue 1 (2008)



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