A positive temperature phase transition in random hypergraph 2-coloring

Abstract

Diluted mean-field models are graphical models in which the geometry of interactions is determined by a sparse random graph or hypergraph. Based on a nonrigorous but analytic approach called the "cavity method", physicists have predicted that in many diluted mean-field models a phase transition occurs as the inverse temperature grows from $0$ to $\infty$ [Proc. National Academy of Sciences 104 (2007) 10318-10323]. In this paper, we establish the existence and asymptotic location of this so-called condensation phase transition in the random hypergraph $2$-coloring problem.

Published at http://dx.doi.org/10.1214/15-AAP1119 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (5)

• Gibbs States and the Set of Solutions of Random Constraint Satisfaction Problems
• Probabilistic Reconstruction in Compressed Sensing: Algorithms, Phase Diagrams, and Threshold Achieving Matrices
• Entropy landscape and non-Gibbs solutions in constraint satisfaction problems
• Potts Glass on Random Graphs
• The condensation phase transition in random graph coloring

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