The condensation transition in random hypergraph 2-coloring

For many random constraint satisfaction problems such as random satisfiability or random graph or hypergraph coloring, the best current estimates of the threshold for the existence of solutions are based on the first and the second moment method. However, in most cases these techniques do not yield matching upper and lower bounds. Sophisticated but non-rigorous arguments from statistical mechanics have ascribed this discrepancy to the existence of a phase transition called condensation that occurs shortly before the actual threshold for the existence of solutions and that affects the combinatorial nature of the problem (Krzakala, Montanari, Ricci-Tersenghi, Semerjian, Zdeborova: PNAS 2007). In this paper we prove for the first time that a condensation transition exists in a natural random CSP, namely in random hypergraph 2-coloring. Perhaps surprisingly, we find that the second moment method applied to the number of 2-colorings breaks down strictly before the condensation transition. Our proof also yields slightly improved bounds on the threshold for random hypergraph 2-colorability.

[1]  Lenka Zdeborová,et al.  Random Subcubes as a Toy Model for Constraint Satisfaction Problems , 2007, ArXiv.

[2]  Jeanette P. Schmidt,et al.  Component structure in the evolution of random hypergraphs , 1985, Comb..

[3]  Alan M. Frieze,et al.  Random k-SAT: The Limiting Probability for Satisfiability for Moderately Growing k , 2008, Electron. J. Comb..

[4]  Lenka Zdeborová,et al.  Constraint satisfaction problems with isolated solutions are hard , 2008, ArXiv.

[5]  Thierry Mora,et al.  Pairs of SAT-assignments in random Boolean formulæ , 2005, Theor. Comput. Sci..

[6]  W. Kauzmann The Nature of the Glassy State and the Behavior of Liquids at Low Temperatures. , 1948 .

[7]  Federico Ricci-Tersenghi,et al.  On the solution-space geometry of random constraint satisfaction problems , 2006, STOC '06.

[8]  Cristopher Moore,et al.  Random k-SAT: Two Moments Suffice to Cross a Sharp Threshold , 2003, SIAM J. Comput..

[9]  Andrea Montanari,et al.  Gibbs states and the set of solutions of random constraint satisfaction problems , 2006, Proceedings of the National Academy of Sciences.

[10]  Thierry Mora,et al.  Pairs of SAT Assignment in Random Boolean Formulae , 2005, ArXiv.

[11]  Andrea Montanari,et al.  Reconstruction and Clustering in Random Constraint Satisfaction Problems , 2011, SIAM J. Discret. Math..

[12]  Yuval Peres,et al.  The threshold for random k-SAT is 2k (ln 2 - O(k)) , 2003, STOC '03.

[13]  Amin Coja-Oghlan,et al.  Algorithmic Barriers from Phase Transitions , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.

[14]  Riccardo Zecchina,et al.  Entropy landscape and non-Gibbs solutions in constraint satisfaction problems , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Assaf Naor,et al.  The two possible values of the chromatic number of a random graph , 2004, STOC '04.

[16]  Olivier Dubois,et al.  The 3-XORSAT threshold , 2002, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings..

[17]  David Gamarnik,et al.  Combinatorial approach to the interpolation method and scaling limits in sparse random graphs , 2010, STOC '10.

[18]  D. Achlioptas The Threshold for Random kSAT is 2 k ( ln 2 + o ( 1 ) ) , 2007 .

[19]  E. Friedgut,et al.  Sharp thresholds of graph properties, and the -sat problem , 1999 .

[20]  Ehud Friedgut,et al.  Hunting for sharp thresholds , 2005, Random Struct. Algorithms.

[21]  Alan M. Frieze,et al.  Random k-Sat: A Tight Threshold For Moderately Growing k , 2005, Comb..

[22]  N. Wormald The differential equation method for random graph processes and greedy algorithms , 1999 .

[23]  A. Naor,et al.  The two possible values of the chromatic number of a random graph , 2005 .

[24]  Dimitris Achlioptas,et al.  THE THRESHOLD FOR RANDOM k-SAT IS 2k log 2 O(k) , 2004, FOCS 2004.

[25]  Michael Krivelevich,et al.  Two‐coloring random hypergraphs , 2002, Random Struct. Algorithms.