1999/10/06 by Rémi Monasson, R. Monasson, Riccardo Zecchina +11
Computer Science · Engineering · Physics and Astronomy · #Constraint Satisfaction and Optimization #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Optimization and Packing Problems #Optimization and Search Problems #cond-mat.dis-nn
paper · pdf · doi:10.48550/arxiv.cond-mat/9910080
25 pages, to appear in Random Structures and Algorithms
arxiv created 1999/10/06 · openalex publication_date 1999/10/06 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Heuristic methods for solution of problems in the NP-Complete class of decision problems often reach exact solutions, but fail badly at "phase boundaries", across which the decision to be reached changes from almost always having one value to almost having a different value. We report an analytic solution and experimental investigations of the phase transition that occurs in the limit of very large problems in K-SAT. The nature of its "random first-order" phase transition, seen at values of K large enough to make the computational cost of solving typical instances increase exponenitally with problem size, suggest a mechanism for the cost increase. There has been evidence for features like the "backbone" of frozen inputs which characterizes the UNSAT phase in K-SAT in the study of models of disordered materials, but this feature and this transition are uniquely accessible to analysis in K-SAT. The random first order transition combines properties of the 1st order (discontinuous onset of order) and 2nd order (with power law scaling, e.g. of the width of the the critical region in a finite system) transitions known in the physics of pure solids. Such transitions should occur in other combinatoric problems in the large N limit. Finally, improved search heuristics may be developed when a "backbone" is known to exist.