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Clustering of solutions in hard satisfiability problems

John Ardelius et al J. Stat. Mech. (2007) P10012   doi: 10.1088/1742-5468/2007/10/P10012  Help

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John Ardelius1, Erik Aurell2 and Supriya Krishnamurthy3
1 SICS Swedish Institute of Computer Science AB, PO Box 1263, SE-16429 Kista, Sweden
2 Department of Computational Biology, KTH-Royal Institute of Technology, AlbaNova University Center, SE-106 91 Stockholm, Sweden
3 Department of Information and Communication Technology, KTH-Royal Institute of Technology, Forum 105, SE-164 40 Kista, Sweden
E-mail: john@sics.se, eaurell@kth.se and supriya@sics.se

Abstract. We study numerically the solution space structure of random 3-SAT problems close to the SAT/UNSAT transition. This is done by considering chains of satisfiability problems, where clauses are added sequentially to a problem instance. Using the overlap measure of similarity between different solutions found on the same problem instance, we examine geometrical changes as a function of α. In each chain, the overlap distribution is first smooth, but then develops a tiered structure, indicating that the solutions are found in well separated clusters. On chains of not too large instances, all remaining solutions are eventually observed to be found in only one small cluster before vanishing. This condensation transition point is estimated by finite size scaling to be αc = 4.26 with an apparent critical exponent of about 1.7. The average overlap value is also observed to increase with α up to the transition, indicating a reduction in solutions space size, in accordance with theoretical predictions. The solutions are generated by a local heuristic, ASAT, and compared to those found by the Survey Propagation algorithm up to αc.

Key words: finite-size scaling; energy landscapes (experiment); network dynamics; random graphs, networks

E-print number: cond-mat/0702672
Cited: by
Refers: to

Received 21 June 2007, accepted for publication 4 October 2007
Published 24 October 2007

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