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Survey and Belief Propagation on Random K-SAT

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2919))

Abstract

Survey Propagation (SP) is a message passing algorithm that can be viewed as a generalization of the so called Belief Propagation (BP) algorithm used in statistical inference and error correcting codes. In this work we discuss the connections between BP and SP.

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References

  1. Mézard, M., Zecchina, R.: Random K-satisfiability: from an analytic solution to a new efficient algorithm. Phys. Rev. E 66.056126 (2002)

    Google Scholar 

  2. Braunstein, A., Mezard, M., Zecchina, R.: Survey Propagation: an Algorithm for Satisfiability (preprint), http://lanl.arXiv.org/cs.CC/0212002

  3. Code and benchmarks available, http://www.ictp.trieste.it/~zecchina/SP/

  4. Mézard, M., Parisi, G., Zecchina, R.: Analytic and Algorithmic solutions to Random Satisfiability Problems. Science 297, 812 (2002); (Sciencexpress published on-line 27-June 2002; 10.1126/science.1073287)

    Article  Google Scholar 

  5. Mertens, S., Mézard, M., Zecchina, R.: High precision values for SAT/UNSAT thresholds in random K-SAT (2003) (in preparation)

    Google Scholar 

  6. Friedgut, E.: Necessary and Sufficient conditions for sharp thresholds of graph properties, and the k-SAT problem. J. Amer. Math. Soc. 12, 1017–1054 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  7. Dubois, O., Monasson, R., Selman, B., Zecchina, R. (eds.): Phase Transitions in Combinatorial Problems. Theoret. Comp. Sci. 265 (2001)

    Google Scholar 

  8. Mezard, M., Parisi, G., Virasoro, M.A.: SK model: replica solution without replicas. Europhys. Lett. 1, 77 (1986); Mezard, M., Parisi, G.: The cavity method revisited. Eur. Phys. J. B 20, 217 (2001)

    Article  Google Scholar 

  9. Talagrand, M.: Rigorous low temperature results for the p-spin mean field spin glass model. Prob. Theory and Related Fields 117, 303–360 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  10. The cavity and the replica methods deal with average quantities over some probability distribution of problem instances (e.g. average fraction of violated clauses in random K-SAT problems). See ref. Complex Systems: a Physicist’s View, G. Parisi (2002), cond-mat/0205297 for a recent review. In ref. [1] it was realized that quite in general the cavity approach could be brought down to the level of single problem instances thus revealing the algorithmic potentialities of the formalism. As discussed in this paper, a simple version of survey propagation was already known to Gallager since 1963 and used in practice as decoding algorithm in Low Density Parity Check codes and Turbo Codes.

    Google Scholar 

  11. Guerra, F., Toninelli, F.L.: The infinite volume limit in generalized mean field disordered models. Commun. Math. Phys. 230(1), 71 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  12. Franz, S., Leone, M.: Replica bounds for optimization problems and diluted spin systems. J. Stat. Phys. 111, 535 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  13. Gallager, R.G.: Low-Density Parity-Check Codes. MIT Press, Cambridge (1963)

    Google Scholar 

  14. Pearl, J.: Probabilistic Reasoning in Intelligent Systems, 2nd edn. Morgan Kaufmann, San Francisco (1988)

    Google Scholar 

  15. Over the n-dimensional hypercube, we say that two vertices are connected if they represent solutions at Hamming distances one. We define a cluster as a connected component over the whole hypercube. SP looks at clusters with a finite fraction of backbone variables [17,1]. Exact enumerations on small random formulas indeed confirm the onset of clustering below αc already for rather small values of n (A. Braunstein, V. Napolano, R. Zecchina, in preparation, 2003). Rigorous results about clustering phenomenon taking place in random K-XOR-SAT can be found in: S. Cocco, O. Dubois,J. Mandler, R. Monasson, Rigorous decimation-based construction of ground pure states for spin glass models on random lattices, Phys. Rev. Lett. 90, 047205 (2003): Mezard, M., Ricci-Tersenghi, F., Zecchina, R.: Two solutions to diluted p-spin models and XORSAT problems J. Stat. Phys. 111, 505 (2003); We mention that for random 3-SAT, there exists another clustering transition at α _ 3.87 [5] where clustering appears and yet no variable is constrained. Such a transition is absent for any K >3.

    Google Scholar 

  16. In the large n limit, properties of typical random K-SAT instances are: – P (|A( i) | = k) = Poisson ( i, k) so |A( i) | = o(1) for each i with high probability. – The most loops are of length O( ln( n)) Such properties are usually referred to as locally tree-like. Cavity variables will be at typical distance of order log n and hence conjectured to have marginal probability distributions approximatively uncorrelated

    Google Scholar 

  17. Monasson, R., Zecchina, R., Kirkpatrick, S., Selman, B., Troyansky, L.: Computational complexity from “characteristic” phase transitions. Nature (London) 400, 133 (1999)

    Article  MathSciNet  Google Scholar 

  18. Selman, B., Kautz, H., Cohen, B.: Local search strategies for satisfiability testing. In: Proceedings of DIMACS, p. 661 (1993)

    Google Scholar 

  19. Seitz, S., Orponen, P.: An efficient local search method for random 3-satisfiability. In: LICS 2003 Workshop on Typical Case Complexity and Phase Transitions, Ottawa, Canada (June 2003)

    Google Scholar 

  20. Parisi, G.: On the survey-propagation equations for the random K-satisfiability problem. cs.CC/0212009 (preprint)

    Google Scholar 

  21. Yedidia, J.S., Freeman, W.T., Weiss, Y.: Generalized Belief Propagation. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems 13, pp. 689–695. MIT Press, Cambridge (2001)

    Google Scholar 

  22. Kikuchi, R.: Phys. Rev. 81, 988 (1951)

    Article  MATH  MathSciNet  Google Scholar 

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Braunstein, A., Zecchina, R. (2004). Survey and Belief Propagation on Random K-SAT. In: Giunchiglia, E., Tacchella, A. (eds) Theory and Applications of Satisfiability Testing. SAT 2003. Lecture Notes in Computer Science, vol 2919. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24605-3_38

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  • DOI: https://doi.org/10.1007/978-3-540-24605-3_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20851-8

  • Online ISBN: 978-3-540-24605-3

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