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Thresholds of Random k-Sat

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Encyclopedia of Algorithms

Years and Authors of Summarized Original Work

  • 2002; Kaporis, Kirousis, Lalas

Problem Definition

Consider n Boolean variables \(V = \{x_{1},\ldots,x_{n}\}\) and the corresponding set of 2n literals \(L =\{ x_{1}\overline{x}_{1}\ldots,x_{n},\overline{x}_{n}\}\). A k-clause is a disjunction of k literals of distinct underlying variables. A random formula ϕn, m in k conjunctive normal form (k-CNF) is the conjunction of m clauses, each selected in a uniformly random and independent way among the \(2^{k}\left (\begin{array}{c}n \\ \\ k\end{array}\right )\) possible k-clauses on n variables in V. The density r k of a k-CNF formula ϕn, m is the clauses-to-variables ratio m/n.

It was conjectured that for each k ≥ 2 there exists a critical density r k ∗ such that asymptotically almost all (a.a.a.) k-CNF formulas with density r < r k ∗ (r > r k ∗) are satisfiable (unsatisfiable, respectively). So far, the conjecture has been proved only for k = 2 [3, 11]. For k≥ 3, the conjecture still remains...

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Recommended Reading

  1. Achlioptas D (2001) Lower bounds for random 3-SAT via differential equations. Theor Comput Sci 265(1–2):159–185

    Article  MathSciNet  MATH  Google Scholar 

  2. Achlioptas D, Sorkin GB (2000) Optimal myopic algorithms for random 3-SAT. In: 41st annual symposium on foundations of computer science, Redondo Beach. IEEE Computer Society, Washington, DC, pp 590–600

    Chapter  Google Scholar 

  3. Chvátal V, Reed B (1992) Mick gets some (the odds are on his side). In: 33rd annual symposium on foundations of computer science, Pittsburgh. IEEE Computer Society, pp 620–627

    Chapter  Google Scholar 

  4. Davis M, Putnam H (1960) A computing procedure for quantification theory. J Assoc Comput Mach 7(4):201–215

    Article  MathSciNet  MATH  Google Scholar 

  5. Davis M, Logemann G, Loveland D (1962) A machine program for theoremproving. Commun ACM 5:394–397

    Article  MathSciNet  MATH  Google Scholar 

  6. Dubois O (2001) Upper bounds on the satisfiability threshold. Theor Comput Sci 265:187–197

    Article  MathSciNet  MATH  Google Scholar 

  7. Dubois O, Boufkhad Y, Mandler J (2000) Typical random 3-SAT formulae and the satisfiability threshold. In: 11th ACM-SIAM symposium on discrete algorithms, San Francisco. Society for Industrial and Applied Mathematics, pp 126–127

    Google Scholar 

  8. Franco J (1984) Probabilistic analysis of the pure literal heuristic for the satisfiability problem. Ann Oper Res 1:273–289

    Article  MATH  Google Scholar 

  9. Franco J (2001) Results related to threshold phenomena research in satisfiability: lower bounds. Theor Comput Sci 265:147–157

    Article  MathSciNet  MATH  Google Scholar 

  10. Friedgut E (1997) Sharp thresholds of graph properties, and the k-SAT problem. J AMS 12:1017–1054

    MathSciNet  MATH  Google Scholar 

  11. Goerdt A (1996) A threshold for unsatisfiability. J Comput Syst Sci 33:469–486

    Article  MathSciNet  MATH  Google Scholar 

  12. Kaporis AC, Kirousis LM, Lalas EG (2006) The probabilistic analysis of a greedy satisfiability algorithm. Random Struct Algorithms 28(4):444–480

    Article  MathSciNet  MATH  Google Scholar 

  13. Kirousis L, Stamatiou Y, Zito M (2006) The unsatisfiability threshold conjecture: the techniques behind upper bound improvements. In: Percus A, Istrate G, Moore C (eds) Computational complexity and statistical physics. Santa Fe Institute studies in the sciences of complexity. Oxford University Press, New York, pp 159–178

    Google Scholar 

  14. Mitchell D, Selman B, Levesque H (1992) Hard and easy distribution of SAT problems. In: 10th national conference on artificial intelligence, San Jose. AAAI Press, Menlo Park, pp 459–465

    Google Scholar 

  15. Monasson R, Zecchina R (1997) Statistical mechanics of the random k-SAT problem. Phys Rev E 56:1357–1361

    Article  MathSciNet  Google Scholar 

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Correspondence to Alexis Kaporis .

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Kaporis, A., Kirousis, L. (2016). Thresholds of Random k-Sat. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2864-4_423

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