[go: up one dir, main page]

Skip to main content
Log in

Random walks for selected boolean implication and equivalence problems

  • Original Article
  • Published:
Acta Informatica Aims and scope Submit manuscript

Abstract

This paper is concerned with the design and analysis of a random walk algorithm for the 2CNF implication problem (2CNFI). In 2CNFI, we are given two 2CNF formulas \({\phi_{1}}\) and \({\phi_{2}}\) and the goal is to determine whether every assignment that satisfies \({\phi_{1}}\) , also satisfies \({\phi_{2}}\) . The implication problem is clearly coNP-complete for instances of kCNF, k ≥ 3; however, it can be solved in polynomial time, when k ≤ 2. The goal of this paper is to provide a Monte Carlo algorithm for 2CNFI with a bounded probability of error. The technique developed for 2CNFI is then extended to derive a randomized, polynomial time algorithm for the problem of checking whether a given 2CNF formula Nae-implies another 2CNF formula.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Karger D.R., Klein P.N., Tarjan R.E.: A randomized linear-time algorithm to find minimum spanning trees. J. ACM 42(2), 321–328 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  2. Motwani R., Raghavan P.: Randomized Algorithms. Cambridge University Press, Cambridge (1995)

    MATH  Google Scholar 

  3. Papadimitriou, C.H.: On selecting a satisfying truth assignment. In: IEEE, editor, Proceedings: 32nd annual Symposium on Foundations of Computer Science, San Juan, Puerto Rico, October 1–4, 1991, pp. 163–169, 1109 Spring Street, Suite 300, Silver Spring, MD 20910, USA, 1991. IEEE Computer Society Press

  4. Papadimitriou C.H.: Computational Complexity. Addison-Wesley, New York (1994)

    MATH  Google Scholar 

  5. Ross S.M.: Probability Models, 7th edn. Academic Press, Inc., London (2000)

    MATH  Google Scholar 

  6. Schöning, U.: New algorithms for k-SAT based on the local search principle. In: MFCS: Symposium on Mathematical Foundations of Computer Science (2001)

  7. Subramani, K., Gu, X.: Absorbing random walks and the nae2sat problem. Fundamenta Informatica. (2008, Submitted)

  8. Subramani K.: Cascading random walks. Int. J. Found. Comput. Sci. (IJFCS) 16(3), 599–622 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  9. Subramani K. et al.: Absorbing random walks and the nae2sat problem. In: Preparata, F. et al. (eds) Proceedings of the 2nd Annual International Frontiers of Algorithmics Workshop. Lecture Notes in Computer Science, vol. 5059, pp. 89–100. Springer, Heidelberg (2008)

    Google Scholar 

  10. Valiant L.G.: A theory of the learnable. Commun. ACM 27(11), 1134–1142 (1984)

    Article  MATH  Google Scholar 

  11. Wei, W., Selman, B.: Accelerating random walks. In: The Eighth International Conference on Constraint Programming (CP), LNCS, pp. 216–232 (2002)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Subramani.

Additional information

This research has been supported in part by the Air Force Office of Scientific Research under grant FA9550-06-1-0050.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Subramani, K., Lai, HJ. & Gu, X. Random walks for selected boolean implication and equivalence problems. Acta Informatica 46, 155–168 (2009). https://doi.org/10.1007/s00236-009-0089-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00236-009-0089-4

Keywords

Navigation