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How Many Needles are in a Haystack, or How to Solve #P-Complete Counting Problems Fast

  • Reuven Y. Rubinstein
Article

Abstract

We present two randomized entropy-based algorithms for approximating quite general #P-complete counting problems, like the number of Hamiltonian cycles in a graph, the permanent, the number of self-avoiding walks and the satisfiability problem. In our algorithms we first cast the underlying counting problem into an associate rare-event probability estimation, and then apply dynamic importance sampling (IS) to estimate efficiently the desired counting quantity. We construct the IS distribution by using two different approaches: one based on the cross-entropy (CE) method and the other one on the stochastic version of the well known minimum entropy (MinxEnt) method. We also establish convergence of our algorithms and confidence intervals for some special settings and present supportive numerical results, which strongly suggest that both ones (CE and MinxEnt) have polynomial running time in the size of the problem.

Keywords

Cross-entropy Rare-event probability estimation Hamilton cycles Self-avoiding walks #P-complete problems Stochastic and simulation-based optimization 

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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Faculty of Industrial Engineering and Management, TechnionHaifaIsrael

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