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Not all FPRASs are equal: demystifying FPRASs for DNF-counting

  • Kuldeep S. Meel
  • Aditya A. ShrotriEmail author
  • Moshe Y. Vardi
Article

Abstract

The problem of counting the number of solutions of a DNF formula, also called #DNF, is a fundamental problem in artificial intelligence with applications in diverse domains ranging from network reliability to probabilistic databases. Owing to the intractability of the exact variant, efforts have focused on the design of approximate techniques for #DNF. Consequently, several Fully Polynomial Randomized Approximation Schemes (FPRASs) based on Monte Carlo techniques have been proposed. Recently, it was discovered that hashing-based techniques too lend themselves to FPRASs for #DNF. Despite significant improvements, the complexity of the hashing-based FPRAS is still worse than that of the best Monte Carlo FPRAS by polylog factors. Two questions were left unanswered in previous works: Can the complexity of the hashing-based techniques be improved? How do the various approaches stack up against each other empirically? In this paper, we first propose a new search procedure for the hashing-based FPRAS that removes the polylog factors from its time complexity. We then present the first empirical study of runtime behavior of different FPRASs for #DNF. The result of our study produces a nuanced picture. First of all, we observe that there is no single best algorithm that outperforms all others for all classes of formulas and input parameters. Second, we observe that the algorithm with one of the worst time complexities solves the largest number of benchmarks.

Keywords

Model counting Hashing Disjunctive normal form Boolean formulas Fully polynomial randomized approximation scheme ApproxMC 

Notes

Acknowledgements

The authors would like to thank anonymous reviewers for their insightful comments and suggestions. Moshe Y. Vardi and Aditya A. Shrotri’s work was supported in parts by NSF grant IIS-1527668, NSF Expeditions in Computing project “ExCAPE: Expeditions in Computer Augmented Program Engineering”. Kuldeep S. Meel’s work was supported in parts by NUS ODPRT Grant R-252-000-685-133, AI Singapore Grant R-252-000-A16-490, and Sung Kah Kay Assistant Professorship Fund.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Kuldeep S. Meel
    • 1
  • Aditya A. Shrotri
    • 2
    Email author
  • Moshe Y. Vardi
    • 2
  1. 1.National University of SingaporeSingaporeSingapore
  2. 2.Rice UniversityHoustonUSA

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