Skip to main content
Log in

DNF sparsification and a faster deterministic counting algorithm

  • Published:
computational complexity Aims and scope Submit manuscript

Abstract

Given a DNF formula f on n variables, the two natural size measures are the number of terms or size s(f) and the maximum width of a term w(f). It is folklore that small DNF formulas can be made narrow: if a formula has m terms, it can be \({\epsilon}\)-approximated by a formula with width \({{\rm log}(m/{\epsilon})}\). We prove a converse, showing that narrow formulas can be sparsified. More precisely, any width w DNF irrespective of its size can be \({\epsilon}\)-approximated by a width w DNF with at most \({(w\, {\rm log}(1/{\epsilon}))^{O(w)}}\) terms.

We combine our sparsification result with the work of Luby & Velickovic (1991, Algorithmica 16(4/5):415–433, 1996) to give a faster deterministic algorithm for approximately counting the number of satisfying solutions to a DNF. Given a formula on n variables with poly(n) terms, we give a deterministic \({n^{\tilde{O}({\rm log}\, {\rm log} (n))}}\) time algorithm that computes an additive \({\epsilon}\) approximation to the fraction of satisfying assignments of f for \({\epsilon = 1/{\rm poly}({\rm log}\, n)}\). The previous best result due to Luby and Velickovic from nearly two decades ago had a run time of \({n^{{\rm exp}(O(\sqrt{{\rm log}\, {\rm log} n}))}}\) (Luby & Velickovic 1991, in Algorithmica 16(4/5):415–433, 1996).

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

Access this article

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. Manindra Agrawal, Eric Allender, Russell Impagliazzo, Toniann Pitassi & Steven Rudich (2001). Reducing the complexity of reductions. Computational Complexity 10(2), 117–138.

    Google Scholar 

  2. Miklos Ajtai (1983) \({\sum^{2}_{1}}\)-formula on finite structures. Ann. Pure. Appl. Logic 24: 1–48

    Article  MathSciNet  Google Scholar 

  3. Mikls Ajtai & Avi Wigderson (1985). Deterministic Simulation of Probabilistic Constant Depth Circuits (Preliminary Version). In FOCS, 11–19.

  4. N. Alon & J.H. Spencer (2011). The Probabilistic Method. Wiley Series in Discrete Mathematics and Optimization. John Wiley & Sons. ISBN 9781118210444.

  5. Kazuyuki Amano (2011) Tight Bounds on the Average Sensitivity of k-CNF. Theory of Computing 7(1): 45–48

    Article  MathSciNet  Google Scholar 

  6. Louay M. J. Bazzi (2009). Polylogarithmic Independence Can Fool DNF Formulas. SIAM J. Comput. 38(6), 2220–2272.

  7. I. Benjamini, O. Gurel-Gurevich & R. Peled (2007). On K wise Independent Distributions and Boolean Functions. Available at http://www.wisdom.weizmann.ac.il/~origurel/.

  8. Mark Braverman (2010). Polylogarithmic independence fools AC0 circuits. J. ACM 57(5).

  9. Anindya De, Omid Etesami, Luca Trevisan & Madhur Tulsiani (2010). Improved Pseudorandom Generators for Depth 2 Circuits. In APPROX-RANDOM, 504–517.

  10. P. Erdös & R. Rado (1960). Intersection Theorems for Systems of Sets. Journal of the London Mathematical Society s1-35(1), 85–90.

    Google Scholar 

  11. Ehud Friedgut (1998). Boolean Functions With Low Average Sensitivity Depend On Few Coordinates. Combinatorica 18(1), 27–35.

    Google Scholar 

  12. Merrick L. Furst, James B. Saxe & Michael Sipser (1984). Parity, Circuits, and the Polynomial-Time Hierarchy. Mathematical Systems Theory 17(1), 13–27.

  13. Parikshit Gopalan, Raghu Meka, Omer Reingold, Luca Trevisan & Salil Vadhan (2012). Better Pseudoranom Generators from Milder Pseudorandom Restrictions. In FOCS.

  14. Johan Håstad (1986). Almost Optimal Lower Bounds for Small Depth Circuits. In STOC, 6–20.

  15. S. Jukna (2001). Extremal Combinatorics: With Applications in Computer Science. Texts in Theoretical Computer Science. Springer. ISBN 9783540663133.

  16. Richard M. Karp & Michael Luby (1983). Monte-Carlo Algorithms for Enumeration and Reliability Problems. In FOCS, 56–64.

  17. Richard M. Karp, Michael Luby & Neal Madras (1989). Monte-Carlo Approximation Algorithms for Enumeration Problems. J. Algorithms 10(3), 429–448.

  18. N. Linial, Y. Mansour & N. Nisan (1993). Constant depth circuits, Fourier transform and learnability. Journal of the ACM 40(3), 607–620.

    Google Scholar 

  19. N. Linial & N. Nisan (1990). Approximate inclusion-exclusion. Combinatorica 10, 349–365.

    Google Scholar 

  20. Michael Luby & Boban Velickovic (1991). On Deterministicc Approximation of DNF. In STOC, 430–438.

  21. Michael Luby & Boban Velickovic (1996). On Deterministic Approximation of DNF. Algorithmica 16(4/5), 415–433.

    Google Scholar 

  22. Michael Luby, Boban Velickovic & Avi Wigderson (1993). Deterministic Approximate Counting of Depth-2 Circuits. In ISTCS, 18–24.

  23. Y. Mansour (1994). Learning Boolean functions via the Fourier transform, 391–424. Kluwer Academic Publishers.

  24. Y. Mansour (1995). An \({O(n^{{\rm log}\, {\rm log} n)}}\) learning algorithm for DNF under the uniform distribution. Journal of Computer and System Sciences 50, 543–550.

    Google Scholar 

  25. Joseph Naor & Moni Naor (1993). Small-Bias Probability Spaces: Efficient Constructions and Applications. SIAM J. Comput. 22(4), 838–856.

  26. Noam Nisan (1991). Pseudorandom bits for constant depth circuits. Combinatorica 11(1), 63–70.

    Google Scholar 

  27. Noam Nisan & Avi Wigderson (1994). Hardness vs Randomness. J. Comput. Syst. Sci. 49(2), 149–167.

    Google Scholar 

  28. Ryan O’Donnell (2012a). Analysis of Boolean functions. http://analysisofbooleanfunctions.org.

  29. Ryan O’Donnell (2012b). Open Problems in Analysis of Boolean Functions. CoRR abs/1204.6447.

  30. Alexander A. Razborov (2009). A Simple Proof of Bazzi’s Theorem. TOCT 1(1).

  31. Benjamin Rossman (2010). The Monotone Complexity of k-clique on Random Graphs. In FOCS, 193–201.

  32. Luca Trevisan (2004). A Note on Approximate Counting for k-DNF. In APPROX-RANDOM, 417–426.

  33. L.G. Valiant (1979). The complexity of computing the permanent. Theoretical Computer Science 8(2), 189 – 201.

  34. Jan Vondrak (2012). Personal communication.

  35. Andrew C. Yao (1985). Separating the Polynomial-Time Hierarchy by Oracles (Preliminary Version). In FOCS, 1–10.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Parikshit Gopalan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gopalan, P., Meka, R. & Reingold, O. DNF sparsification and a faster deterministic counting algorithm. comput. complex. 22, 275–310 (2013). https://doi.org/10.1007/s00037-013-0068-6

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00037-013-0068-6

Keywords

Subject Classification

Navigation