computational complexity

, Volume 22, Issue 2, pp 275–310

DNF sparsification and a faster deterministic counting algorithm



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 wDNF irrespective of its size can be \({\epsilon}\)-approximated by a width wDNF 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).


Complexity pseudorandomness derandomization DNF formulae 

Subject Classification

68Q25 68Q87 


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Copyright information

© Springer Basel 2013

Authors and Affiliations

  1. 1.Microsoft Research Silicon ValleyMountain ViewUSA
  2. 2.Institute for Advanced StudyPrincetonUSA

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