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The shortest disjunctive normal form of a random Boolean function
Author(s) -
Pippenger Nicholas
Publication year - 2003
Publication title -
random structures and algorithms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.314
H-Index - 69
eISSN - 1098-2418
pISSN - 1042-9832
DOI - 10.1002/rsa.10065
Subject(s) - mathematics , combinatorics , upper and lower bounds , boolean function , logarithm , function (biology) , discrete mathematics , parity function , disjunctive normal form , binary logarithm , mathematical proof , boolean expression , mathematical analysis , geometry , evolutionary biology , biology
This paper gives a new upper bound for the average length ℓ ( n ) of the shortest disjunctive normal form for a random Boolean function of n arguments, as well as new proofs of two old results related to this quantity. We consider a random Boolean function of n arguments to be uniformly distributed over all 2 2 nsuch functions. (This is equivalent to considering each entry in the truth‐table to be 0 or 1 independently and with equal probabilities.) We measure the length of a disjunctive normal form by the number of terms. (Measuring it by the number of literals would simply introduce a factor of n into all our asymptotic results.) We give a short proof using martingales of Nigmatullin's result that almost all Boolean functions have the length of their shortest disjunctive normal form asymptotic to the average length ℓ ( n ). We also give a short information‐theoretic proof of Kuznetsov's lower bound ℓ ( n ) ≥ (1 + o (1)) 2 n /log n log log n . (Here log denotes the logarithm to base 2.) Our main result is a new upper bound ℓ ( n ) ≤ (1 + o (1)) H ( n ) 2 n /log n log log n , where H ( n ) is a function that oscillates between 1.38826 … and 1.54169 … . The best previous upper bound, due to Korshunov, had a similar form, but with a function oscillating between 1.581411 … and 2.621132 … . The main ideas in our new bound are (1) the use of Rödl's “nibble” technique for solving packing and covering problems, (2) the use of correlation inequalities due to Harris and Janson to bound the effects of weakly dependent random variables, and (3) the solution of an optimization problem that determines the sizes of “nibbles” and larger “bites” to be taken at various stages of the construction. © 2003 Wiley Periodicals, Inc. Random Struct. Alg., 22: 161–186, 2003