Combinatorics Probability Computing · 2004 · 49 citations · 12 references
ComputingComputer ScienceMathematicsAlgorithmsProbability TheoryTheory Of ComputationAlgorithmic ComplexityCircuit ComplexityComplexity TheoryComputational ComplexityLimit TheoremsStructural ComplexityBoolean Circuit ComplexityBoolean Function ComplexityBoolean FunctionsComplexityComplexity Of Boolean FunctionsComplexity Of Specific Function FamiliesUpper Bound Computation
We consider Boolean functions over $n$ variables. Any such function can be represented (and computed) by a complete binary tree with and or or in the internal nodes and a literal in the external nodes, and many different trees can represent the same function, so that a fundamental question is related to the so-called complexity of a Boolean function: $L(f):=$ minimal size of a tree computing $f$ . The existence of a limiting probability distribution $P(\cdot)$ on the set of and/or trees was shown by Lefmann and Savický [8]. We give here an alternative proof, which leads to effective computation in simple cases. We also consider the relationship between the probability $P(f)$ and the complexity $L(f)$ of a Boolean function $f$ . A detailed analysis of the functions enumerating some sub-families of trees, and of their radius of convergence, allows us to improve on the upper bound of $P(f)$ , established by Lefmann and Savický.
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PE, P. Billingsley · Journal of the American Statistical Association · 1996 · 6.7K citations
Systems of functional equations
Michael Drmota · Random Structures and Algorithms · 2000 · 145 citations