Communication Complexity Under Product and Nonproduct Distributions |
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Authors: | Alexander A Sherstov |
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Affiliation: | 1. Department of Computer Sciences, The University of Texas at Austin, 1 University Station C0500, Austin, TX, 78712-0233, USA
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Abstract: | We solve an open problem in communication complexity posed by Kushilevitz and Nisan (1997). Let R∈(f) and $D^\mu_\in
(f)$D^\mu_\in
(f) denote the randomized and μ-distributional communication complexities of f, respectively (∈ a small constant). Yao’s well-known minimax principle states that $R_{\in}(f) = max_\mu
\{D^\mu_\in(f)\}$R_{\in}(f) = max_\mu
\{D^\mu_\in(f)\}. Kushilevitz and Nisan (1997) ask whether this equality is approximately preserved if the maximum is taken over product distributions
only, rather than all distributions μ. We give a strong negative answer to this question. Specifically, we prove the existence
of a function f : {0, 1}n ×{0, 1}n ? {0, 1}f : \{0, 1\}^n \times \{0, 1\}^n \rightarrow \{0, 1\} for which maxμ product {Dm ? (f)} = Q(1) but R ? (f) = Q(n)\{D^\mu_\in (f)\} = \Theta(1) \,{\textrm but}\, R_{\in} (f) = \Theta(n). We also obtain an exponential separation between the statistical query dimension and signrank, solving a problem previously
posed by the author (2007). |
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