Rectangle Size Bounds and Threshold Covers in Communication Complexity

  • Hartmut Klauck
  • Published 2003 in IEEE Conference on Computational Complexity

Abstract

We investigate the power of the most important lower bound technique in randomized communication complexity, which is based on an evaluation of the maximal size of approximately monochromatic rectangles, with respect to arbitrary distributions on the inputs. While it is known that the 0-error version of this bound is polynomially tight for deterministic communication, nothing in this direction is known for constant error and randomized communication complexity. We first study a one-sided version of this bound and obtain that its value lies between the MAand AM -complexities of the considered function. Hence the lower bound actually works for a (communication) complexity class between MA∩ co−MA and AM ∩ co−AM , and allows to show that the MA-complexity of the disjointness problem is Ω( √ n). Following this we consider the conjecture that the lower bound method is polynomially tight for randomized communication complexity. First we disprove a distributional version of this conjecture. Then we give a combinatorial characterization of the value of the lower bound method, in which the optimization over all distributions is absent. This characterization is done by what we call a bounded error uniform threshold cover, and reduces showing tightness of the bound to the construction of an efficient protocol for a specific communication problem. We then study relaxations of bounded error uniform threshold covers, namely approximate majority covers and majority covers, and exhibit exponential separations between them. Each of these covers captures a lower bound method previously used for randomized communication complexity.

DOI: 10.1109/CCC.2003.1214415

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Cite this paper

@inproceedings{Klauck2003RectangleSB, title={Rectangle Size Bounds and Threshold Covers in Communication Complexity}, author={Hartmut Klauck}, booktitle={IEEE Conference on Computational Complexity}, year={2003} }