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  • 标题:Sign-Rank Can Increase Under Intersection
  • 本地全文:下载
  • 作者:Mark Bun ; Nikhil Mande ; Justin Thaler
  • 期刊名称:Electronic Colloquium on Computational Complexity
  • 印刷版ISSN:1433-8092
  • 出版年度:2019
  • 卷号:2019
  • 页码:1-18
  • 出版社:Universität Trier, Lehrstuhl für Theoretische Computer-Forschung
  • 摘要:

    The communication class UP P cc is a communication analog of the Turing Machine complexity class P P . It is characterized by a matrix-analytic complexity measure called sign-rank (also called dimension complexity), and is essentially the most powerful communication class against which we know how to prove lower bounds.For a communication problem f , let f f denote the function that evaluates f on two disjoint inputs and outputs the AND of the results. We exhibit a communication problem f with UP P ( f ) = O ( log n ) , and UP P ( f f ) = ( log 2 n ) . This is the first result showing that UP P communication complexity can increase by more than a constant factor under intersection. We view this as a first step toward showing that UP P cc , the class of problems with polylogarithmic-cost UP P communication protocols, is not closed under intersection.Our result shows that the function class consisting of intersections of two majorities on n bits has dimension complexity n ( log n ) . This matches an upper bound of (Klivans, O'Donnell, and Servedio, FOCS 2002), who used it to give a quasipolynomial time algorithm for PAC learning intersections of polylogarithmically many majorities. Hence, fundamentally new techniques will be needed to learn this class of functions in polynomial time.

  • 关键词:communication complexity ; dimension complexity ; Learning theory ; sign rank
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