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  • 标题:Optimal Unateness Testers for Real-Valued Functions: Adaptivity Helps
  • 本地全文:下载
  • 作者:Roksana Baleshzar ; Deeparnab Chakrabarty ; Ramesh Krishnan S. Pallavoor
  • 期刊名称:Electronic Colloquium on Computational Complexity
  • 印刷版ISSN:1433-8092
  • 出版年度:2017
  • 卷号:2017
  • 出版社:Universität Trier, Lehrstuhl für Theoretische Computer-Forschung
  • 摘要:

    We study the problem of testing unateness of functions f : 0 1 d R We give a O ( d log d ) -query nonadaptive tester and a O ( d ) -query adaptive tester and show that both testers are optimal for a fixed distance parameter . Previously known unateness testers worked only for Boolean functions, and their query complexity had worse dependence on the dimension both for the adaptive and the nonadaptive case. Moreover, no lower bounds for testing unateness were known. We also generalize our results to obtain optimal unateness testers for functions f : [ n ] d R .

    Our results establish that adaptivity helps with testing unateness of real-valued functions on domains of the form 0 1 d and, more generally, [ n ] d . This stands in contrast to the situation for monotonicity testing where there is no adaptivity gap for functions f : [ n ] d R .

  • 关键词:testing unateness
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