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  • 标题:The Power of Many Samples in Query Complexity
  • 本地全文:下载
  • 作者:Andrew Bassilakis ; Andrew Drucker ; Mika Göös
  • 期刊名称:Electronic Colloquium on Computational Complexity
  • 印刷版ISSN:1433-8092
  • 出版年度:2020
  • 卷号:2020
  • 页码:1-16
  • 出版社:Universität Trier, Lehrstuhl für Theoretische Computer-Forschung
  • 摘要:The randomized query complexity R(f) of a boolean function f : {0, 1} n → {0, 1} is famously characterized (via Yao’s minimax) by the least number of queries needed to distinguish a distribution D0 over 0-inputs from a distribution D1 over 1-inputs, maximized over all pairs (D0, D1). We ask: Does this task become easier if we allow query access to infinitely many samples from either D0 or D1? We show the answer is no: There exists a hard pair (D0, D1) such that distinguishing D∞ 0 from D∞ 1 requires Θ(R(f)) many queries. As an application, we show that for any composed function f ◦ g we have R(f ◦ g) ≥ Ω(fbs(f)R(g)) where fbs denotes fractional block sensitivity.
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