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文章基本信息

  • 标题:Preserving Randomness for Adaptive Algorithms
  • 本地全文:下载
  • 作者:William Hoza ; Adam Klivans
  • 期刊名称:Electronic Colloquium on Computational Complexity
  • 印刷版ISSN:1433-8092
  • 出版年度:2016
  • 卷号:2016
  • 出版社:Universität Trier, Lehrstuhl für Theoretische Computer-Forschung
  • 摘要:

    Suppose Est is a randomized estimation algorithm that uses n random bits and outputs values in R d . We show how to execute Est on k adaptively chosen inputs using only n + O ( k log ( d + 1 )) random bits instead of the trivial n k (at the cost of mild increases in the error and failure probability). Our algorithm combines a variant of the INW pseudorandom generator (STOC '94) with a new scheme for shifting and rounding the outputs of Est . We prove that modifying the outputs of Est is necessary in this setting, and furthermore, our algorithm's randomness complexity is near-optimal in the case d O (1) . As an application, we give a randomness-efficient version of the Goldreich-Levin algorithm; our algorithm finds all Fourier coefficients with absolute value at least of a function F : 0 1 n − 1 1 using O ( n log n ) pol y (1 ) queries to F and O ( n ) random bits (independent of ), improving previous work by Bshouty et al. (JCSS '04).

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