Improved bounds for the randomized decision tree Complexity of recursive majority

Frédéric Magniez, Ashwin Nayak, Miklos Santha, Jonah Sherman, Gábor Tardos, David Xiao

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

We consider the randomized decision tree complexity of the recursive 3-majority function. We prove a lower bound of (1/2-δ)·2.57143h for the two-sided-error randomized decision tree complexity of evaluating height h formulae with error δ∈[0,1/2). This improves the lower bound of (1-2δ)(7/3)h given by Jayram, Kumar, and Sivakumar (STOC'03), and the one of (1-2δ)·2.55h given by Leonardos (ICALP'13). Second, we improve the upper bound by giving a new zero-error randomized decision tree algorithm that has complexity at most (1.007)·2.64944h. The previous best known algorithm achieved complexity (1.004)·2.65622h. The new lower bound follows from a better analysis of the base case of the recursion of Jayram et al. The new algorithm uses a novel "interleaving" of two recursive algorithms.

Original languageEnglish
Pages (from-to)612-638
Number of pages27
JournalRandom Structures and Algorithms
Volume48
Issue number3
DOIs
Publication statusPublished - May 1 2016

Keywords

  • Randomized decision tree
  • Recursive majority

ASJC Scopus subject areas

  • Software
  • Mathematics(all)
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics

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