Optimal depth-first algorithms and equilibria of independent distributions on multi-branching trees

Weiguang Peng, Ning Ning Peng, Keng Meng Ng, Kazuyuki Tanaka, Yue Yang

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The main purpose of this paper is to answer two questions about the distributional complexity of multi-branching trees. We first show that for any independent distribution d on assignments for a multi-branching tree, a certain directional algorithm DIRd is optimal among all the depth-first algorithms (including non-directional ones) with respect to d. We next generalize Suzuki–Niida's result on binary trees to the case of multi-branching trees. By means of this result and our optimal algorithm, we show that for any balanced multi-branching AND–OR tree, the optimal distributional complexity among all the independent distributions (ID) is (under an assumption that the probability of the root having value 0 is neither 0 nor 1) actually achieved by an independent and identical distribution (IID).

Original languageEnglish
Pages (from-to)41-45
Number of pages5
JournalInformation Processing Letters
Volume125
DOIs
Publication statusPublished - 2017 Sep

Keywords

  • Analysis of algorithms
  • Computational complexity
  • Depth-first algorithms
  • Independent distribution
  • Multi-branching trees

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Computer Science Applications

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