Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-22006-7_27
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dc.titleImproved bounds for the randomized decision tree complexity of recursive majority
dc.contributor.authorMagniez, F.
dc.contributor.authorNayak, A.
dc.contributor.authorSantha, M.
dc.contributor.authorXiao, D.
dc.date.accessioned2014-12-12T08:03:25Z
dc.date.available2014-12-12T08:03:25Z
dc.date.issued2011
dc.identifier.citationMagniez, F.,Nayak, A.,Santha, M.,Xiao, D. (2011). Improved bounds for the randomized decision tree complexity of recursive majority. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6755 LNCS (PART 1) : 317-329. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-22006-7_27" target="_blank">https://doi.org/10.1007/978-3-642-22006-7_27</a>
dc.identifier.isbn9783642220050
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/117252
dc.description.abstractWe consider the randomized decision tree complexity of the recursive 3-majority function. For evaluating height h formulae, we prove a lower bound for the δ-two-sided-error randomized decision tree complexity of (1 - 2δ)(5/2) h , improving the lower bound of (1 - 2δ)(7/3) h given by Jayram, Kumar, and Sivakumar (STOC '03). Second, we improve the upper bound by giving a new zero-error randomized decision tree algorithm that has complexity at most (1.007) •2.64946 h . The previous best known algorithm achieved complexity (1.004) •2.65622 h . 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. © 2011 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-22006-7_27
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCENTRE FOR QUANTUM TECHNOLOGIES
dc.description.doi10.1007/978-3-642-22006-7_27
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume6755 LNCS
dc.description.issuePART 1
dc.description.page317-329
dc.identifier.isiutNOT_IN_WOS
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