Luc Devroye ; Omar Fawzi ; Nicolas Fraiman
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The height of scaled attachment random recursive trees
dmtcs:2788 -
Discrete Mathematics & Theoretical Computer Science,
January 1, 2010,
DMTCS Proceedings vol. AM, 21st International Meeting on Probabilistic, Combinatorial, and Asymptotic Methods in the Analysis of Algorithms (AofA'10)
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https://doi.org/10.46298/dmtcs.2788
The height of scaled attachment random recursive treesArticle
Authors: Luc Devroye ; Omar Fawzi 1; Nicolas Fraiman 1
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Luc Devroye;Omar Fawzi;Nicolas Fraiman
1 Department of Mathematics and Statistics [Montréal]
We study depth properties of a general class of random recursive trees where each node $n$ attaches to the random node $\lfloor nX_n \rfloor$ and $X_0, \ldots , X_n$ is a sequence of i.i.d. random variables taking values in $[0,1)$. We call such trees scaled attachment random recursive trees (SARRT). We prove that the height $H_n$ of a SARRT is asymptotically given by $H_n \sim \alpha_{\max} \log n$ where $\alpha_{\max}$ is a constant depending only on the distribution of $X_0$ whenever $X_0$ has a bounded density. This gives a new elementary proof for the height of uniform random recursive trees $H_n \sim e \log n$ that does not use branching random walks.
Volume: DMTCS Proceedings vol. AM, 21st International Meeting on Probabilistic, Combinatorial, and Asymptotic Methods in the Analysis of Algorithms (AofA'10)
Section: Proceedings
Published on: January 1, 2010
Imported on: January 31, 2017
Keywords: Random recursive tree,Random web model,Power of choice,Renewal process,Second moment method,[INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS],[MATH.MATH-CO] Mathematics [math]/Combinatorics [math.CO],[INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM],[INFO.INFO-CG] Computer Science [cs]/Computational Geometry [cs.CG]
Funding:
Source : OpenAIRE Graph
Funder: Natural Sciences and Engineering Research Council of Canada
Bibliographic References
1 Document citing this article
Mark Korenblit;Vadim Talis;Ilya Levin, Studies in computational intelligence, One-Max Constant-Probability Models for Complex Networks, pp. 181-188, 2014, 10.1007/978-3-319-05401-8_17.