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Asymptotic Properties of a Leader Election Algorithm

Part of: Limit theorems Algorithms - Computer Science Graph theory Combinatorial probability

Published online by Cambridge University Press:  14 July 2016

Ravi Kalpathy ,
Hosam M. Mahmoud  and
Mark Daniel Ward
Ravi Kalpathy*
Affiliation:
The George Washington University
Hosam M. Mahmoud*
Affiliation:
The George Washington University
Mark Daniel Ward*
Affiliation:
Purdue University
*
∗Postal address: Department of Statistics, The George Washington University, 2140 Pennsylvania Avenue, Washington, DC 20052, USA.
∗Postal address: Department of Statistics, The George Washington University, 2140 Pennsylvania Avenue, Washington, DC 20052, USA.
∗∗∗∗Postal address: Department of Statistics, Purdue University, 150 North University Street, West Lafayette, IN 47907-1451, USA. Email address: [email protected]
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Abstract

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We consider a serialized coin-tossing leader election algorithm that proceeds in rounds until a winner is chosen, or all contestants are eliminated. The analysis allows for either biased or fair coins. We find the exact distribution for the duration of any fixed contestant; asymptotically, it turns out to be a geometric distribution. Rice's method (an analytic technique) shows that the moments of the duration contain oscillations, which we give explicitly for the mean and variance. We also use convergence in the Wasserstein metric space to show that the distribution of the total number of coin flips (among all participants), suitably normalized, approaches a normal limiting random variable.

Keywords

Leader electionanalytic combinatoricsRice's methodfixed pointcontraction methodWasserstein metric spaceweak convergence

MSC classification

Secondary: 60C05: Combinatorial probability 05C05: Trees 60F05: Central limit and other weak theorems 68W40: Analysis of algorithms
Type
Research Article
Information
Journal of Applied Probability , Volume 48 , Issue 2 , June 2011 , pp. 569 - 575
Copyright
Copyright © Applied Probability Trust 2011 

Footnotes

Research supported by NSF Science & Technology Center grant CCF-0939370.

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