Process convergence for the complexity of Radix Selection on Markov sources

Kevin Leckey, Ralph Neininger and Henning Sulzbach

Stochastic Processes and their Applications, 2019, vol. 129, issue 2, 507-538

Abstract: A fundamental algorithm for selecting ranks from a finite subset of an ordered set is Radix Selection. This algorithm requires the data to be given as strings of symbols over an ordered alphabet, e.g., binary expansions of real numbers. Its complexity is measured by the number of symbols that have to be read. In this paper the model of independent data identically generated from a Markov chain is considered.

Keywords: Radix Selection; Gaussian process; Markov source model; Complexity; Weak convergence; Probabilistic analysis of algorithms (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.spa.2018.03.009

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