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Process convergence for the complexity of Radix Selection on Markov sources

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  • Leckey, Kevin
  • Neininger, Ralph
  • Sulzbach, Henning

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.

Suggested Citation

  • Leckey, Kevin & Neininger, Ralph & Sulzbach, Henning, 2019. "Process convergence for the complexity of Radix Selection on Markov sources," Stochastic Processes and their Applications, Elsevier, vol. 129(2), pages 507-538.
    • Handle: RePEc:eee:spapps:v:129:y:2019:i:2:p:507-538
    DOI: 10.1016/j.spa.2018.03.009
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    References listed on IDEAS

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    1. Ragab, Mahmoud & Roesler, Uwe, 2014. "The Quicksort process," Stochastic Processes and their Applications, Elsevier, vol. 124(2), pages 1036-1054.
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    1. Roesler, Uwe, 2020. "Almost sure convergence to the Quicksort process," Stochastic Processes and their Applications, Elsevier, vol. 130(9), pages 5290-5309.

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