Abstract
The worst-case complexity of an implementation of Quicksort depends on the random source that is used to select the pivot elements. In this paper we estimate the expected number of comparisons of Quicksort as a function of the entropy of the random source. We give upper and lower bounds and show that the expected number of comparisons increases from nlog n to n 2, if the entropy of the random source is bounded. As examples we show explicit bounds for distributions with bounded min-entropy and the geometrical distribution, as well as an upper bound when using a δ-random source.
Work supported by DFG research grant Scho 302/6-1
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© 2005 Springer-Verlag Berlin Heidelberg
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List, B., Maucher, M., Schöning, U., Schuler, R. (2005). Randomized Quicksort and the Entropy of the Random Source. In: Wang, L. (eds) Computing and Combinatorics. COCOON 2005. Lecture Notes in Computer Science, vol 3595. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11533719_46
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DOI: https://doi.org/10.1007/11533719_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28061-3
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