A Gaussian limit process for optimal FIND algorithms

Henning Sulzbach (INRIA Paris-Rocquencourt)
Ralph Neininger (Goethe University Frankfurt)
Michael Drmota (TU Vienna)


We consider versions of the FIND algorithm where the pivot element used is the median of a subset chosen uniformly at random from the data. For the median selection we assume that subsamples of size asymptotic to $c \cdot n^\alpha$ are chosen, where $0< \alpha \leq \frac{1}{2}$, $c>0$ and $n$ is the size of the data set to be split. We consider the complexity of FIND as a process in the rank to be selected and measured by the number of key comparisons required. After normalization we show weak convergence of the complexity to a centered Gaussian process as $n \to \infty$, which depends on $\alpha$. The proof relies on a contraction argument for probability distributions on càdlàg functions. We also identify the covariance function of the Gaussian limit process and discuss path and tail properties.

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Pages: 1-28

Publication Date: January 6, 2014

DOI: 10.1214/EJP.v19-2933


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