Node Profiles of Symmetric Digital Search Trees: Concentration Properties
Abstract
We give a detailed asymptotic analysis of the profiles of random symmetric digital search trees, which are in close connection with the performance of the search complexity of random queries in such trees. While the expected profiles have been analyzed for several decades, the analysis of the variance turns out to be very difficult and challenging, and requires the combination of several different analytic techniques, including Mellin and Laplace transforms, analytic de-Poissonization, and Laplace convolutions. Our results imply concentration of the profiles in the range where the mean tends to infinity. Moreover, we also obtain a two-point concentration for the distributions of the height and the saturation level.
Keywords
Cite
@article{arxiv.1711.06941,
title = {Node Profiles of Symmetric Digital Search Trees: Concentration Properties},
author = {Michael Drmota and Michael Fuchs and Hsien-Kuei Hwang and Ralph Neininger},
journal= {arXiv preprint arXiv:1711.06941},
year = {2020}
}
Comments
The central limit theorem was removed from this version (and moved to a follow-up paper) since the proof in the previous versions was incomplete. Also, the word "Concentration Properties" was added to the title since this part now entirely focuses on such results