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arXiv · 1609.08513

Median-of-k Jumplists and Dangling-Min BSTs

Markus E. Nebel·Elisabeth Neumann·Sebastian Wild
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Abstract

We extend randomized jumplists introduced by Br\"onnimann et al. (STACS 2003) to choose jump-pointer targets as median of a small sample for better search costs, and present randomized algorithms with expected $O(\log n)$ time complexity that maintain the probability distribution of jump pointers upon insertions and deletions. We analyze the expected costs to search, insert and delete a random element, and we show that omitting jump pointers in small sublists hardly affects search costs, but significantly reduces the memory consumption. We use a bijection between jumplists and "dangling-min BSTs", a variant of (fringe-balanced) binary search trees for the analysis. Despite their similarities, some standard analysis techniques for search trees fail for dangling-min trees (and hence for jumplists).

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Markus E. Nebel, Elisabeth Neumann, Sebastian Wild. 2016-09-27. Median-of-k Jumplists and Dangling-Min BSTs. https://doi.org/10.1137/1.9781611975505.8

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