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Nearly-Optimal Mergesorts: Fast, Practical Sorting Methods That Optimally Adapt to Existing Runs

Authors J. Ian Munro , Sebastian Wild

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Author Details

J. Ian Munro
  • University of Waterloo, Canada
Sebastian Wild
  • University of Waterloo, Canada

Funding

This work was supported by the Natural Sciences and Engineering Research Council of Canada and the Canada Research Chairs Programme.

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J. Ian Munro and Sebastian Wild. Nearly-Optimal Mergesorts: Fast, Practical Sorting Methods That Optimally Adapt to Existing Runs. In 26th Annual European Symposium on Algorithms (ESA 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 112, pp. 63:1-63:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.ESA.2018.63

Abstract

We present two stable mergesort variants, "peeksort" and "powersort", that exploit existing runs and find nearly-optimal merging orders with negligible overhead. Previous methods either require substantial effort for determining the merging order (Takaoka 2009; Barbay & Navarro 2013) or do not have an optimal worst-case guarantee (Peters 2002; Auger, Nicaud & Pivoteau 2015; Buss & Knop 2018) . We demonstrate that our methods are competitive in terms of running time with state-of-the-art implementations of stable sorting methods.

Subject Classification

ACM Subject Classification
  • Theory of computation → Sorting and searching
Keywords
  • adaptive sorting
  • nearly-optimal binary search trees
  • Timsort

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References

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