License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.SEA.2017.22
URN: urn:nbn:de:0030-drops-76202
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2017/7620/
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Focke, Jacob ; Megow, Nicole ; Meißner, Julie

Minimum Spanning Tree under Explorable Uncertainty in Theory and Experiments

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LIPIcs-SEA-2017-22.pdf (0.7 MB)

Abstract

We consider the minimum spanning tree (MST) problem in an uncertainty model where uncertain edge weights can be explored at extra cost. The task is to find an MST by querying a minimum number of edges for their exact weight. This problem has received quite some attention from the algorithms theory community. In this paper, we conduct the first practical experiments for MST under uncertainty, theoretically compare three known algorithms, and compare theoretical with practical behavior of the algorithms. Among others, we observe that the average performance and the absolute number of queries are both far from the theoretical worst-case bounds. Furthermore, we investigate a known general preprocessing procedure and develop an implementation thereof that maximally reduces the data uncertainty. We also characterize a class of instances that is solved completely by our preprocessing. Our experiments are based on practical data from an application in telecommunications and uncertainty instances generated from the standard TSPLib graph library.

BibTeX - Entry

@InProceedings{focke_et_al:LIPIcs:2017:7620,
  author =	{Jacob Focke and Nicole Megow and Julie Mei{\ss}ner},
  title =	{{Minimum Spanning Tree under Explorable Uncertainty in Theory and Experiments}},
  booktitle =	{16th International Symposium on Experimental Algorithms (SEA 2017)},
  pages =	{22:1--22:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-036-1},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{75},
  editor =	{Costas S. Iliopoulos and Solon P. Pissis and Simon J. Puglisi and Rajeev Raman},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7620},
  URN =		{urn:nbn:de:0030-drops-76202},
  doi =		{10.4230/LIPIcs.SEA.2017.22},
  annote =	{Keywords: MST, explorable uncertainty, competitive ratio, experimental algorithms}
}

Keywords: MST, explorable uncertainty, competitive ratio, experimental algorithms
Collection: 16th International Symposium on Experimental Algorithms (SEA 2017)
Issue Date: 2017
Date of publication: 07.08.2017

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