Tight approximation algorithms for bichromatic graph diameter and related problems
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LIPIcs-ICALP-2019-47.pdf
Description
Published version
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492.42 KB
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Author(s)
Dalirrooyfard, Mina
Williams, Virginia Vassilevska
Vyas, Nikhil
Wein, Nicole
Date Issued
2019
Journal
Leibniz International Proceedings in Informatics, LIPIcs
Citation
Dalirrooyfard, Mina, Williams, Virginia Vassilevska, Vyas, Nikhil and Wein, Nicole. 2019. "Tight approximation algorithms for bichromatic graph diameter and related problems." Leibniz International Proceedings in Informatics, LIPIcs, 132.
Version
Final published version
Abstract
© Graham Cormode, Jacques Dark, and Christian Konrad; licensed under Creative Commons License CC-BY Some of the most fundamental and well-studied graph parameters are the Diameter (the largest shortest paths distance) and Radius (the smallest distance for which a “center” node can reach all other nodes). The natural and important ST-variant considers two subsets S and T of the vertex set and lets the ST-diameter be the maximum distance between a node in S and a node in T, and the ST-radius be the minimum distance for a node of S to reach all nodes of T. The bichromatic variant is the special case in which S and T partition the vertex set. In this paper we present a comprehensive study of the approximability of ST and Bichromatic Diameter, Radius, and Eccentricities, and variants, in graphs with and without directions and weights. We give the first nontrivial approximation algorithms for most of these problems, including time/accuracy trade-off upper and lower bounds. We show that nearly all of our obtained bounds are tight under the Strong Exponential Time Hypothesis (SETH), or the related Hitting Set Hypothesis. For instance, for Bichromatic Diameter in undirected weighted graphs with m edges, we present an Õ(m3/2) time 1 5/3-approximation algorithm, and show that under SETH, neither the running time, nor the approximation factor can be significantly improved while keeping the other unchanged.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.4230/LIPIcs.ICALP.2019.47
