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Sublinear Estimation of Weighted Matchings in Dynamic Data Streams

  • Marc BuryEmail author
  • Chris Schwiegelshohn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9294)

Abstract

This paper presents an algorithm for estimating the weight of a maximum weighted matching by augmenting any estimation routine for the size of an unweighted matching. The algorithm is implementable in any streaming model including dynamic graph streams. We also give the first constant estimation for the maximum matching size in a dynamic graph stream for planar graphs (or any graph with bounded arboricity) using \(\tilde{O}(n^{4/5})\) space which also extends to weighted matching. Using previous results by Kapralov, Khanna, and Sudan (2014) we obtain a polylog(n) approximation for general graphs using polylog(n) space in random order streams, respectively. In addition, we give a space lower bound of Ω(n1 − ε) for any randomized algorithm estimating the size of a maximum matching up to a 1 + O(ε) factor for adversarial streams.

Keywords

Planar Graph Maximum Match Pseudorandom Generator Weighted Match Stream Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Efficient Algorithms and Complexity TheoryTU DortmundDortmundGermany

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