Improved Approximation Guarantees for Weighted Matching in the Semi-Streaming Model

Authors Leah Epstein, Asaf Levin, Julián Mestre, Danny Segev



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Leah Epstein
Asaf Levin
Julián Mestre
Danny Segev

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Leah Epstein, Asaf Levin, Julián Mestre, and Danny Segev. Improved Approximation Guarantees for Weighted Matching in the Semi-Streaming Model. In 27th International Symposium on Theoretical Aspects of Computer Science. Leibniz International Proceedings in Informatics (LIPIcs), Volume 5, pp. 347-358, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2010) https://doi.org/10.4230/LIPIcs.STACS.2010.2476

Abstract

We study the maximum weight matching problem in the semi-streaming
model, and improve on the currently best one-pass algorithm due to
Zelke (Proc.\ STACS~'08, pages 669--680) by devising a deterministic
approach whose performance guarantee is $4.91 + \eps$. In addition,
we study {\em preemptive} online algorithms, a sub-class of one-pass
algorithms where we are only allowed to maintain a feasible matching
in memory at any point in time. All known results prior to Zelke's
belong to this sub-class. We provide a lower bound of $4.967$ on the
competitive ratio of any such deterministic algorithm, and hence
show that future improvements will have to store in memory a set of
edges which is not necessarily a feasible matching. We conclude by
presenting an empirical study, conducted in order to compare the
practical performance of our approach to that of previously
suggested algorithms.

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Keywords
  • Approximation guarantees
  • semi-streaming model
  • one-pass algorithm

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