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Semi-Online Bipartite Matching

Authors Ravi Kumar, Manish Purohit, Aaron Schild, Zoya Svitkina, Erik Vee

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Ravi Kumar
  • Google, Mountain View, CA, USA
Manish Purohit
  • Google, Mountain View, CA, USA
Aaron Schild
  • University of California, Berkeley, CA, USA
Zoya Svitkina
  • Google, Mountain View, CA, USA
Erik Vee
  • Google, Mountain View, CA, USA

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Ravi Kumar, Manish Purohit, Aaron Schild, Zoya Svitkina, and Erik Vee. Semi-Online Bipartite Matching. In 10th Innovations in Theoretical Computer Science Conference (ITCS 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 124, pp. 50:1-50:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.ITCS.2019.50

Abstract

In this paper we introduce the semi-online model that generalizes the classical online computational model. The semi-online model postulates that the unknown future has a predictable part and an adversarial part; these parts can be arbitrarily interleaved. An algorithm in this model operates as in the standard online model, i.e., makes an irrevocable decision at each step. We consider bipartite matching in the semi-online model. Our main contributions are competitive algorithms for this problem and a near-matching hardness bound. The competitive ratio of the algorithms nicely interpolates between the truly offline setting (i.e., no adversarial part) and the truly online setting (i.e., no predictable part).

Subject Classification

ACM Subject Classification
  • Theory of computation → Online algorithms
Keywords
  • Semi-Online Algorithms
  • Bipartite Matching

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