Online maximum directed cut

  • Authors:
  • Amotz Bar-Noy;Michael Lampis

  • Affiliations:
  • Doctoral Program in Computer Science, Graduate Center, City University of New York, New York, USA 10016;Doctoral Program in Computer Science, Graduate Center, City University of New York, New York, USA 10016

  • Venue:
  • Journal of Combinatorial Optimization
  • Year:
  • 2012

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Abstract

We investigate a natural online version of the well-known Maximum Directed Cut problem on DAGs. We propose a deterministic algorithm and show that it achieves a competitive ratio of $\frac{3\sqrt{3}}{2}\approx 2.5981$ . We then give a lower bound argument to show that no deterministic algorithm can achieve a ratio of $\frac{3\sqrt{3}}{2}-\epsilon$ for any 驴0 thus showing that our algorithm is essentially optimal. Then, we extend our technique to improve upon the analysis of an old result: we show that greedily derandomizing the trivial randomized algorithm for MaxDiCut in general graphs improves the competitive ratio from 4 to 3, and also provide a tight example.