Online maximum directed cut

Amotz Bar-Noy () and Michael Lampis ()
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Amotz Bar-Noy: City University of New York
Michael Lampis: City University of New York

Journal of Combinatorial Optimization, 2012, vol. 24, issue 1, No 4, 52-64

Abstract: 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 ε>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.

Keywords: MaxDiCut; Online algorithms; DAG (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s10878-010-9318-6

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