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Theory of Computing Systems

, Volume 61, Issue 4, pp 1128–1177 | Cite as

The Advice Complexity of a Class of Hard Online Problems

  • Joan Boyar
  • Lene M. Favrholdt
  • Christian Kudahl
  • Jesper W. Mikkelsen
Article
  • 172 Downloads

Abstract

The advice complexity of an online problem is a measure of how much knowledge of the future an online algorithm needs in order to achieve a certain competitive ratio. Using advice complexity, we define the first online complexity class, AOC. The class includes independent set, vertex cover, dominating set, and several others as complete problems. AOC-complete problems are hard, since a single wrong answer by the online algorithm can have devastating consequences. For each of these problems, we show that \(\log \left (1+(c-1)^{c-1}/c^{c}\right )n={\Theta } (n/c)\) bits of advice are necessary and sufficient (up to an additive term of \(O(\log n)\)) to achieve a competitive ratio of c. The results are obtained by introducing a new string guessing problem related to those of Emek et al. (Theor. Comput. Sci. 412(24), 2642–2656 2011) and Böckenhauer et al. (Theor. Comput. Sci. 554, 95–108 2014). It turns out that this gives a powerful but easy-to-use method for providing both upper and lower bounds on the advice complexity of an entire class of online problems, the AOC-complete problems. Previous results of Halldórsson et al. (Theor. Comput. Sci. 289(2), 953–962 2002) on online independent set, in a related model, imply that the advice complexity of the problem is Θ(n/c). Our results improve on this by providing an exact formula for the higher-order term. For online disjoint path allocation, Böckenhauer et al. (ISAAC 2009) gave a lower bound of Ω(n/c) and an upper bound of \(O((n\log c)/c)\) on the advice complexity. We improve on the upper bound by a factor of \(\log c\). For the remaining problems, no bounds on their advice complexity were previously known.

Keywords

Online algorithms Advice complexity Complexity class Asymmetric string guessing Covering designs Asymmetric Online Covering (AOC) 

Notes

Acknowledgments

The authors would like to thank Magnus Gausdal Find for helpful discussions.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Joan Boyar
    • 1
  • Lene M. Favrholdt
    • 1
  • Christian Kudahl
    • 1
  • Jesper W. Mikkelsen
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of Southern DenmarkOdense MDenmark

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