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Online Function Tracking with Generalized Penalties

  • Marcin Bienkowski
  • Stefan Schmid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6139)

Abstract

We attend to the classic setting where an observer needs to inform a tracker about an arbitrary time varying function f: ℕ0 →ℤ. This is an optimization problem, where both wrong values at the tracker and sending updates entail a certain cost. We consider an online variant of this problem, i.e., at time t, the observer only knows f(t′) for all t′ ≤ t. In this paper, we generalize existing cost models (with an emphasis on concave and convex penalties) and present two online algorithms. Our analysis shows that these algorithms perform well in a large class of models, and are even optimal in some settings.

Keywords

Penalty Function Input Sequence Competitive Ratio Online Algorithm Total Penalty 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marcin Bienkowski
    • 1
  • Stefan Schmid
    • 2
  1. 1.Institute of Computer ScienceUniversity of WrocławPoland
  2. 2.Deutsche Telekom LaboratoriesTU BerlinGermany

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