Smoothed Performance Guarantees for Local Search

  • Tobias Brunsch
  • Heiko Röglin
  • Cyriel Rutten
  • Tjark Vredeveld
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6942)


We study popular local search and greedy algorithms for scheduling. The performance guarantee of these algorithms is well understood, but the worst-case lower bounds seem somewhat contrived and it is questionable if they arise in practical applications. To find out how robust these bounds are, we study the algorithms in the framework of smoothed analysis, in which instances are subject to some degree of random noise.

While the lower bounds for all scheduling variants with restricted machines are rather robust, we find out that the bounds are fragile for unrestricted machines. In particular, we show that the smoothed performance guarantee of the jump and the lex-jump algorithm are (in contrast to the worst case) independent of the number of machines. They are Θ(φ) and Θ(logφ), respectively, where 1/φ is a parameter measuring the magnitude of the perturbation. The latter immediately implies that also the smoothed price of anarchy is Θ(logφ) for routing games on parallel links. Additionally we show that for unrestricted machines also the greedy list scheduling algorithm has an approximation guarantee of Θ(logφ).


Local Search Greedy Algorithm Optimal Schedule Local Search Algorithm Processing Requirement 
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 2011

Authors and Affiliations

  • Tobias Brunsch
    • 1
  • Heiko Röglin
    • 1
  • Cyriel Rutten
    • 2
  • Tjark Vredeveld
    • 2
  1. 1.Dept. of Computer ScienceUniversity of BonnGermany
  2. 2.Dept. of Quantitative EconomicsMaastricht UniversityThe Netherlands

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