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Approximation-Friendly Discrepancy Rounding

  • Nikhil Bansal
  • Viswanath NagarajanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9682)

Abstract

Rounding linear programs using techniques from discrepancy is a recent approach that has been very successful in certain settings. However this method also has some limitations when compared to approaches such as randomized and iterative rounding. We provide an extension of the discrepancy-based rounding algorithm due to Lovett-Meka that (i) combines the advantages of both randomized and iterated rounding, (ii) makes it applicable to settings with more general combinatorial structure such as matroids. As applications of this approach, we obtain new results for various classical problems such as linear system rounding, degree-bounded matroid basis and low congestion routing.

Keywords

Random Walk Fractional Solution Multiplicative Error Iterate Rounding Violation Bound 
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 International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Department of Industrial and Operations EngineeringUniversity of MichiganAnn ArborUSA

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