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Scaling up Local Search for Minimum Vertex Cover in Large Graphs by Parallel Kernelization

  • Wanru GaoEmail author
  • Tobias Friedrich
  • Timo Kötzing
  • Frank Neumann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10400)

Abstract

We investigate how well-performing local search algorithms for small or medium size instances can be scaled up to perform well for large inputs. We introduce a parallel kernelization technique that is motivated by the assumption that graphs in medium to large scale are composed of components which are on their own easy for state-of-the-art solvers but when hidden in large graphs are hard to solve. To show the effectiveness of our kernelization technique, we consider the well-known minimum vertex cover problem and two state-of-the-art solvers called NuMVC and FastVC. Our kernelization approach reduces an existing large problem instance significantly and produces better quality results on a wide range of benchmark instances and real world graphs.

Keywords

Vertex cover Local search algorithms 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wanru Gao
    • 1
    Email author
  • Tobias Friedrich
    • 1
    • 2
  • Timo Kötzing
    • 2
  • Frank Neumann
    • 1
  1. 1.School of Computer ScienceThe University of AdelaideAdelaideAustralia
  2. 2.Hasso Plattner InstitutePotsdamGermany

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