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

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AI 2017: Advances in Artificial Intelligence (AI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10400))

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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.

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Correspondence to Wanru Gao .

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Gao, W., Friedrich, T., Kötzing, T., Neumann, F. (2017). Scaling up Local Search for Minimum Vertex Cover in Large Graphs by Parallel Kernelization. In: Peng, W., Alahakoon, D., Li, X. (eds) AI 2017: Advances in Artificial Intelligence. AI 2017. Lecture Notes in Computer Science(), vol 10400. Springer, Cham. https://doi.org/10.1007/978-3-319-63004-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-63004-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63003-8

  • Online ISBN: 978-3-319-63004-5

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