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Deeper Local Search for Better Approximation on Maximum Internal Spanning Trees

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Algorithms - ESA 2014 (ESA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8737))

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Abstract

Spanning tree has been fundamental in the research of graph algorithms. In this paper, we study the optimization problem MaxIST, which maximizes the number of internal nodes in a spanning tree of a given graph, and is a generalization of the famous Hamiltonian-Path problem. We present a polynomial-time approximation algorithm based on a deep local search strategy, identify combinatorial structures that support thorough analysis on the spanning trees resulted from such deep local search strategies, and prove that our algorithm has an approximation ratio 1.5 for the MaxIST problem, improving the previous best approximation algorithm of ratio 5/3 for the problem.

This work is supported by the National Natural Science Foundation of China under Grant (61173051, 61103033, 71221061).

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, Hunan, 410083, P.R. China

    Wenjun Li, Jianer Chen & Jianxin Wang

  2. Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, 77843-3112, USA

    Jianer Chen

Authors
  1. Wenjun Li
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  2. Jianer Chen
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  3. Jianxin Wang
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Editor information

Editors and Affiliations

  1. Massachusetts Institute of Technology, Cambridge, MA, USA

    Andreas S. Schulz

  2. Karlsruhe Institute of Technology (KIT), Kaiserstruhe 12, 76131, Karlsruhe, Germany

    Dorothea Wagner

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Li, W., Chen, J., Wang, J. (2014). Deeper Local Search for Better Approximation on Maximum Internal Spanning Trees. In: Schulz, A.S., Wagner, D. (eds) Algorithms - ESA 2014. ESA 2014. Lecture Notes in Computer Science, vol 8737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44777-2_53

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  • DOI: https://doi.org/10.1007/978-3-662-44777-2_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44776-5

  • Online ISBN: 978-3-662-44777-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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