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An Improved Kernel for the Complementary Maximal Strip Recovery Problem

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Computing and Combinatorics (COCOON 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9198))

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Abstract

We study the parameterized complexity of the complementary maximal strip recovery problem (CMSR), which is to delete the minimum number of gene markers from two genetic maps so that the remaining markers in the maps can be partitioned into matched strips. It is known that the CMSR problem has a kernel of size bounded by 78k, and a question has been raised whether this bound can be further improved. In this paper, we answer this question by presenting an improved kernel of size 58k for the CMSR problem. Our results are based on the techniques of building a weighted bipartite graph from a given instance of the CMSR problem so that three additional and more powerful reduction rules can be applied to further reduce the kernel size.

This work is supported by the National Natural Science Foundation of China under Grants (61232001, 61472449, 61420106009, 61402054).

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, People’s Republic of China

    Shuai Hu, Wenjun Li & Jianxin Wang

Authors
  1. Shuai Hu
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  2. Wenjun Li
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  3. Jianxin Wang
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Corresponding author

Correspondence to Wenjun Li .

Editor information

Editors and Affiliations

  1. Beijing University of Technology, Beijing, China

    Dachuan Xu

  2. University of New Brunswick, Fredericton, Canada

    Donglei Du

  3. University of Texas at Dallas, Richardson, USA

    Dingzhu Du

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Hu, S., Li, W., Wang, J. (2015). An Improved Kernel for the Complementary Maximal Strip Recovery Problem. In: Xu, D., Du, D., Du, D. (eds) Computing and Combinatorics. COCOON 2015. Lecture Notes in Computer Science(), vol 9198. Springer, Cham. https://doi.org/10.1007/978-3-319-21398-9_47

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  • DOI: https://doi.org/10.1007/978-3-319-21398-9_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21397-2

  • Online ISBN: 978-3-319-21398-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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