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On Unknown Small Subsets and Implicit Measures: New Techniques for Parameterized Algorithms

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Abstract

Parameterized computation is a recently proposed alternative approach to dealing with NP-hard problems. Developing efficient parameterized algorithms has become a very active research area in the current research in theoretical computer science. In this paper, we investigate a number of new algorithmic techniques that were proposed and initiated by ourselves in our research in parameterized computation. The techniques have proved to be very useful and promising, and have led to improved parameterized algorithms for many well-known NP-hard problems.

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Authors and Affiliations

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

    Jianer Chen & Qi-Long Feng

  2. Department of Computer Science, Texas A&M University, College Station, Texas, 77843-3112, U.S.A.

    Jianer Chen

Authors
  1. Jianer Chen
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  2. Qi-Long Feng
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Corresponding author

Correspondence to Qi-Long Feng.

Additional information

This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61173051, 61103033, and 71221061.

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Chen, J., Feng, QL. On Unknown Small Subsets and Implicit Measures: New Techniques for Parameterized Algorithms. J. Comput. Sci. Technol. 29, 870–878 (2014). https://doi.org/10.1007/s11390-014-1474-1

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  • DOI: https://doi.org/10.1007/s11390-014-1474-1

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