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International Conference on Algorithmic Applications in Management

AAIM 2019: Algorithmic Aspects in Information and Management pp 249–260Cite as

Maximize a Monotone Function with a Generic Submodularity Ratio

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11640))

Abstract

Generic submodularity ratio \(\gamma \) is a general measurement to characterize how close a nonnegative monotone set function is to be submodular. In this paper, we make a systematic analysis of greedy algorithms for maximizing a monotone and normalized set function with a generic submodularity ratio \(\gamma \) under Cardinality constraints, Knapsack constraints, Matroid constraints and K-intersection constraints.

This research was supported in part by the National Natural Science Foundation of China under grant numbers 11201439 and 11871442, and was also supported in part by the Natural Science Foundation of Shandong Province under grant number ZR2019MA052 and the Fundamental Research Funds for the Central Universities.

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Author information

Authors and Affiliations

  1. School of Mathematical Science, Ocean University of China, Qingdao, 266100, Shandong, People’s Republic of China

    Qingqin Nong, Tao Sun, Suning Gong, Qizhi Fang & Xiaoyu Shao

  2. Department of Computer Science, University of Texas, Dallas, 75083, USA

    Dingzhu Du

Authors
  1. Qingqin Nong
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  2. Tao Sun
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  3. Suning Gong
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  4. Qizhi Fang
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  5. Dingzhu Du
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  6. Xiaoyu Shao
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Corresponding author

Correspondence to Qingqin Nong .

Editor information

Editors and Affiliations

  1. The University of Texas at Dallas, Richardson, TX, USA

    Ding-Zhu Du

  2. Hefei University of Technology, Hefei, China

    Lian Li

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xiaoming Sun

  4. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Jialin Zhang

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Nong, Q., Sun, T., Gong, S., Fang, Q., Du, D., Shao, X. (2019). Maximize a Monotone Function with a Generic Submodularity Ratio. In: Du, DZ., Li, L., Sun, X., Zhang, J. (eds) Algorithmic Aspects in Information and Management. AAIM 2019. Lecture Notes in Computer Science(), vol 11640. Springer, Cham. https://doi.org/10.1007/978-3-030-27195-4_23

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  • DOI: https://doi.org/10.1007/978-3-030-27195-4_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27194-7

  • Online ISBN: 978-3-030-27195-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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