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Data Summarization Beyond Monotonicity: Non-monotone Two-Stage Submodular Maximization

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Combinatorial Optimization and Applications (COCOA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14461))

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Abstract

The objective of a two-stage submodular maximization problem is to reduce the ground set using provided training functions that are submodular, with the aim of ensuring that optimizing new objective functions over the reduced ground set yields results comparable to those obtained over the original ground set. This problem has applications in various domains including data summarization. Existing studies often assume the monotonicity of the objective function, whereas our work pioneers the extension of this research to accommodate non-monotone submodular functions. We have introduced the first constant-factor approximation algorithms for this more general case.

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Author information

Authors and Affiliations

  1. Naveen Jindal School of Management, University of Texas at Dallas, Richardson, USA

    Shaojie Tang

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  1. Shaojie Tang
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Correspondence to Shaojie Tang .

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Editors and Affiliations

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

    Weili Wu

  2. Beijing Normal University, Zhuhai, China

    Jianxiong Guo

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Tang, S. (2024). Data Summarization Beyond Monotonicity: Non-monotone Two-Stage Submodular Maximization. In: Wu, W., Guo, J. (eds) Combinatorial Optimization and Applications. COCOA 2023. Lecture Notes in Computer Science, vol 14461. Springer, Cham. https://doi.org/10.1007/978-3-031-49611-0_20

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  • DOI: https://doi.org/10.1007/978-3-031-49611-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49610-3

  • Online ISBN: 978-3-031-49611-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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