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Streaming Submodular Maximization with the Chance Constraint

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Frontiers of Algorithmic Wisdom (IJTCS-FAW 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13461))

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Abstract

Submodular optimization plays a significant role in combinatorial problems due to its diminishing marginal return property. Many artificial intelligence and machine learning problems can be cast as submodular maximization problems with applications in object detection, data summarization, and video summarization. In this paper, we consider the problem of monotone submodular function maximization in the streaming setting with the chance constraint. Using mainly the idea of guessing the threshold, we propose streaming algorithms and prove good approximation guarantees and computational complexity. In our experiments, we demonstrate the efficiency of our algorithm on synthetic data for the influence maximization problem and indicate that even if the strong restriction of chance constraint is imposed, we can still get a good solution. To the best of our knowledge, this is the first paper to study the problem of monotone submodular function maximization with chance constraint in the streaming model.

This work was supported in part by the National Natural Science Foundation of China (11971447, 11871442), and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, Shandong, China

    Shufang Gong, Bin Liu & Qizhi Fang

Authors
  1. Shufang Gong
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  2. Bin Liu
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  3. Qizhi Fang
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Corresponding author

Correspondence to Bin Liu .

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Editors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Minming Li

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

    Xiaoming Sun

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Gong, S., Liu, B., Fang, Q. (2022). Streaming Submodular Maximization with the Chance Constraint. In: Li, M., Sun, X. (eds) Frontiers of Algorithmic Wisdom. IJTCS-FAW 2022. Lecture Notes in Computer Science, vol 13461. Springer, Cham. https://doi.org/10.1007/978-3-031-20796-9_10

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  • DOI: https://doi.org/10.1007/978-3-031-20796-9_10

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  • Online ISBN: 978-3-031-20796-9

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