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Two-stage submodular maximization under curvature

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Abstract

The concept of submodularity has wide applications in data science, artificial intelligence, and machine learning, providing a boost to the investigation of new ideas, innovative techniques, and creative algorithms to solve different submodular optimization problems arising from a diversity of applications. However pure submodular problems only represent a small portion of the problems we are facing in real life applications. In this paper, we further discuss the two-stage submodular maximization problem under a \(\ell \)-matroid constraint. We design an approximation algorithm with constant approximation ratio with respect to the curvature, which improves the previous bound. In addition, we generalize our algorithm to the two-stage submodular maximization problem under a \(\ell \)-exchange system constraint.

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Acknowledgements

The authors would like to thank the referees for giving this paper a careful reading and many valuable comments and useful suggestions.

Funding

The research is supported by NSFC (Nos.12271259, 11971349, 12101314, 12131003), Qinglan Project, Natural Science Foundation of Jiangsu Province (No. BK20200723), and Jiangsu Province Higher Education Foundation (No.20KJB110022).

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Correspondence to Hong Chang.

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A preliminary version of this paper appeared in Proceedings of the 15th Annual International Conference on Combinatorial Optimization and Applications (COCOA).

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Li, Y., Liu, Z., Xu, C. et al. Two-stage submodular maximization under curvature. J Comb Optim 45, 77 (2023). https://doi.org/10.1007/s10878-023-01001-0

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  • DOI: https://doi.org/10.1007/s10878-023-01001-0

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