Resilient monotone submodular function maximization
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1703.07280.pdf
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Author(s)
Tzoumas, Vasileios
Gatsis, Konstantinos
Jadbabaie, Ali
Pappas, George J.
Jadbabaie-Moghadam, Ali
Date Issued
January 2018
Journal
2017 IEEE 56th Annual Conference on Decision and Control (CDC)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Tzoumas, Vasileios, et al. “Resilient Monotone Submodular Function Maximization.” 2017 December, Melbourne, Australia, 2017, IEEE 56th Annual Conference on Decision and Control (CDC), 12-15 IEEE, 2017, pp. 1362–67.
Version
Original manuscript
Abstract
In this paper, we focus on applications in machine learning, optimization, and control that call for the resilient selection of a few elements, e.g. features, sensors, or leaders, against a number of adversarial denial-of-service attacks or failures. In general, such resilient optimization problems are hard, and cannot be solved exactly in polynomial time, even though they often involve objective functions that are monotone and submodular. Notwithstanding, in this paper we provide the first scalable algorithm for their approximate solution, that is valid for any number of attacks or failures, and which, for functions with low curvature, guarantees superior approximation performance. Notably, the curvature has been known to tighten approximations for several non-resilient maximization problems, yet its effect on resilient maximization had hitherto been unknown. We complement our theoretical analyses with supporting empirical evaluations.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
MIT Sociotechnical Systems Research Center
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/CDC.2017.8263844
