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dc.contributor.authorTzoumas, V
dc.contributor.authorJadbabaie, A
dc.contributor.authorPappas, GJ
dc.date.accessioned2023-03-17T16:46:09Z
dc.date.available2023-03-17T16:46:09Z
dc.date.issued2020-01-01
dc.identifier.urihttps://hdl.handle.net/1721.1/148601
dc.description.abstractIEEE Emerging applications of control, estimation, and machine learning pose resource constraints that limit which of the available sensors, actuators, or data can be simultaneously used. Therefore, researchers have proposed solutions within discrete optimization frameworks where the optimization is performed over finite sets. In this paper, we consider such problems but in adversarial environments, where in every step a number of the chosen elements in the optimization is removed due to failures/attacks. Specifically, we consider for the first time a sequential version of the problem that allows us to observe the failures and adapt, while the attacker also adapts to our response. We call the novel problem Robust Sequential submodular Maximization (RSM). Generally, the problem is computationally hard and no scalable algorithm is known for its solution. In this paper, we propose Robust and Adaptive Maximization (RAM), the first scalable algorithm. RAM adapts in every step to the history of failures. Also, it guarantees a near-optimal performance. Finally, we demonstrate RAM's near-optimality in simulations across various application scenarios, along with its robustness against several failure types, from worst-case to random.en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/TAC.2020.3046222en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleRobust and Adaptive Sequential Submodular Optimizationen_US
dc.typeArticleen_US
dc.identifier.citationTzoumas, V, Jadbabaie, A and Pappas, GJ. 2020. "Robust and Adaptive Sequential Submodular Optimization." IEEE Transactions on Automatic Control, 67 (1).
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineeringen_US
dc.relation.journalIEEE Transactions on Automatic Controlen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-03-17T16:42:32Z
dspace.orderedauthorsTzoumas, V; Jadbabaie, A; Pappas, GJen_US
dspace.date.submission2023-03-17T16:42:34Z
mit.journal.volume67en_US
mit.journal.issue1en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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