| dc.contributor |
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
|
| dc.contributor |
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems |
|
| dc.contributor |
MIT Sociotechnical Systems Research Center |
|
| dc.contributor |
Jadbabaie-Moghadam, Ali |
|
| dc.creator |
Tzoumas, Vasileios |
|
| dc.creator |
Gatsis, Konstantinos |
|
| dc.creator |
Jadbabaie, Ali |
|
| dc.creator |
Pappas, George J. |
|
| dc.creator |
Jadbabaie-Moghadam, Ali |
|
| dc.date |
2018-09-11T19:30:02Z |
|
| dc.date |
2018-09-11T19:30:02Z |
|
| dc.date |
2018-01 |
|
| dc.date |
2017-12 |
|
| dc.date |
2018-08-16T16:46:13Z |
|
| dc.date.accessioned |
2023-03-01T18:07:39Z |
|
| dc.date.available |
2023-03-01T18:07:39Z |
|
| dc.identifier |
978-1-5090-2873-3 |
|
| dc.identifier |
http://hdl.handle.net/1721.1/117723 |
|
| dc.identifier |
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. |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/278850 |
|
| dc.description |
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. |
|
| dc.format |
application/pdf |
|
| dc.publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
|
| dc.relation |
http://dx.doi.org/10.1109/CDC.2017.8263844 |
|
| dc.relation |
2017 IEEE 56th Annual Conference on Decision and Control (CDC) |
|
| dc.rights |
Creative Commons Attribution-Noncommercial-Share Alike |
|
| dc.rights |
http://creativecommons.org/licenses/by-nc-sa/4.0/ |
|
| dc.source |
arXiv |
|
| dc.title |
Resilient monotone submodular function maximization |
|
| dc.type |
Article |
|
| dc.type |
http://purl.org/eprint/type/ConferencePaper |
|