Abstract
We discover discrete-time counterpart of the continuous-time saddle point algorithm developed in [1] for solving robust optimization problems. Under the assumption that the cost function is convex in the decision variable and uncertainties enter concavely in the robust optimization problem, we prove global asymptotic convergence of the saddle-point algorithm to the robust optimal solution. The sub-gradient nature of the proposed discrete-time algorithm allows us to handle robust optimization problems with discontinuous cost function and constraint.
Original language | English (US) |
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Title of host publication | 2019 IEEE 58th Conference on Decision and Control, CDC 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2473-2478 |
Number of pages | 6 |
ISBN (Electronic) | 9781728113982 |
DOIs | |
State | Published - Dec 2019 |
Event | 58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France Duration: Dec 11 2019 → Dec 13 2019 |
Publication series
Name | Proceedings of the IEEE Conference on Decision and Control |
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Volume | 2019-December |
ISSN (Print) | 0743-1546 |
ISSN (Electronic) | 2576-2370 |
Conference
Conference | 58th IEEE Conference on Decision and Control, CDC 2019 |
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Country/Territory | France |
City | Nice |
Period | 12/11/19 → 12/13/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.