Robust Optimization via Discrete-Time Saddle Point Algorithm

Keivan Ebrahimi, Umesh Vaidya, Nicola Elia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2473-2478
Number of pages6
ISBN (Electronic)9781728113982
DOIs
StatePublished - Dec 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2019-December
ISSN (Print)0743-1546

Conference

Conference58th IEEE Conference on Decision and Control, CDC 2019
CountryFrance
CityNice
Period12/11/1912/13/19

Fingerprint Dive into the research topics of 'Robust Optimization via Discrete-Time Saddle Point Algorithm'. Together they form a unique fingerprint.

Cite this