| Issue |
RAIRO-Oper. Res.
Volume 55, Number 4, July-August 2021
|
|
|---|---|---|
| Page(s) | 2181 - 2188 | |
| DOI | https://doi.org/10.1051/ro/2021095 | |
| Published online | 14 July 2021 | |
Robust duality for generalized convex nonsmooth vector programs with uncertain data in constraints
1
Department of Mathematics and Statistics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
2
School of Mathematics, Thapar Institute of Engineering and Technology, Patiala, India
* Corresponding author: arshpreetkaurdr@gmail.com
Received:
5
November
2020
Accepted:
21
June
2021
Robust optimization has come out to be a potent approach to study mathematical problems with data uncertainty. We use robust optimization to study a nonsmooth nonconvex mathematical program over cones with data uncertainty containing generalized convex functions. We study sufficient optimality conditions for the problem. Then we construct its robust dual problem and provide appropriate duality theorems which show the relation between uncertainty problems and their corresponding robust dual problems.
Mathematics Subject Classification: 49J52 / 90C26 / 90C30 / 90C46
Key words: Robust nonsmooth optimization / robust duality / generalized convexity
© The authors. Published by EDP Sciences, ROADEF, SMAI 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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