Robust convex optimization: A new perspective that unifies and extends

Bertsimas, D, den Hertog, D, Pauphilet, J and Zhen, J (2023) Robust convex optimization: A new perspective that unifies and extends. Mathematical Programming, 200 (2). pp. 877-918. ISSN 0025-5610

Abstract

Robust convex constraints are difficult to handle, since finding the worst-case scenario is equivalent to maximizing a convex function. In this paper, we propose a new approach to deal with such constraints that unifies most approaches known in the literature and extends them in a significant way. The extension is either obtaining better solutions than the ones proposed in the literature, or obtaining solutions for classes of problems unaddressed by previous approaches. Our solution is based on an extension of the Reformulation-Linearization-Technique, and can be applied to general convex inequalities and general convex uncertainty sets. It generates a sequence of conservative approximations which can be used to obtain both upper- and lower- bounds for the optimal objective value. We illustrate the numerical benefit of our approach on a robust control and robust geometric optimization example.

More Details

Item Type:	Article
Subject Areas:	Management Science and Operations
Date Deposited:	20 Sep 2022 09:17
Date of first compliant deposit:	10 Aug 2022
Subjects:	Mathematical programming
Last Modified:	12 Nov 2024 16:26
URI:	https://lbsresearch.london.edu/id/eprint/2617

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