Comparing structured ambiguity sets for stochastic optimization: Application to uncertainty quantification

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Abstract

The aim of this paper is to compare two classes of structured ambiguity sets, which are data-driven and can reduce the conservativeness of their associated optimization problems. These two classes of structured sets, coined Wasserstein hyperrectangles and multi-transport hyperrectangles, are explored in their trade-offs in terms of reducing conservativeness and providing tractable reformulations. It follows that multi-transport hyperrectangles lead to tractable optimization problems for a significantly broader range of objective functions under a decent compromise in terms of conservativeness reduction. The results are illustrated in an uncertainty quantification case study.
Original languageEnglish
Title of host publicationProceedings of the IEEE 62nd Conference on Decision and Control (CDC 2023)
PublisherIEEE
Publication statusAccepted/In press - 2023
Event62nd IEEE Conference on Decision and Control - Singapore, Singapore
Duration: 13 Dec 202315 Dec 2023
Conference number: 62

Conference

Conference62nd IEEE Conference on Decision and Control
Abbreviated titleCDC 2023
Country/TerritorySingapore
CitySingapore
Period13/12/2315/12/23

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