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

Conference paper (2023)

Authors

L. Chaouach Team Dimitris Boskos - Mechanical, Maritime and Materials Engineering
T.A.E. Oomen Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
D. Boskos Team Dimitris Boskos - Mechanical, Maritime and Materials Engineering
Research Group
Team Dimitris Boskos (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 L. Chaouach, T.A.E. Oomen, D. Boskos
DOI
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https://doi.org/10.1109/CDC49753.2023.10383381
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Dimitris Boskos

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.