Frameworks and Results in Distributionally Robust Optimization
Open Journal of Mathematical Optimization, Volume 3 (2022), article no. 4, 85 p.

The concepts of risk aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. The statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions to DRO, and relationships with robust optimization, risk aversion, chance-constrained optimization, and function regularization. Various approaches to model the distributional ambiguity and their calibrations are discussed. The paper also describes the main solution techniques used to the solve the resulting optimization problems.

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DOI: 10.5802/ojmo.15
Classification: 90C15,  90C22,  90C25,  90C30,  90C34,  90C90,  68T37,  68T05
Keywords: Distributionally robust optimization; Robust optimization; Stochastic optimization; Risk-averse optimization; Chance-constrained optimization; Statistical learning
Hamed Rahimian 1; Sanjay Mehrotra 2

1 Department of Industrial Engineering, Clemson University, Clemson, SC 29634, USA
2 Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL 60208, USA
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights
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Hamed Rahimian; Sanjay Mehrotra. Frameworks and Results in Distributionally Robust Optimization. Open Journal of Mathematical Optimization, Volume 3 (2022), article  no. 4, 85 p. doi : 10.5802/ojmo.15. https://ojmo.centre-mersenne.org/articles/10.5802/ojmo.15/

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