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Distributionally Robust Optimization: A Review

Hamed Rahimian (hamed.rahimian***at***northwestern.edu)
Sanjay Mehrotra (mehrotra***at***northwestern.edu)

Abstract: The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. 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 its relationships with robust optimization, risk-aversion, chance-constrained optimization, and function regularization.

Keywords: Distributionally Robust Optimization; Robust Optimization; Stochastic Optimization; Risk-Averse Optimization; Chance-Constrained Optimization; Statistical Learning

Category 1: Stochastic Programming

Category 2: Robust Optimization

Citation: Manuscript, submitted for publication.

Download: [PDF]

Entry Submitted: 08/12/2019
Entry Accepted: 08/12/2019
Entry Last Modified: 08/13/2019

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