Distributionally Robust Optimization: A Review
Hamed Rahimian (hamed.rahimiannorthwestern.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.
Entry Submitted: 08/12/2019
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