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Frameworks and Results in Distributionally Robust Optimization
2022
Open Journal of Mathematical Optimization
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
doi:10.5802/ojmo.15
fatcat:mekcrfo4fvez3fdjnaietptg6y