Data-Driven Chance Constrained Programs over Wasserstein Balls

@article{Chen2018DataDrivenCC,
  title={Data-Driven Chance Constrained Programs over Wasserstein Balls},
  author={Zhi Chen and Daniel Kuhn and Wolfram Wiesemann},
  journal={Operations Research},
  year={2018}
}
In the era of modern business analytics, data-driven optimization has emerged as a popular modeling paradigm to transform data into decisions. By constructing an ambiguity set of the potential data-generating distributions and subsequently hedging against all member distributions within this ambiguity set, data-driven optimization effectively combats the ambiguity with which real-life data sets are plagued. Chen et al. (2022) study data-driven, chance-constrained programs in which a decision… 

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