Data-Driven Chance Constrained Optimization under Wasserstein Ambiguity Sets

@article{Hota2019DataDrivenCC,
  title={Data-Driven Chance Constrained Optimization under Wasserstein Ambiguity Sets},
  author={A. Hota and A. Cherukuri and J. Lygeros},
  journal={2019 American Control Conference (ACC)},
  year={2019},
  pages={1501-1506}
}
We present a data-driven approach for distri-butionally robust chance constrained optimization problems (DRCCPs). We consider the case where the decision maker has access to a finite number of samples or realizations of the uncertainty. The chance constraint is then required to hold for all distributions that are close to the empirical distribution constructed from the samples (where the distance between two distributions is defined via the Wasserstein metric). We first reformulate DRCCPs under… Expand
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