Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations

@article{Esfahani2018DatadrivenDR,
  title={Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations},
  author={Peyman Mohajerin Esfahani and Daniel Kuhn},
  journal={Math. Program.},
  year={2018},
  volume={171},
  pages={115-166}
}
We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) probability distributions centered at the uniform distribution on the training samples, and we seek decisions that perform best in view of the worstcase distribution within this Wasserstein ball. The state-of-the-art methods for solving the resulting… CONTINUE READING
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