From Data to Decisions: Distributionally Robust Optimization Is Optimal

From Data to Decisions: Distributionally Robust Optimization Is Optimal

<jats:p> We study stochastic programs where the decision maker cannot observe the distribution of the exogenous uncertainties but has access to a finite set of independent samples from this distribution. In this setting, the goal is to find a procedure that transforms the data to an estimate o...

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Bibliographic Details
Main Authors: Van Parys, Bart PG, Esfahani, Peyman Mohajerin, Kuhn, Daniel
Other Authors: Massachusetts Institute of Technology. Operations Research Center
Format: Article
Language:English
Published: Institute for Operations Research and the Management Sciences (INFORMS) 2022
Online Access:https://hdl.handle.net/1721.1/144252

Internet

https://hdl.handle.net/1721.1/144252

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