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Optimal Transport Based Distributionally Robust Optimization: Structural Properties and Iterative Schemes

Jose Blanchet (jose.blanchet***at***stanford.edu)
Karthyek Murthy (karthyek_murthy***at***sutd.edu.sg)
Fan Zhang (fzh***at***stanford.edu)

Abstract: We consider optimal transport based distributionally robust optimization (DRO) problems with locally strongly convex transport cost functions and affine decision rules. Under conventional convexity assumptions on the underlying loss function, we obtain structural results about the value function, the optimal policy, and the worst-case optimal transport adversarial model. These results expose a rich structure embedded in the DRO problem (e.g. strong convexity even if the non-DRO problem was not strongly convex, a suitable scaling of the Lagrangian for the DRO constraint, etc, which are crucial for the design of efficient algorithms). As a consequence of these results, one can develop efficient optimization procedures which have the same sample and iteration complexity as a natural non-DRO benchmark algorithm such as stochastic gradient descent.

Keywords: Distributionally Robust Optimization, Stochastic Gradient Descent, Optimal Transport, Wasserstein distances, Adversarial, Strong convexity, Comparative statics, Rate of convergence

Category 1: Stochastic Programming

Category 2: Robust Optimization

Citation:

Download: [PDF]

Entry Submitted: 10/04/2018
Entry Accepted: 10/05/2018
Entry Last Modified: 06/06/2020

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