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Quantitative Stability Analysis for Minimax Distributionally Robust RiskOptimization

Alois Pichler(alois.pichler***at***mathematik.tu-chemnitz.de)
Huifu Xu(H.Xu***at***soton.ac.uk)

Abstract: This paper considers distributionally robust formulations of a two stage stochastic programming problem with the objective of minimizing a distortion risk of the minimal cost incurred at the second stage. We carry out stability analysis by looking into variations of the ambiguity set under the Wasserstein metric, decision spaces at both stages and the support set of the random variables. In the case when it is risk neutral, the stability result is presented with the variation of the ambiguity set being measured by generic metrics ofζ-structure, which provides a unified framework for quantitative stability analysis under various metrics including total variation metric and Kantorovich metric. When the ambiguity set is structured by aζ-ball, we find that the Hausdorff distance between twoζ-balls is bounded by the distance of their centres and difference of their radius. The findings allow us to strengthen some recent convergence results on distributionally robust optimization where the centre of the Wasserstein ball is constructed by the empirical probability distribution.

Keywords: Distortion risk measure, ζ-ball, Wasserstein ball, quantitative stability analysis

Category 1: Stochastic Programming

Category 2: Convex and Nonsmooth Optimization

Citation:

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Entry Submitted: 01/11/2017
Entry Accepted: 01/15/2017
Entry Last Modified: 01/11/2017

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