- Distributionally Robust Stochastic Programming Alexander Shapiro (ashapiroisye.gatech.edu) Abstract: In this paper we study distributionally robust stochastic programming in a setting where there is a specified reference probability measure and the uncertainty set of probability measures consists of measures in some sense close to the reference measure. We discuss law invariance of the associated worst case functional and consider two basic constructions of such uncertainty sets. Finally we illustrate some implications of the property of law invariance. Keywords: Coherent risk measures, law invariance, Wasserstein distance, $\psi$-divergence, sample average approximation, chance constraints Category 1: Stochastic Programming Category 2: Robust Optimization Citation: Download: [PDF]Entry Submitted: 12/09/2015Entry Accepted: 12/09/2015Entry Last Modified: 06/11/2017Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.