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Primal-Dual Hybrid Gradient Method for Distributionally Robust Optimization Problems

Yongchao Liu(lyc***at***dlut.edu.cn)
Xiaoming Yuan(xmyuan***at***hkbu.edu.hk)
Shangzhi Zeng(15484203***at***life.hkbu.edu.hk)
Jin Zhang(zhangjin***at***hkbu.edu.hk)

Abstract: We focus on the discretization approach to distributionally robust optimization (DRO) problems and propose a numerical scheme originated from the primal-dual hybrid gradient (PDHG) method that recently has been well studied in convex optimization area. Specifically, we consider the cases where the ambiguity set of the discretized DRO model is defined through the moment condition and Wasserstein metric, respectively. Moreover, we apply the PDHG to a portfolio selection problem modelled by DRO and verify its efficiency.

Keywords: Distributionally robust optimization, discretization method, primal-dual hybrid gradient, moment conditions, Wasserstein metric

Category 1: Robust Optimization

Citation: Operations Research Letters

Download: [PDF]

Entry Submitted: 10/02/2017
Entry Accepted: 10/02/2017
Entry Last Modified: 10/02/2017

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