  


A DataDriven Distributionally Robust Bound on the Expected Optimal Value of Uncertain Mixed 01 Linear Programming
Guanglin Xu(guanglinxuuiowa.edu) Abstract: This paper studies the expected optimal value of a mixed 01 programming problem with uncertain objective coefficients following a joint distribution. We assume that the true distribution is not known exactly, but a set of independent samples can be observed. Using the Wasserstein metric, we construct an ambiguity set centered at the empirical distribution from the observed samples and containing the true distribution with a high statistical guarantee. The problem of interest is to investigate the bound on the expected optimal value over the Wasserstein ambiguity set. Under standard assumptions, we reformulate the problem into a copositive program, which naturally leads to a tractable semidefinitebased approximation. We compare our approach with a momentbased approach from the literature on three applications. Numerical results illustrate the effectiveness of our approach. Keywords: Distributionally robust optimization; Wasserstein metric; copositive programming; semidefinite programming Category 1: Robust Optimization Category 2: Linear, Cone and Semidefinite Programming (Semidefinite Programming ) Category 3: Stochastic Programming Citation: Download: [PDF] Entry Submitted: 08/24/2017 Modify/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  