- Improving sample average approximation using distributional robustness E.J. Anderson (edward.andersonsydney.edu.au) A.B. Philpott (a.philpottauckland.ac.nz) Abstract: We consider stochastic optimization problems in which we aim to minimize the expected value of an objective function with respect to an unknown distribution of random parameters. We analyse the out-of-sample performance of solutions obtained by solving a distributionally robust version of the sample average approximation problem for unconstrained quadratic problems, and derive conditions under which these solutions are improved in comparison with those of the sample average approximation. We compare different mechanisms for constructing a robust solution: phi-divergence using both total variation and standard smooth $\phi$ functions; a CVaR-based risk measure; and a Wasserstein metric. Keywords: sample average approximation, distributional robustness, out-of-sample performance Category 1: Stochastic Programming Citation: Download: [PDF]Entry Submitted: 10/03/2019Entry Accepted: 10/04/2019Entry Last Modified: 10/04/2019Modify/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.