Probability approximation schemes for stochastic programs with distributionally robust second-order dominance constraints

Indexed by:期刊论文

First Author:Guo, Shaoyan

Correspondence Author:Xu, HF (reprint author), Univ Southampton, Sch Math, Southampton SO17 1BJ, Hants, England.; Xu, HF (reprint author), Dalian Univ Technol, Dalian, Peoples R China.

Co-author:Xu, Huifu,Zhang, Liwei

Journal:17th British-French-German Conference on Optimization

Included Journals:SCIE、CPCI-S

Volume:32

Issue:4,SI

Page Number:770-789

ISSN No.:1055-6788

Key Words:second-order dominance; probability discretization; Kantorovich metric; stabilityanalysis

Abstract:Since the pioneering work by Dentcheva and Ruszczyski [Optimization with stochastic dominance constraints, SIAM J. Optim. 14 (2003), pp. 548-566], stochastic programs with second-order dominance constraints (SPSODC) have received extensive discussions over the past decade from theory of optimality to numerical schemes and practical applications. In this paper, we investigate discrete approximation of SPSODC when (a) the true probability is known but continuously distributed and (b) the true probability distribution is unknown but it lies within an ambiguity set of distributions. Differing from the well-known Monte Carlo discretization method, we propose a deterministic discrete approximation scheme due to Pflug and Pichler [Approximations for Probability Distributions and Stochastic Optimization Problems, International Series in Operations Research & Management Science, Vol. 163, Springer, New York, 2011, pp. 343-387] and demonstrate that the discrete probability measure and the ambiguity set of discrete probability measures approximate their continuous counterparts under the Kantorovich metric. Stability analysis of the optimal value and optimal solutions of the resulting discrete optimization problems is presented and some comparative numerical test results are reported.

Date of Publication:2017-01-01