报告人：童小娇 (湘潭大学/湖南第一师范学院)
报告题目：Discretization and Quantification for Distributionally Robust Optimization with Decision-Dependent Ambiguity Sets
摘要：In this talk, we discuss the discrete approximation and the quantitative analysis for a class of the distributionally robust optimization problems with decision dependent ambiguity sets. We establish the local Lipschitz continuity of the decision dependent ambiguity set, measured by the Hausdorff distance. We then employ Lagrange duality and first-order growth conditions to derive quantitative analysis for the optimal value and optimal solution. We also examine the application of a classical and widely-used ambiguity set within the theoretical framework. Finally, we conduct experiments to demonstrate the computational times and variations in the optimal value.
