讲座题目：Globalized Distributionally Robust Counterpart
报告人： 刘丰 中国科学院大学经济与管理学院
讲座时间：2023年4月28日(周五) 12:15-13:30
讲座地点：中国科学院大学中关村校区教学楼S406
腾讯会议 ID：900-463-701
内容摘要
We extend the notion of globalized robustness to consider distributional information beyond the support of the ambiguous probability distribution. We propose the globalized distributionally robust counterpart that disallows any (resp., allows limited) constraint violation for distributions residing (resp., not residing) in the ambiguity set. By varying its inputs, our proposal recovers several existing perceptions of parameter uncertainty. Focusing on the type-1 Wasserstein distance, we show that the globalized distributionally robust counterpart has an insightful interpretation in terms of shadow price of globalized robustness, and it can be seamlessly integrated with many popular optimization models under uncertainty without incurring any extra computational cost. Such computational attractiveness also holds for other ambiguity sets, including the ones based on probability metric, optimal transport, phi-divergences, or moment conditions, as well as the event-wise ambiguity set. Numerical studies on an adaptive network lot-sizing problem demonstrate the modeling flexibility of our proposal and its emphases on globalized robustness to constraint violation.
主讲人简介
刘丰，中国科学院大学经济与管理学院2021级硕士研究生，研究方向为鲁棒优化及其应用，目前以第一作者身份在UT-Dallas 24期刊INFORMS Journal on Computing上发表论文一篇。
