学术报告

【online】Globalized Distributionally Robust Optimization Based on Samples

发布人:发布时间: 2022-06-17

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题目:Globalized Distributionally Robust Optimization Based on Samples


报告人:邢文训 教授 (清华大学)


方式:腾讯会议  ID501-314-599


时间:2022062219:00-20:00


摘要:It is known that the set of perturbed data is key in robust optimization (RO) modelling. Distributionally robust optimization (DRO) is a methodology used for optimization problems affected by random parameters with uncertain probability distribution. In terms of the information of the perturbed data, it is essential to choose an appropriate support set of the probability distribution in formulating DRO models. In this paper, we introduce two globalized distributionally robust optimization (GDRO) models which choose a core set based on data and a sample space containing the core set to balance the degree of robustness and conservatism at the same time. The degree of conservatism can be controlled by the expected distance of random parameters from the core set. Under some assumptions, we further reformulate several GDRO models into tractable semi-definite programs. In addition, numerical experiments are provided showing the relationship between the optimal values of the DRO/GDRO models and the size of the sample spaces.

 

报告人简介:邢文训,清华大学数学科学系教授、博士生导师,北京大学理学学士,清华大学理学博士。目前研究兴趣为非凸/非光滑全局最优化及组合最优化问题,在国内外学术刊物SIAM Journal on Optimization, European Journal of Operational Research, IIE Transactions, Discrete Applied Mathematics, Annals of Operations Research等发表论文60余篇,出版专著1部,教材7部。2007年获得国防科工委国防科学技术进步奖(一等),2008年获国家科学技术进步奖(二等),2001年获中国运筹学会运筹学应用奖(二等)。目前为中国运筹学会监事,数学规划分会常务理事,JIMOJORSC编委等。

 

邀请人:杨俊锋 老师