模棱两可
圆锥截面
计算机科学
数学优化
集合(抽象数据类型)
经验分布函数
概率分布
球(数学)
整数(计算机科学)
最优化问题
数据集
大数据
数学
算法
数据挖掘
人工智能
统计
几何学
数学分析
程序设计语言
作者
Zhi Chen,Daniel Kühn,Wolfram Wiesemann
出处
期刊:Operations Research
[Institute for Operations Research and the Management Sciences]
日期:2024-01-01
卷期号:72 (1): 410-424
被引量:33
标识
DOI:10.1287/opre.2022.2330
摘要
In the era of modern business analytics, data-driven optimization has emerged as a popular modeling paradigm to transform data into decisions. By constructing an ambiguity set of the potential data-generating distributions and subsequently hedging against all member distributions within this ambiguity set, data-driven optimization effectively combats the ambiguity with which real-life data sets are plagued. Chen et al. (2022) study data-driven, chance-constrained programs in which a decision has to be feasible with high probability under every distribution within a Wasserstein ball centered at the empirical distribution. The authors show that the problem admits an exact deterministic reformulation as a mixed-integer conic program and demonstrate (in numerical experiments) that the reformulation compares favorably to several state-of-the-art data-driven optimization schemes.
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