Distribution-robust loss-averse optimization

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• 作者：Kyungchul Park ; Kyungsik Lee
• 关键词：Distribution ; robust ; Loss ; averse ; Stochastic programming ; Robust optimization
• 刊名：Optimization Letters
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：11
• 期：1
• 页码：153-163
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Optimization; Operation Research/Decision Theory; Computational Intelligence; Numerical and Computational Physics, Simulation;
• 出版者：Springer Berlin Heidelberg
• ISSN：1862-4480
• 卷排序：11

文摘

Distribution-robust loss-averse optimization optimizes a nominal value with some protection against downside loss, under the assumption that only partial information on the underlying distribution is available. We herein present a general modeling framework for the distribution-robust loss-averse optimization problem. We provide an equivalent simpler formulation that usually permits a tractable solution procedure. We then explore the modeling framework’s relations with traditional robust optimization and mean-variance optimization. Additionally, we discuss extensions to stochastic linear programming.