Sensitivity to uncertainty and scalarization in robust multiobjective optimization: an overview with application to mean-variance portfolio optimization
本文综述了鲁棒多目标优化中关于线性标量化和最优值对不确定性集变化的敏感性结果，并证明了最优解对不确定性集变化的敏感性，最后应用于均值-方差投资组合优化。
Abstract Robust optimization is proving to be a fruitful tool to study problems with uncertain data. In this paper we deal with the minmax aproach to robust multiobjective optimization. We survey the main features of this problem with particular reference to results concerning linear scalarization and sensitivity of optimal values with respect to changes in the uncertainty set. Furthermore we prove results concerning sensitivity of optimal solutions with respect to changes in the uncertainty set. Finally we apply the presented results to mean-variance portfolio optimization.