This paper is concerned with higher-order sensitivity analysis in parametric vector optimization problems. Firstly, higher-order proto-differentiability of a set-valued mapping from one Euclidean space to another is defined. Then, we prove that the perturbation map/the proper perturbation map/the weak perturbation map of a parameterized vector optimization problem are higher-order proto-differentiable under some suitable qualification conditions.

中文翻译：

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关于扰动图的高阶原微分

本文涉及参数向量优化问题中的高阶灵敏度分析。首先，定义了从一个欧几里得空间到另一个欧几里得空间的集值映射的高阶原型可微性。然后，我们证明了在某些合适的限定条件下，参数化向量优化问题的扰动图/适当扰动图/弱扰动图是高阶原型可微的。