subdifferential

Aim to study the cone subdifferential of the cone efficient solution sets for set valued vector optimization problem with perturbed order.

目的研究锥扰动集值映射向量优化问题锥有效解的锥次可微性。

In this paper, we prove a theorem on the convergence of the gradient of K-S function to the subdifferential of corresponding maximum function.

本文证明了一条关于K - S函数的梯度收敛于相应的极大值由数的次微分的定理。

And the expression of its subdifferential is developed in the case that the effective domain of the sup-type function is a non-empty convex set.

在极大值函数的有效域为非空凸集的条件下研究了次微分，并给出它的结构表达式。

Finally, the generalized dual model of the problem (VP) is presented with the help of upper subdifferential of function, and a weak duality theorem is given.

接着，利用函数的上次微分构造了不可微向量优化问题（VP）的广义对偶模型，并且在适当的弱凸性条件下建立了弱对偶定理。

The problem of the existence of a cone subdifferential for the cone convex set valued maps in the locally convex, linear and topological vector space is discussed.

在局部凸线性拓扑向量空间讨论了一种锥凸集值映射的锥次微分的存在性问题，证明了几个锥次微分的存在定理。