ABSTRACT

In this paper, we investigate robust necessary optimality for scalar uncertain optimization problems under the strictly robust counterpart, where the uncertainties are included in the objective and the constraints. Based on generalized differentiable assumptions, some robust necessary conditions via a separation scheme are derived. Besides, by means of strong separation functions, several characterizations of robust necessary optimality can be established. Finally, an example is given to show the effectiveness of the results derived in this paper.

中文翻译：

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非光滑鲁棒优化问题的必要最优性条件

摘要

在本文中，我们研究了严格鲁棒对应物下标量不确定优化问题的鲁棒必要最优性，其中不确定性包含在目标和约束中。基于广义可微假设，通过分离方案导出了一些稳健的必要条件。此外，通过强大的分离函数，可以建立鲁棒必要最优性的几个表征。最后给出一个例子来说明本文推导结果的有效性。