Optimality Conditions in DC-Constrained Mathematical Programming Problems
本文在局部凸空间中，利用凸函数差的结构和广义微分技术，给出了抽象约束数学规划问题的最优性条件，并应用于无限、随机和半定规划。
Abstract This paper provides necessary and sufficient optimality conditions for abstract-constrained mathematical programming problems in locally convex spaces under new qualification conditions. Our approach exploits the geometrical properties of certain mappings, in particular their structure as difference of convex functions, and uses techniques of generalized differentiation (subdifferential and coderivative). It turns out that these tools can be used fruitfully out of the scope of Asplund spaces. Applications to infinite, stochastic and semi-definite programming are developed in separate sections.