On weak conjugacy, augmented Lagrangians and duality in nonconvex optimization

被引：0
作者：
Gulcin Dinc Yalcin
Refail Kasimbeyli
机构：
[1] Eskisehir Technical University,Department of Industrial Engineering, Faculty of Engineering
来源：
Mathematical Methods of Operations Research | 2020年 / 92卷
关键词：
Weak conjugacy; Augmented Lagrangians; Weak subdifferential; Nonconvex analysis; Duality; 90C26; 90C30; 90C46;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, zero duality gap conditions in nonconvex optimization are investigated. It is considered that dual problems can be constructed with respect to the weak conjugate functions, and/or directly by using an augmented Lagrangian formulation. Both of these approaches and the related strong duality theorems are studied and compared in this paper. By using the weak conjugate functions approach, special cases related to the optimization problems with equality and inequality constraints are studied and the zero duality gap conditions in terms of objective and constraint functions, are established. Illustrative examples are provided.
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页码：199 / 228
页数：29
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