Canonical duality for solving general nonconvex constrained problems

被引：0
|
作者：
Vittorio Latorre
David Yang Gao
机构：
[1] University of Rome Sapienza,Department of Computer Control and Management Engineering
[2] Federation University Australia,School of Science Information Technology and Engineering
来源：
Optimization Letters | 2016年 / 10卷
关键词：
Global optimization; Nonlinear constrained programming ; Augmented Lagrangian;
D O I：
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学科分类号：
摘要：
This paper presents a canonical duality theory for solving a general nonconvex constrained optimization problem within a unified framework to cover Lagrange multiplier method and KKT theory. It is proved that if both target function and constraints possess certain patterns necessary for modeling real systems, a perfect dual problem (without duality gap) can be obtained in a unified form with global optimality conditions provided.While the popular augmented Lagrangian method may produce more difficult nonconvex problems due to the nonlinearity of constraints. Some fundamental concepts such as the objectivity and Lagrangian in nonlinear programming are addressed.
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页码：1763 / 1779
页数：16
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