Generalized Augmented Lagrangian Problem and Approximate Optimal Solutions in Nonlinear Programming
Zhe Chen, Kequan Zhao, Yuke Chen
Abstract
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Zhe Chen, Kequan Zhao, Yuke Chen
Abstract
Open-access reader
We introduce some approximate optimal solutions and a generalized augmented Lagrangian in nonlinear programming, establish dual function and dual problem based on the generalized augmented Lagrangian, obtain approximate KKT necessary optimality condition of the generalized augmented Lagrangian dual problem, prove that the approximate stationary points of generalized augmented Lagrangian problem converge to that of the original problem. Our results improve and generalize some known results.
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We introduce some approximate optimal solutions and a generalized augmented Lagrangian in nonlinear programming, establish dual function and dual problem based on the generalized augmented Lagrangian, obtain approximate KKT necessary optimality condition of the generalized augmented Lagrangian dual problem, prove that the approximate stationary points of generalized augmented Lagrangian problem converge to that of the original problem. Our results improve and generalize some known results.
Key concepts: Augmented Lagrangian method, Karush–Kuhn–Tucker conditions, Lagrangian, Lagrangian relaxation, Mathematics, Mathematical optimization, Nonlinear system, Nonlinear programming