- Title
- A sharp augmented Lagrangian-based method in constrained non-convex optimization
- Creator
- Bagirov, Adil; Ozturk, Gurkan; Kasimbeyli, Refail
- Date
- 2019
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/168518
- Identifier
- vital:13828
- Identifier
-
https://doi.org/10.1080/10556788.2018.1496431
- Identifier
- ISBN:1055-6788
- Abstract
- In this paper, a novel sharp Augmented Lagrangian-based global optimization method is developed for solving constrained non-convex optimization problems. The algorithm consists of outer and inner loops. At each inner iteration, the discrete gradient method is applied to minimize the sharp augmented Lagrangian function. Depending on the solution found the algorithm stops or updates the dual variables in the inner loop, or updates the upper or lower bounds by going to the outer loop. The convergence results for the proposed method are presented. The performance of the method is demonstrated using a wide range of nonlinear smooth and non-smooth constrained optimization test problems from the literature.
- Publisher
- Taylor and Francis Ltd.
- Relation
- Optimization Methods and Software Vol. 34, no. 3 (2019), p. 462-488
- Rights
- Copyright © 2018 Informa UK Limited, trading as Taylor & Francis Group.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0102 Applied Mathematics; 0103 Numerical and Computational Mathematics; 0802 Computation Theory and Mathematics; 65K05; 90C25; Constrained optimization; Discrete gradient method; Modified subgradient algorithm; Non-convex optimization; Non-smooth optimization; Sharp augmented Lagrangian
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