On a Modified Subgradient Algorithm for Dual Problems via Sharp Augmented Lagrangian

We study convergence properties of a modified subgradient algorithm, applied to the dual problem defined by the sharp augmented Lagrangian. The primal problem we consider is nonconvex and nondifferentiable, with equality constraints. We obtain primal and dual convergence results, as well as a condit...

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Bibliographic Details
Published in:Journal of global optimization 2006-01, Vol.34 (1), p.55
Main Authors: Burachik, Regina S, Gasimov, Rafail N, Ismayilova, Nergiz A, C. Yalçin. Kaya
Format: Article
Language:English
Subjects:
Algorithms
Industrial engineering
Integer programming
Mathematics
Operations research
Optimization
Optimization algorithms
Optimization techniques
Theorems
Online Access:Get full text
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Summary:We study convergence properties of a modified subgradient algorithm, applied to the dual problem defined by the sharp augmented Lagrangian. The primal problem we consider is nonconvex and nondifferentiable, with equality constraints. We obtain primal and dual convergence results, as well as a condition for existence of a dual solution. Using a practical selection of the step-size parameters, we demonstrate the algorithm and its advantages on test problems, including an integer programming and an optimal control problem. [PUBLICATION ABSTRACT]
ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-005-3270-5