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Global minimization using an Augmented Lagrangian method with variable lower-level constraints

Ernesto G. Birgin(egbirgin***at***ime.usp.br)
Christodoulos A. Floudas(floudas***at***titan.princeton.edu)
José Mario Martínez(martinez***at***ime.unicamp.br)

Abstract: A novel global optimization method based on an Augmented Lagrangian framework is introduced for continuous constrained nonlinear optimization problems. At each outer iteration the method requires the $\varepsilon$-global minimization of the Augmented Lagrangian with simple constraints. Global convergence to an $\varepsilon$-global minimizer of the original problem is proved. The subproblems are solved using the $\alpha$BB method. Numerical experiments are presented.

Keywords: Deterministic global optimization, Augmented Lagrangians, nonlinear programming, algorithms, numerical experiments.

Category 1: Global Optimization


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Entry Submitted: 12/12/2006
Entry Accepted: 12/12/2006
Entry Last Modified: 12/12/2006

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