- Global minimization using an Augmented Lagrangian method with variable lower-level constraints Ernesto G. Birgin(egbirginime.usp.br) Christodoulos A. Floudas(floudastitan.princeton.edu) José Mario Martínez(martinezime.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 Citation: Download: [PDF]Entry Submitted: 12/12/2006Entry Accepted: 12/12/2006Entry Last Modified: 12/12/2006Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Programming Society and by the Optimization Technology Center.