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

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

PRONEX-Optimization

PRONEX-Optimization[PRONEX-CNPq/FAPERJ E-26/171.164/2003-APQ1]

FAPESP[06/53768-0 and 06/51827-9]

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

CNPq[PROSUL 490333/2004-4]

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

National Science Foundation (NSF)

National Science Foundation (NSF)

National Institute of Health (NIH)[R01 GM52032]

National Institutes of Health (NIH)