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

• 成果类型: Article
• 署名作者: Birgin, E. G.; Floudas, C. A.; Martinez, J. M.
• 署名单位: Universidade de Sao Paulo; Princeton University; Universidade Estadual de Campinas
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-009-0264-y
• 发表日期: 2010
• 页码: 139-162
• 关键词: optimization algorithm gop; rlt-based approach; alpha-bb; nonconvex nlps; Duality

摘要

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.