Derivative-free methods for nonlinear programming with general lower-level constraints

作     者：Diniz-Ehrhardt, M. A. Martinez, J. M. Pedroso, L. G. 

作者机构：Univ Estadual Campinas Dept Appl Math IMECC UNICAMP BR-13083859 Campinas SP Brazil Univ Fed Parana Dept Math BR-81531980 Curitiba Parana Brazil 

出 版 物：《COMPUTATIONAL & APPLIED MATHEMATICS》 (Comput. Appl. Math.)

年 卷 期：2011年第30卷第1期

页      面：19-52页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：PRONEX-Optimization [PRONEX - CNPq/FAPERJ E-26/171.510/2006 - APQ1] FAPESP [2006/53768-0, 2004/15635-2] CNPq Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [06/53768-0, 04/15635-2] Funding Source: FAPESP 

主　　题：nonlinear programming Augmented Lagrangian global convergence optimality conditions derivative-free optimization constraint qualifications 

摘      要：Augmented Lagrangian methods for derivative-free continuous optimization with constraints are introduced in this paper. The algorithms inherit the convergence results obtained by Andreani, Birgin, Martinez and Schuverdt for the case in which analytic derivatives exist and are available. In particular, feasible limit points satisfy KKT conditions under the Constant Positive Linear Dependence (CPLD) constraint qualification. The form of our main algorithm allows us to employ well established derivative-free subalgorithms for solving lower-level constrained subproblems. Numerical experiments are presented.