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Type: Artigo de periódico
Title: Augmented Lagrangian algorithms based on the spectral projected gradient method for solving nonlinear programming problems
Author: Diniz-Ehrhardt, MA
Gomes-Ruggiero, MA
Martinez, JM
Santos, SA
Abstract: The spectral projected gradient method (SPG) is an algorithm for large-scale bound-constrained optimization introduced recently by Birgin, Martinez, and Raydan. It is based on the Raydan unconstrained generalization of the Barzilai-Borwein method for quadratics. The SPG algorithm turned out to be surprisingly effective for solving many large-scale minimization problems with box constraints. Therefore, it is natural to test its perfomance for solving the subproblems that appear in nonlinear programming methods based on augmented Lagrangians. In this work, augmented Lagrangian methods which use SPG as the underlying convex-constraint solver are introduced (ALSPG) and the methods are tested in two sets of problems. First, a meaningful subset of large-scale nonlinearly constrained problems of the CUTE collection is solved and compared with the perfomance of LANCELOT. Second, a family of location problems in the minimax formulation is solved against the package FFSQP.
Subject: augmented Lagrangian methods
projected gradient methods
nonmonotone line search
large-scale problems
bound-constrained problems
Barzilai-Borwein method
Country: EUA
Editor: Kluwer Academic/plenum Publ
Rights: fechado
Identifier DOI: 10.1007/s10957-004-5720-5
Date Issue: 2004
Appears in Collections:Unicamp - Artigos e Outros Documentos

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