Augmented Lagrangian Algorithms Based on the Spectral Projected Gradient Method for Solving Nonlinear Programming Problems

Autor: M. A. Diniz-Ehrhardt, Márcia A. Gomes-Ruggiero, José Mario Martínez, Sandra A. Santos
Rok vydání: 2004
Předmět:
Zdroj: Journal of Optimization Theory and Applications. 123:497-517
ISSN: 1573-2878
0022-3239
DOI: 10.1007/s10957-004-5720-5
Popis: 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 sub-problems 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.
Databáze: OpenAIRE
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