Journal
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
Volume 123, Issue 3, Pages 497-517Publisher
KLUWER ACADEMIC/PLENUM PUBL
DOI: 10.1007/s10957-004-5720-5
Keywords
augmented Lagrangian methods; projected gradient methods; nonmonotone line search; large-scale problems; bound-constrained problems; Barzilai-Borwein method
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available