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A Limited-Memory Multipoint Symmetric Secant Method for Bound Constrained Optimization |
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| Authors: | Oleg P. Burdakov José Mario Martínez Elvio A. Pilotta |
| Affiliation: | (1) Division of Optimization, Department of Mathematics, Linköping University, S-581 83 Linköping, Sweden;(2) Department of Applied Mathematics, IMECC-UNICAMP, University of Campinas, CP 6065, 13081-970 Campinas SP, Brazil;(3) Facultad de Matemática, Astronomía y Física, FaMAF-CIEM, Universidad Nacional de Córdoba, Ciudad Universitaria, 5000, Argentina |
| Abstract: | A new algorithm for solving smooth large-scale minimization problems with bound constraints is introduced. The way of dealing with active constraints is similar to the one used in some recently introduced quadratic solvers. A limited-memory multipoint symmetric secant method for approximating the Hessian is presented. Positive-definiteness of the Hessian approximation is not enforced. A combination of trust-region and conjugate-gradient approaches is used to explore useful information. Global convergence is proved for a general model algorithm. Results of numerical experiments are presented. |
| Keywords: | large-scale optimization box constraints gradient projection trust region multipoint symmetric secant methods global convergence |
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