A Limited-Memory Multipoint Symmetric Secant Method for Bound Constrained Optimization

Oleg Burdakov (), José Martínez () and Elvio Pilotta ()

Annals of Operations Research, 2002, vol. 117, issue 1, 70 pages

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. Copyright Kluwer Academic Publishers 2002

Keywords: large-scale optimization; box constraints; gradient projection; trust region; multipoint symmetric secant methods; global convergence (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1023/A:1021561204463

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