A limited-memory multipoint symmetric secant method for bound constrained optimization
2002 (English)In: Annals of Operations Research, ISSN 0254-5330, Vol. 117, no 1-4, 51-70 p.Article in journal (Refereed) Published
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 a useful negative curvature information. Global convergence is proved for a general model algorithm. Results of numerical experiments are presented.
Place, publisher, year, edition, pages
2002. Vol. 117, no 1-4, 51-70 p.
large-scale optimization, box constraints, gradient projection, trust region, multipoint symmetric secant methods, global convergence
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-48712OAI: oai:DiVA.org:liu-48712DiVA: diva2:269608