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A limited-memory multipoint symmetric secant method for bound constrained optimization
Linköping University, Department of Mathematics, Optimization . Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-1836-4200
Linkoping Univ, Dept Math, Div Optimizat, S-58183 Linkoping, Sweden Univ Campinas, UNICAMP, IMECC, Dept Appl Math, BR-13081970 Campinas, SP, Brazil Univ Nacl Cordoba, CIEM, Fac Matemat Astron & Fis, RA-5000 Cordoba, Argentina.
Linkoping Univ, Dept Math, Div Optimizat, S-58183 Linkoping, Sweden Univ Campinas, UNICAMP, IMECC, Dept Appl Math, BR-13081970 Campinas, SP, Brazil Univ Nacl Cordoba, CIEM, Fac Matemat Astron & Fis, RA-5000 Cordoba, Argentina.
2002 (English)In: Annals of Operations Research, ISSN 0254-5330, E-ISSN 1572-9338, Vol. 117, no 1-4, 51-70 p.Article in journal (Refereed) Published
Abstract [en]

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.
Keyword [en]
large-scale optimization, box constraints, gradient projection, trust region, multipoint symmetric secant methods, global convergence
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-48712DOI: 10.1023/A:1021561204463OAI: oai:DiVA.org:liu-48712DiVA: diva2:269608
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-02-13Bibliographically approved

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Burdakov, Oleg

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