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A limited-memory mu...
A limited-memory multipoint symmetric secant method for bound constrained optimization
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- Burdakov, Oleg (författare)
- Linköpings universitet,Optimeringslära,Tekniska fakulteten
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- Martinez, JM (författare)
- 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
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- Pilotta, EA (författare)
- 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
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(creator_code:org_t)
- 2002
- 2002
- Engelska.
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Ingår i: Annals of Operations Research. - 0254-5330 .- 1572-9338. ; 117:1-4, s. 51-70
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Abstract
Ämnesord
Stäng
- 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.
Nyckelord
- large-scale optimization
- box constraints
- gradient projection
- trust region
- multipoint symmetric secant methods
- global convergence
- TECHNOLOGY
- TEKNIKVETENSKAP
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