Actas de congresos
A Limited-memory Multipoint Symmetric Secant Method For Bound Constrained Optimization
Registro en:
Annals Of Operations Research. , v. 117, n. 1-4, p. 51 - 70, 2002.
2545330
10.1023/A:1021561204463
2-s2.0-1642543147
Autor
Burdakov O.P.
Martinez J.M.
Pilotta E.A.
Institución
Resumen
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. 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