Global convergence of a class of trust region algorithms for optimization with simple bounds

Andy Conn, Nick Gould, Philippe Toint

    Résultats de recherche: Contribution à un journal/une revueArticleRevue par des pairs

    Résumé

    This paper extends the known excellent global convergence properties of trust-region algorithms for unconstrained optimization to the case where bounds on the variables are present. Weak conditions on the accuracy of the Hessian approximations are considered. It is also shown that, when the strict complementarity condition holds, the proposed algorithms reduce to an unconstrained calculation after finitely many iterations, allowing a fast rate of convergence.
    langue originaleAnglais
    Pages (de - à)430-460
    Nombre de pages31
    journalSIAM Journal on Numerical Analysis
    Volume25
    Numéro de publication182
    Etat de la publicationPublié - 1988

    Empreinte digitale Examiner les sujets de recherche de « Global convergence of a class of trust region algorithms for optimization with simple bounds ». Ensemble, ils forment une empreinte digitale unique.

    Contient cette citation