On the global convergence of a filter-SQP algorithm

Roger Fletcher, Sven Leyffer, Philippe Toint

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    Résumé

    A mechanism for proving global convergence in SQP-filter methods for nonlinear programming (NLP) is described. Such methods are characterized by their use of the dominance concept of multiobjective optimization, instead of a penalty parameter whose adjustment can be problematic. The main point of interest is to demonstrate how convergence for NLP can be induced without forcing sufficient descent in a penalty-type merit function. The proof relates to a prototypical algorithm, within which is allowed a range of specific algorithm choices associated with the Hessian matrix representation, updating the trust region radius, and feasibility restoration.
    langue originaleAnglais
    Pages (de - à)44-59
    Nombre de pages16
    journalSIAM Journal on Optimization
    Volume13
    Numéro de publication1
    Les DOIs
    Etat de la publicationPublié - 1 janv. 2003

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