On the Convergence of a Filter-SQP Algorithm

Roger Fletcher, Sven Leyffer, Philippe Toint

    Research output: Book/Report/JournalOther report


    A mechanism for proving gobal convergence in filter-type methods for nonlinear programming is described. Such methods are characterized by their use of the dominance concept of multi-objective optimization, instead of a penalty paremeter 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.
    Original languageEnglish
    Place of PublicationNamur
    PublisherFUNDP, Faculté des Sciences. Département de Mathématique.
    Publication statusPublished - 2000

    Publication series

    NameTechnical report
    PublisherDepartment of MAthematics, University of Namur


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