On the global convergence of a filter-SQP algorithm

Roger Fletcher, Sven Leyffer, Philippe L. Toint

    Research output: Contribution to journalArticlepeer-review

    271 Citations (Scopus)


    A mechanism for proving global convergence in SQP--filter methods for nonlinear programming (NLP) is described. Such methods are characterized by their use of thedominance 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.
    Original languageEnglish
    Pages (from-to)44-59
    Number of pages16
    JournalSIAM Journal on Optimization
    Issue number1
    Publication statusPublished - 2002


    • Nonlinear programming
    • Global convergence
    • Filter
    • Multiobjective optimization
    • SQP


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