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

Andy Conn, Nick Gould, Philippe Toint

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Pages (from-to)430-460
    Number of pages31
    JournalSIAM Journal on Numerical Analysis
    Issue number182
    Publication statusPublished - 1988

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