A primal-dual trust-region algorithm for non-convex nonlinear programming

Andy Conn, Nick Gould, Dominique Orban, Philippe Toint

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    A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Numerical results are presented for general quadratic programs.
    Original languageEnglish
    Pages (from-to)215-249
    Number of pages35
    JournalMathematical Programming Series B
    Issue number2
    Publication statusPublished - 1 Apr 2000


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