Numerical methods for large-scale non-convex quadratic programming

Nick Gould, Philippe Toint

    Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceChapitre

    Résumé

    We consider numerical methods for finding (weak) second-order critical points for large-scale non-convex quadratic programming problems. We describe two new methods. The first is of the active-set variety. Although convergent from any starting point, it is intended primarily for the case where a good estimate of the optimal active set can be predicted. The second is an interior-point trust-region type, and has proved capable of solving problems involving up to half a million unknowns and constraints. The solution of a key equality constrained subproblem, common to both methods, is described. The results of comparative tests on a large set of convex and non-convex quadratic programming examples are given.
    langue originaleAnglais
    titreTrends in Industrial and Applied Mathematics
    rédacteurs en chefA Siddiqi, M Kocvara
    Lieu de publicationDordrecht (NL)
    EditeurKluwer academic
    Pages149-179
    Nombre de pages31
    Etat de la publicationPublié - 2002

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