The reformulation-based αGO algorithm for solving nonconvex MINLP problems - some improvements

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    The alpha-reformulation (alpha R) technique can be used to transform any nonconvex twice-differentiable mixed-integer nonlinear programming problem to a convex relaxed form. By adding a quadratic function to the nonconvex function it is possible to convexify it, and by subtracting a piecewise linearization of the added function a convex underestimator will be obtained. This reformulation technique is implemented in the a global optimization (alpha GO) algorithm solving the specified problem type to global optimality as a sequence of reformulated subproblems where the piecewise linear functions are refined in each step. The tightness of the underestimator has a large impact on the efficiency of the solution process, and in this paper it is shown how it is possible to reduce the approximation error by utilizing a piecewise quadratic spline function defined on smaller subintervals. The improved underestimator is also applied to test problems illustrating its performance.
    Titel på värdpublikation11th International Conference on Chemical and Process Engineering - selected papers of ICheaP11
    RedaktörerSauro Pierucci, Jiří J. Klemeš
    FörlagAssociazione Italiana di Ingegneria Chimica
    Antal sidor6
    ISBN (tryckt)978-88-95608-23-5
    StatusPublicerad - 2013
    MoE-publikationstypA4 Artikel i en konferenspublikation
    EvenemangInternational Conference on Chemical and Process Engineering (ICheaP) - 11th International Conference on Chemical and Process Engineering (ICheaP)
    Varaktighet: 2 juni 20135 juni 2013


    KonferensInternational Conference on Chemical and Process Engineering (ICheaP)

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