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

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    Sammanfattning

    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.
    OriginalspråkOdefinierat/okänt
    Titel på gästpublikation11th International Conference on Chemical and Process Engineering - selected papers of ICheaP11
    RedaktörerSauro Pierucci, Jiří J. Klemeš
    FörlagAssociazione Italiana di Ingegneria Chimica
    Sidor1321–1326
    Antal sidor6
    ISBN (tryckt)978-88-95608-23-5
    DOI
    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 jun 20135 jun 2013

    Konferens

    KonferensInternational Conference on Chemical and Process Engineering (ICheaP)
    Period02/06/1305/06/13

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