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.
    Original languageUndefined/Unknown
    Title of host publication11th International Conference on Chemical and Process Engineering - selected papers of ICheaP11
    EditorsSauro Pierucci, Jiří J. Klemeš
    PublisherAssociazione Italiana di Ingegneria Chimica
    Number of pages6
    ISBN (Print)978-88-95608-23-5
    Publication statusPublished - 2013
    MoE publication typeA4 Article in a conference publication
    EventInternational Conference on Chemical and Process Engineering (ICheaP) - 11th International Conference on Chemical and Process Engineering (ICheaP)
    Duration: 2 Jun 20135 Jun 2013


    ConferenceInternational Conference on Chemical and Process Engineering (ICheaP)

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