Finding an optimized set of transformations for convexifying nonconvex MINLP problems

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    Sammanfattning

    In this paper we describe a method for obtaining sets of transformations for reformulating a mixed integer nonlinear programming (MINLP) problem containing nonconvex twice-differentiable (C-2) functions to a convex MINLP problem in an extended variable space. The method for obtaining the transformations is based on solving a mixed integer linear programming (MILP) problem given the structure of the nonconvex MINLP problem. The solution of the MILP problem renders a minimal set of transformations convexifying the nonconvex problem. This technique is implemented as an part of the alpha signomial global optimization algorithm (alpha SGO), a global optimization algorithm for nonconvex MINLP problems.
    OriginalspråkOdefinierat/okänt
    Titel på gästpublikation11th International Symposium on Process Systems Engineering
    RedaktörerIftekhar A Karimi, Rajagopalan Srinivasan
    FörlagElsevier
    Sidor1497–1501
    Antal sidor5
    ISBN (tryckt)978-0-444-59505-8
    StatusPublicerad - 2012
    MoE-publikationstypA4 Artikel i en konferenspublikation
    Evenemangconference -
    Varaktighet: 1 jan 2012 → …

    Konferens

    Konferensconference
    Period01/01/12 → …

    Nyckelord

    • global optimization
    • nonconvex MINLP problems
    • reformulation techniques
    • signomial functions

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