Finding an optimized set of transformations for convexifying nonconvex MINLP problems

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    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.
    Original languageUndefined/Unknown
    Title of host publication11th International Symposium on Process Systems Engineering
    EditorsIftekhar A Karimi, Rajagopalan Srinivasan
    Number of pages5
    ISBN (Print)978-0-444-59505-8
    Publication statusPublished - 2012
    MoE publication typeA4 Article in a conference publication
    Eventconference -
    Duration: 1 Jan 2012 → …


    Period01/01/12 → …


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

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