Global optimization of signomial programming problems

    Research output: Chapter in Book/Conference proceedingConference contributionScientific


    In this presentation, an overview of a signomial global optimization algorithm is given. As the name indicates, the algorithm can be used to solve mixed integer nonlinear programming problems containing signomial functions to global optimality. The method employs singlevariable power and exponential transformations for convexifying the nonconvex signomial functions termwise. By approximating the transformations using piecewise linear functions, piecewise convex underestimators for the nonconvex signomial functions as well as a relaxed convex problem can be obtained. In the algorithm, the approximations resulting from the piecewise linear functions are subsequentially improved resulting in a set of subproblems whose optimal solution converges to that of the original nonconvex problem. Finally, some recent theoretical results regarding the underestimation properties of the convexified signomial terms obtained using different transformations are also given
    Original languageUndefined/Unknown
    Title of host publicationProceedings of the European Workshop on Mixed Integer Nonlinear Programming
    EditorsPierre Bonami, Leo Liberti, Andrew J. Miller, Annick Sartenaer
    Publication statusPublished - 2010
    MoE publication typeB3 Non-refereed article in conference proceedings
    EventEuropean Workshop on Mixed Integer Nonlinear Programming - European Workshop on Mixed Integer Nonlinear Programming
    Duration: 12 Apr 201016 Apr 2010


    ConferenceEuropean Workshop on Mixed Integer Nonlinear Programming

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