An augmented Lagrangian approach to non-convex SAO using diagonal quadratic approximations

A.A. Groenwold, L.F.P. Etman, S. Kok, D.W. Wood, S. Tosserams

    Research output: Contribution to journalArticleAcademicpeer-review

    7 Citations (Scopus)

    Abstract

    Successful gradient-based sequential approximate optimization (SAO) algorithms in simulation-based optimization typically use convex separable approximations. Convex approximations may however not be very efficient if the true objective function and/or the constraints are concave. Using diagonal quadratic approximations, we show that non-convex approximations may indeed require significantly fewer iterations than their convex counterparts. The nonconvex subproblems are solved using an augmented Lagragian (AL) strategy, rather than the Falk-dual, which is the norm in SAO based on convex subproblems.
    Original languageEnglish
    Pages (from-to)415-421
    JournalStructural and Multidisciplinary Optimization
    Volume38
    Issue number4
    DOIs
    Publication statusPublished - 2009

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