Recent progress in unconstrained nonlinear optimization without derivatives

A.R. Conn, K. Scheinberg, Ph.L. Toint

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    We present an introduction to a new class of derivative free methods for unconstrained optimization. We start by discussing the motivation for such methods and why they are in high demand by practitioners. We then review the past developments in this field, before introducing the features that characterize the newer algorithms. In the context of a trust region framework, we focus on techniques that ensure a suitable "geometric quality" of the considered models. We then outline the class of algorithms based on these techniques, as well as their respective merits. We finally conclude the paper with a discussion of open questions and perspectives. © 1997 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.
    langue originaleAnglais
    Pages (de - à)397-414
    Nombre de pages18
    journalMathematical Programming Series B
    Numéro de publication3
    Etat de la publicationPublié - 1 oct. 1997

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