Recent progress in unconstrained nonlinear optimization without derivatives

Recent progress in unconstrained nonlinear optimization without derivatives

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Article ID: iaor1999960
Country: Netherlands
Volume: 79
Issue: 1/3
Start Page Number: 397
End Page Number: 414
Publication Date: Oct 1997
Journal: Mathematical Programming
Authors: , ,
Abstract:

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.

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