Model-Based Methods in Derivative-Free Nonsmooth Optimization

Charles Audet () and Warren Hare ()
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Charles Audet: École Polytechnique de Montréal, GERAD and Département de Mathématiques et Génie Industriel
Warren Hare: University of British Columbia, Department of Mathematics

Chapter Chapter 19 in Numerical Nonsmooth Optimization, 2020, pp 655-691 from Springer

Abstract: Abstract Derivative-free optimization (DFO) is the mathematical study of the optimization algorithms that do not use derivatives. One branch of DFO focuses on model-based DFO methods, where an approximation of the objective function is used to guide the optimization algorithm. Historically, model-based DFO has often assumed that the objective function is smooth, but unavailable analytically. However, recent progress has brought model-based DFO into the realm of nonsmooth optimization (NSO). In this chapter, we survey some of the progress of model-based DFO for nonsmooth functions. We begin with some historical context on model-based DFO. From there, we discuss methods for constructing models of smooth functions and their accuracy. This leads to modelling techniques for nonsmooth functions and a discussion on several frameworks for model-based DFO for NSO. We conclude the chapter with some of our opinions on profitable research directions in model-based DFO for NSO.

Date: 2020
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DOI: 10.1007/978-3-030-34910-3_19

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