Nonsmooth DC optimization: recent developments

Abstract: In this talk we consider unconstrained optimization problems where the objective functions are represented as a difference of two convex (DC) functions. Various applications of DC optimization in machine learning are presented. We discuss two different approaches to design methods of nonsmooth DC optimization: an approach based on the extension of bundle methods and an approach based on the DCA (difference of convex algorithm). We also discuss numerical results obtained using these methods.

optimization and control

Audience: researchers in the topic

Variational Analysis and Optimisation Webinar

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