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Numerical nonsmooth optimization

by Adil Bagirov, Manlio Gaudioso, Napsu Karmitsa, Marko M. Mäkelä, and Sona Taheri (Eds.)

"A fool does a lot of work, a wise man gets off easier."
- Old Finnish proverb

Aim of the book

The aim of the forthcoming book is to survey different kind of numerical methods developed for nonsmooth optimization (NSO) and to give an overview to the most resent developments in the area. The book will cover both traditional methods and the methods developed for problems with special structures. The surveys will be written by the top authors on the field.

Nonsmooth optimization (NSO) refers to the general problem of minimizing (or maximizing) functions that are typically not differentiable at their minimizers (maximizers). These kinds of functions can be found in many applied fields, for example in image denoising, optimal control, neural network training, data mining, economics, and computational chemistry and physics. Since classical theory of optimization presumes certain differentiability and strong regularity assumptions for the functions to be optimized, it cannot be directly utilized, nor can the methods developed for smooth problems.

The book is aimed for post graduate students, professionals, and practitioners who know classical optimization, and the fact that it is not always enough. It will be published by Springer in 2019.

List of contents and contributors

General methods

Structure exploiting methods

Methods for special problems

Derivative free methods