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Numerical Nonsmooth Optimization : State of the Art Algorithms (1st ed. 2020)

By: Bagirov, Adil M.(Edited by) Gaudioso, Manlio(Edited by) Karmitsa, Napsu(Edited by) Makela, Marko M.(Edited by) Taheri, Sona(Edited by)

3030349128 / 9783030349127
Paperback / softback
Usually dispatched within 4 weeks
155 x 235 mm, 1080 grams 698 pages, 15 Illustrations, color; 392 Illustrations, black and white; XVII, 698 p. 407 illus., 15
Professional & Vocational  Learn More

Solving nonsmooth optimization (NSO) problems is critical in many practical applications and real-world modeling systems.

The aim of this book is to survey various numerical methods for solving NSO problems and to provide an overview of the latest developments in the field.

Experts from around the world share their perspectives on specific aspects of numerical NSO.

The book is divided into four parts, the first of which considers general methods including subgradient, bundle and gradient sampling methods.

In turn, the second focuses on methods that exploit the problem's special structure, e.g. algorithms for nonsmooth DC programming, VU decomposition techniques, and algorithms for minimax and piecewise differentiable problems.

The third part considers methods for special problems like multiobjective and mixed integer NSO, and problems involving inexact data, while the last part highlights the latest advancements in derivative-free NSO. Given its scope, the book is ideal for students attending courses on numerical nonsmooth optimization, for lecturers who teach optimization courses, and for practitioners who apply nonsmooth optimization methods in engineering, artificial intelligence, machine learning, and business.

Furthermore, it can serve as a reference text for experts dealing with nonsmooth optimization.


KCA Economic theory & philosophy, PBU Optimization, UMB Algorithms & data structures, UNF Data mining, UYA Mathematical theory of computation

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