Numerical Nonsmooth Optimization
State of the Art Algorithms
Adil M Bagirov editor Manlio Gaudioso editor Napsu Karmitsa editor Marko M Mäkelä editor Sona Taheri editor
Format:Hardback
Publisher:Springer Nature Switzerland AG
Published:29th Feb '20
Currently unavailable, and unfortunately no date known when it will be back
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.ISBN: 9783030349097
Dimensions: unknown
Weight: 1232g
698 pages
1st ed. 2020