Name: Numerical Nonsmooth Optimization: State of the Art Algorithms (en Inglés)
SKU: 57268109
Price: 162.97 USD
Availability: InStock
Author: Bagirov, Adil M. ; Gaudioso, Manlio ; Karmitsa, Napsu

portada Numerical Nonsmooth Optimization: State of the Art Algorithms (en Inglés)

Numerical Nonsmooth Optimization: State of the Art Algorithms (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Bagirov, Adil M. ; Gaudioso, Manlio ; Karmitsa, Napsu

Idioma

Inglés

N° páginas

698

Encuadernación

Tapa Blanda

Dimensiones

23.4x15.6x3.7 cm

Peso

0.99 kg.

ISBN13

9783030349127

Categorías

Investigación operativa
Algoritmos y estructuras de datos
Minería de datos («data mining»)

Numerical Nonsmooth Optimization: State of the Art Algorithms (en Inglés)

Bagirov, Adil M. ; Gaudioso, Manlio ; Karmitsa, Napsu (Autor) · Springer · Tapa Blanda

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Reseña del libro "Numerical Nonsmooth Optimization: State of the Art Algorithms (en Inglés)"

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 isideal 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.