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Numerical nonsmooth optimization = s...

Bagirov, Adil M.

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Numerical nonsmooth optimization = state of the art algorithms /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Numerical nonsmooth optimization/ edited by Adil M. Bagirov ... [et al.].
Reminder of title:	state of the art algorithms /
other author:	Bagirov, Adil M.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xvii, 698 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Part I: General Methods -- Part II: Structure Exploiting Methods -- Part III: Methods for Special Problems -- Part IV: Derivative-free Methods.
Contained By:	Springer eBooks
Subject:	Nonsmooth optimization. -
Online resource:	https://doi.org/10.1007/978-3-030-34910-3
ISBN:	9783030349103

Numerical nonsmooth optimization = state of the art algorithms /
Numerical nonsmooth optimizationstate of the art algorithms /[electronic resource] :edited by Adil M. Bagirov ... [et al.]. - Cham :Springer International Publishing :2020. - xvii, 698 p. :ill., digital ;24 cm.

Introduction -- Part I: General Methods -- Part II: Structure Exploiting Methods -- Part III: Methods for Special Problems -- Part IV: Derivative-free Methods.

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: 9783030349103

Standard No.: 10.1007/978-3-030-34910-3doiSubjects--Topical Terms:

709245
Nonsmooth optimization.

LC Class. No.: QA402.5 / .N864 2020

Dewey Class. No.: 519.6

Numerical nonsmooth optimization = state of the art algorithms /
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245 0 0 $a Numerical nonsmooth optimization $h [electronic resource] : $b state of the art algorithms / $c edited by Adil M. Bagirov ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xvii, 698 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Part I: General Methods -- Part II: Structure Exploiting Methods -- Part III: Methods for Special Problems -- Part IV: Derivative-free Methods.
520 $a 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.
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650 2 4 $a Economic Theory/Quantitative Economics/Mathematical Methods. $3 2162305
700 1 $a Bagirov, Adil M. $3 3448541
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