國立虎尾科技大學 |

Numerical Nonsmooth Optimization = State of the Art Algorithms /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Numerical Nonsmooth Optimization/ edited by Adil M. Bagirov, Manlio Gaudioso, Napsu Karmitsa, Marko M. Mäkelä, Sona Taheri.
Reminder of title:	State of the Art Algorithms /
other author:	Bagirov, Adil M.
Description:	XVII, 698 p. 407 illus., 15 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Operations research. -
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, Manlio Gaudioso, Napsu Karmitsa, Marko M. Mäkelä, Sona Taheri. - 1st ed. 2020. - XVII, 698 p. 407 illus., 15 illus. in color.online resource.

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:

573517
Operations research.

LC Class. No.: QA402-402.37

Dewey Class. No.: 519.6

Numerical Nonsmooth Optimization = State of the Art Algorithms /
LDR:02994nam a22004215i 4500 001 1026689
003 DE-He213
005 20200630210025.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030349103 $9 978-3-030-34910-3
024 7 $a 10.1007/978-3-030-34910-3 $2 doi
035 $a 978-3-030-34910-3
050 4 $a QA402-402.37
050 4 $a T57.6-57.97
072 7 $a KJT $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJM $2 thema
082 0 4 $a 519.6 $2 23
245 1 0 $a Numerical Nonsmooth Optimization $h [electronic resource] : $b State of the Art Algorithms / $c edited by Adil M. Bagirov, Manlio Gaudioso, Napsu Karmitsa, Marko M. Mäkelä, Sona Taheri.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XVII, 698 p. 407 illus., 15 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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.
650 0 $a Operations research. $3 573517
650 0 $a Management science. $3 719678
650 0 $a Decision making. $3 528319
650 0 $a Numerical analysis. $3 527939
650 0 $a Data mining. $3 528622
650 0 $a Economic theory. $3 809881
650 1 4 $a Operations Research, Management Science. $3 785065
650 2 4 $a Operations Research/Decision Theory. $3 669176
650 2 4 $a Numeric Computing. $3 669943
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Economic Theory/Quantitative Economics/Mathematical Methods. $3 1069071
700 1 $a Bagirov, Adil M. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1323017
700 1 $a Gaudioso, Manlio. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1307932
700 1 $a Karmitsa, Napsu. $e author. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1321924
700 1 $a Mäkelä, Marko M. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1323018
700 1 $a Taheri, Sona. $e author. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1321925
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030349097
776 0 8 $i Printed edition: $z 9783030349110
776 0 8 $i Printed edition: $z 9783030349127
856 4 0 $u https://doi.org/10.1007/978-3-030-34910-3
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)