東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Order analysis, deep learning, and connections to optimization

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Order analysis, deep learning, and connections to optimization/ by Johannes Jahn.
作者:	Jahn, Johannes.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xix, 181 p. :ill. (some col.), digital ;24 cm.
內容註:	Preliminaries -- C Representing Functionals -- Application in Nonlinear Optimization -- Application in Vector Optimization -- Application in Set Optimization -- Basics of Deep Learning -- Deep Learning with Set-Valued Inputs.
Contained By:	Springer Nature eBook
標題:	Mathematical optimization. -
電子資源:	https://doi.org/10.1007/978-3-031-67422-8
ISBN:	9783031674228

Order analysis, deep learning, and connections to optimization
Jahn, Johannes.

Order analysis, deep learning, and connections to optimization[electronic resource] /by Johannes Jahn. - Cham :Springer Nature Switzerland :2024. - xix, 181 p. :ill. (some col.), digital ;24 cm. - Vector optimization,1867-898X. - Vector optimization..

Preliminaries -- C Representing Functionals -- Application in Nonlinear Optimization -- Application in Vector Optimization -- Application in Set Optimization -- Basics of Deep Learning -- Deep Learning with Set-Valued Inputs.

This book introduces readers to order analysis and various aspects of deep learning, and describes important connections to optimization, such as nonlinear optimization as well as vector and set optimization. Besides a review of the essentials, this book consists of two main parts. The first main part focuses on the introduction of order analysis as an application-driven theory, which allows to treat order structures with an analytical approach. Applications of order analysis to nonlinear optimization, as well as vector and set optimization with fixed and variable order structures, are discussed in detail. This means there are close ties to finance, operations research, and multicriteria decision making. Deep learning is the subject of the second main part of this book. In addition to the usual basics, the focus is on gradient methods, which are investigated in the context of complex models with a large number of parameters. And a new fast variant of a gradient method is presented in this part. Finally, the deep learning approach is extended to data sets given by set-valued data. Although this set-valued approach is more computationally intensive, it has the advantage of producing more robust predictions. This book is primarily intended for researchers in the fields of optimization, order theory, or artificial intelligence (AI), but it will also benefit graduate students with a general interest in these fields. The book assumes that readers have a basic understanding of functional analysis or at least basic analysis. By unifying and streamlining existing approaches, this work will also appeal to professionals seeking a comprehensive and straightforward perspective on AI or order theory approaches.

ISBN: 9783031674228

Standard No.: 10.1007/978-3-031-67422-8doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Order analysis, deep learning, and connections to optimization
LDR:03042nmm a2200361 a 4500 001 2380056
003 DE-He213
005 20241023125747.0
006 m d
007 cr nn 008maaau
008 250418s2024 sz s 0 eng d
020 $a 9783031674228 $q (electronic bk.)
020 $a 9783031674211 $q (paper)
024 7 $a 10.1007/978-3-031-67422-8 $2 doi
035 $a 978-3-031-67422-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5
072 7 $a KJT $2 bicssc
072 7 $a KJMD $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJMD $2 thema
082 0 4 $a 519.6 $2 23
090 $a QA402.5 $b .J25 2024
100 1 $a Jahn, Johannes. $3 3529296
245 1 0 $a Order analysis, deep learning, and connections to optimization $h [electronic resource] / $c by Johannes Jahn.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xix, 181 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Vector optimization, $x 1867-898X
505 0 $a Preliminaries -- C Representing Functionals -- Application in Nonlinear Optimization -- Application in Vector Optimization -- Application in Set Optimization -- Basics of Deep Learning -- Deep Learning with Set-Valued Inputs.
520 $a This book introduces readers to order analysis and various aspects of deep learning, and describes important connections to optimization, such as nonlinear optimization as well as vector and set optimization. Besides a review of the essentials, this book consists of two main parts. The first main part focuses on the introduction of order analysis as an application-driven theory, which allows to treat order structures with an analytical approach. Applications of order analysis to nonlinear optimization, as well as vector and set optimization with fixed and variable order structures, are discussed in detail. This means there are close ties to finance, operations research, and multicriteria decision making. Deep learning is the subject of the second main part of this book. In addition to the usual basics, the focus is on gradient methods, which are investigated in the context of complex models with a large number of parameters. And a new fast variant of a gradient method is presented in this part. Finally, the deep learning approach is extended to data sets given by set-valued data. Although this set-valued approach is more computationally intensive, it has the advantage of producing more robust predictions. This book is primarily intended for researchers in the fields of optimization, order theory, or artificial intelligence (AI), but it will also benefit graduate students with a general interest in these fields. The book assumes that readers have a basic understanding of functional analysis or at least basic analysis. By unifying and streamlining existing approaches, this work will also appeal to professionals seeking a comprehensive and straightforward perspective on AI or order theory approaches.
650 0 $a Mathematical optimization. $3 517763
650 0 $a Deep learning (Machine learning) $3 3538509
650 1 4 $a Operations Research and Decision Theory. $3 3591727
650 2 4 $a Optimization. $3 891104
650 2 4 $a Functional Analysis. $3 893943
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Vector optimization. $3 1565454
856 4 0 $u https://doi.org/10.1007/978-3-031-67422-8
950 $a Business and Management (SpringerNature-41169)