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

Derivative-free optimization = theor...

Yu, Yang.

FindBook

Google Book

Amazon

博客來

Derivative-free optimization = theoretical foundations, algorithms, and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Derivative-free optimization/ by Yang Yu, Hong Qian, Yi-Qi Hu.
其他題名:	theoretical foundations, algorithms, and applications /
作者:	Yu, Yang.
其他作者:	Qian, Hong.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xv, 193 p. :ill. (chiefly color), digital ;24 cm.
內容註:	Introduction -- Preliminaries -- Framework -- Theoretical Foundation -- Basic Algorithm -- Optimization in Sequential Mode -- Optimization in High-Dimensional Search Space -- Optimization under Noise -- Optimization with Parallel Computing.
Contained By:	Springer Nature eBook
標題:	Mathematical optimization. -
電子資源:	https://doi.org/10.1007/978-981-96-5929-6
ISBN:	9789819659296

Derivative-free optimization = theoretical foundations, algorithms, and applications /
Yu, Yang.

Derivative-free optimizationtheoretical foundations, algorithms, and applications /[electronic resource] :by Yang Yu, Hong Qian, Yi-Qi Hu. - Singapore :Springer Nature Singapore :2025. - xv, 193 p. :ill. (chiefly color), digital ;24 cm. - Machine learning: foundations, methodologies, and applications,2730-9916. - Machine learning: foundations, methodologies, and applications..

Introduction -- Preliminaries -- Framework -- Theoretical Foundation -- Basic Algorithm -- Optimization in Sequential Mode -- Optimization in High-Dimensional Search Space -- Optimization under Noise -- Optimization with Parallel Computing.

This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences. The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book's structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML. Readers will benefit from a comprehensive yet concise presentation of modern DFO methods, gaining theoretical insights and practical tools to enhance their research and problem-solving capabilities. A foundational understanding of machine learning, probability theory, and algorithms is recommended for readers to fully engage with the material.

ISBN: 9789819659296

Standard No.: 10.1007/978-981-96-5929-6doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Derivative-free optimization = theoretical foundations, algorithms, and applications /
LDR:02948nmm a2200349 a 4500 001 2413682
003 DE-He213
005 20250702130327.0
006 m d
007 cr nn 008maaau
008 260205s2025 si s 0 eng d
020 $a 9789819659296 $q (electronic bk.)
020 $a 9789819659289 $q (paper)
024 7 $a 10.1007/978-981-96-5929-6 $2 doi
035 $a 978-981-96-5929-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5
072 7 $a UYQM $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 519.6 $2 23
090 $a QA402.5 $b .Y94 2025
100 1 $a Yu, Yang. $3 1672361
245 1 0 $a Derivative-free optimization $h [electronic resource] : $b theoretical foundations, algorithms, and applications / $c by Yang Yu, Hong Qian, Yi-Qi Hu.
260 $a Singapore : $c 2025. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xv, 193 p. : $b ill. (chiefly color), digital ; $c 24 cm.
338 $a online resource $b cr $2 rdacarrier
490 1 $a Machine learning: foundations, methodologies, and applications, $x 2730-9916
505 0 $a Introduction -- Preliminaries -- Framework -- Theoretical Foundation -- Basic Algorithm -- Optimization in Sequential Mode -- Optimization in High-Dimensional Search Space -- Optimization under Noise -- Optimization with Parallel Computing.
520 $a This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences. The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book's structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML. Readers will benefit from a comprehensive yet concise presentation of modern DFO methods, gaining theoretical insights and practical tools to enhance their research and problem-solving capabilities. A foundational understanding of machine learning, probability theory, and algorithms is recommended for readers to fully engage with the material.
650 0 $a Mathematical optimization. $3 517763
650 0 $a Artificial intelligence $x Mathematics. $3 813249
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Qian, Hong. $3 2103719
700 1 $a Hu, Yi-Qi. $3 3789910
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Machine learning: foundations, methodologies, and applications. $3 3531412
856 4 0 $u https://doi.org/10.1007/978-981-96-5929-6
950 $a Professional and Applied Computing (SpringerNature-12059)