東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Optimization, discrete mathematics a...

Nikeghbali, Ashkan.

Linked to FindBook

Google Book

Amazon

博客來

Optimization, discrete mathematics and applications to data sciences

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimization, discrete mathematics and applications to data sciences/ edited by Ashkan Nikeghbali, Panos M. Pardalos, Michael Th. Rassias.
other author:	Nikeghbali, Ashkan.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xi, 237 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Mathematical optimization. -
Online resource:	https://doi.org/10.1007/978-3-031-78369-2
ISBN:	9783031783692

Optimization, discrete mathematics and applications to data sciences
Optimization, discrete mathematics and applications to data sciences[electronic resource] /edited by Ashkan Nikeghbali, Panos M. Pardalos, Michael Th. Rassias. - Cham :Springer Nature Switzerland :2025. - xi, 237 p. :ill., digital ;24 cm. - Springer optimization and its applications,v. 2201931-6836 ;. - Springer optimization and its applications ;v. 220..

This book delves into the dynamic intersection of optimization and discrete mathematics, offering a comprehensive exploration of their applications in data sciences. Through a collection of high-quality papers, readers will gain insights into cutting-edge research and methodologies that address complex problems across a wide array of topics. The chapters cover an impressive range of subjects, including advances in the study of polynomials, combinatorial identities, and global optimization algorithms. Readers will encounter innovative approaches to predictive models for non-performing loans, rainbow greedy matching algorithms, and the cost of detection in interaction testing. The book also examines critical issues such as demand aggregation, mid-term energy planning, and minimum-cost energy flow. Contributions from expert authors provide a deep dive into multilevel low-rank matrices, the protection of medical image authenticity, and the mathematical intricacies of the Braess paradox. This volume invites readers to explore diverse perspectives and theoretical insights that are both practical and forward-thinking. This publication is an invaluable resource for graduate students and advanced researchers in the fields of optimization and discrete mathematics. It is particularly beneficial for those interested in their applications within data sciences. Academics across these disciplines will find the book's content relevant to their work, while practitioners seeking to apply these concepts in industry will appreciate its practical case studies. Whether you are a scholar or a professional, this book offers a wealth of knowledge that bridges theory with real-world applications.

ISBN: 9783031783692

Standard No.: 10.1007/978-3-031-78369-2doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Optimization, discrete mathematics and applications to data sciences
LDR:02796nmm a2200325 a 4500 001 2409389
003 DE-He213
005 20250109115239.0
006 m d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031783692 $q (electronic bk.)
020 $a 9783031783685 $q (paper)
024 7 $a 10.1007/978-3-031-78369-2 $2 doi
035 $a 978-3-031-78369-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5
072 7 $a PBU $2 bicssc
072 7 $a MAT042000 $2 bisacsh
072 7 $a PBU $2 thema
082 0 4 $a 519.6 $2 23
090 $a QA402.5 $b .O62 2025
245 0 0 $a Optimization, discrete mathematics and applications to data sciences $h [electronic resource] / $c edited by Ashkan Nikeghbali, Panos M. Pardalos, Michael Th. Rassias.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xi, 237 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer optimization and its applications, $x 1931-6836 ; $v v. 220
520 $a This book delves into the dynamic intersection of optimization and discrete mathematics, offering a comprehensive exploration of their applications in data sciences. Through a collection of high-quality papers, readers will gain insights into cutting-edge research and methodologies that address complex problems across a wide array of topics. The chapters cover an impressive range of subjects, including advances in the study of polynomials, combinatorial identities, and global optimization algorithms. Readers will encounter innovative approaches to predictive models for non-performing loans, rainbow greedy matching algorithms, and the cost of detection in interaction testing. The book also examines critical issues such as demand aggregation, mid-term energy planning, and minimum-cost energy flow. Contributions from expert authors provide a deep dive into multilevel low-rank matrices, the protection of medical image authenticity, and the mathematical intricacies of the Braess paradox. This volume invites readers to explore diverse perspectives and theoretical insights that are both practical and forward-thinking. This publication is an invaluable resource for graduate students and advanced researchers in the fields of optimization and discrete mathematics. It is particularly beneficial for those interested in their applications within data sciences. Academics across these disciplines will find the book's content relevant to their work, while practitioners seeking to apply these concepts in industry will appreciate its practical case studies. Whether you are a scholar or a professional, this book offers a wealth of knowledge that bridges theory with real-world applications.
650 0 $a Mathematical optimization. $3 517763
650 0 $a Discrete mathematics. $3 3448845
650 1 4 $a Optimization. $3 891104
650 2 4 $a Discrete Mathematics. $3 1569938
650 2 4 $a Number Theory. $3 891078
650 2 4 $a Systems Theory, Control. $3 893834
650 2 4 $a Convex and Discrete Geometry. $3 893686
700 1 $a Nikeghbali, Ashkan. $3 3206200
700 1 $a Pardalos, P. M. $3 3593341
700 1 $a Rassias, Michael Th. $3 2059563
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer optimization and its applications ; $v v. 220. $3 3782599
856 4 0 $u https://doi.org/10.1007/978-3-031-78369-2
950 $a Mathematics and Statistics (SpringerNature-11649)