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Selected applications of convex opti...

Li, Li.

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Selected applications of convex optimization

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Selected applications of convex optimization/ by Li Li.
作者:	Li, Li.
出版者:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2015.,
面頁冊數:	x, 140 p. :ill., digital ;24 cm.
內容註:	Preliminary Knowledge -- Support Vector Machines -- Parameter Estimations -- Norm Approximation and Regulariztion -- Semi-Definite Programing and Linear Matrix Inequalities -- Convex Relaxation -- Geometric Problems.
Contained By:	Springer eBooks
標題:	Mathematical optimization. -
電子資源:	http://dx.doi.org/10.1007/978-3-662-46356-7
ISBN:	9783662463567 (electronic bk.)

Selected applications of convex optimization
Li, Li.

Selected applications of convex optimization[electronic resource] /by Li Li. - Berlin, Heidelberg :Springer Berlin Heidelberg :2015. - x, 140 p. :ill., digital ;24 cm. - Springer optimization and its applications,v.1031931-6828 ;. - Springer optimization and its applications ;v.52..

Preliminary Knowledge -- Support Vector Machines -- Parameter Estimations -- Norm Approximation and Regulariztion -- Semi-Definite Programing and Linear Matrix Inequalities -- Convex Relaxation -- Geometric Problems.

This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems. All derivation processes are presented in detail to aid in comprehension. The book offers concrete guidance, helping readers recognize and formulate convex optimization problems they might encounter in practice.

ISBN: 9783662463567 (electronic bk.)

Standard No.: 10.1007/978-3-662-46356-7doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5 / .L5 2015

Dewey Class. No.: 519.6

Selected applications of convex optimization
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520 $a This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems. All derivation processes are presented in detail to aid in comprehension. The book offers concrete guidance, helping readers recognize and formulate convex optimization problems they might encounter in practice.
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