Convex Optimization : Mathematical optimization, Convex function, Convex analysis

Eldon A. Mainyu

ISBN 10: 6136544830 / ISBN 13: 9786136544830

Published by Omniscriptum, 2026

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Please note that the content of this book primarily consists of articlesavailable from Wikipedia or other free sources online. Convexoptimization, a subfield of mathematical optimization, studies theproblem of minimizing convex functions. Convex minimization hasapplications in a wide range of disciplines, such as automatic controlsystems, estimation and signal processing, communications and networkselectronic circuit design, data analysis and modeling, statistics, andfinance. With recent improvements in computing and in optimizationtheory, convex minimization is nearly as straightforward as linearprogramming. These results are used by the theory of convex minimizationalong with geometric notions from functional analysis such as theHilbert projection theorem, the separating hyperplane theorem, andFarkas' lemma.

Seller Inventory # 9786136544830

Bibliographic Details

• Title: Convex Optimization : Mathematical optimization, Convex function, Convex analysis
• Publisher: Omniscriptum
• Publication Date: 2026
• Language: English
• ISBN 10: 6136544830
• ISBN 13: 9786136544830
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 191 grams

About this title

Synopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Convex optimization, a subfield of mathematical optimization, studies the problem of minimizing convex functions. Convex minimization has applications in a wide range of disciplines, such as automatic control systems, estimation and signal processing, communications and networks, electronic circuit design, data analysis and modeling, statistics, and finance. With recent improvements in computing and in optimization theory, convex minimization is nearly as straightforward as linear programming. These results are used by the theory of convex minimization along with geometric notions from functional analysis such as the Hilbert projection theorem, the separating hyperplane theorem, and Farkas' lemma.