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Selected Applications of Convex Optimization de Li Li

Selected Applications of Convex Optimization: Springer Optimization and Its Applications, cartea 103

Autor Li Li
en Limba Engleză Paperback – 9 apr 2015
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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Specificații

ISBN-13: 9783662463550
ISBN-10: 3662463555
Pagini: 115
Ilustrații: X, 140 p. 30 illus., 25 illus. in color.
Dimensiuni: 155 x 235 x 10 mm
Greutate: 0.22 kg
Ediția:2015
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Springer Optimization and Its Applications

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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

Recenzii

“Selected Applications of Convex Optimization is abrief book, only 140 pages, and includes exercises with each chapter. It wouldbe a good supplemental text for an optimization or machine learning course.”(John D. Cook, MAA Reviews, maa.org, December, 2015)

Textul de pe ultima copertă

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.

Caracteristici

Presents applications of convex optimization issues arranged in a synthetic way Demonstrates the interplay of convex optimization theory and applications of carefully designed Matlab sample codes Introduces all derivation processes in details so that readers can teach themselves without any difficulties