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Selected Applications of Convex Optimization [electronic resource] / by Li Li.

By: Contributor(s): Material type: TextTextSeries: Springer Optimization and Its Applications ; 103Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2015Description: X, 140 p. 30 illus., 25 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783662463567
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 519.6 23
LOC classification:
  • QA402-402.37
  • T57.6-57.97
Online resources:
Contents:
Preliminary Knowledge -- Support Vector Machines -- Parameter Estimations -- Norm Approximation and Regulariztion -- Semi-Definite Programing and Linear Matrix Inequalities -- Convex Relaxation -- Geometric Problems.
In: Springer eBooksSummary: 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.
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library EBook Available
Total holds: 0

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.

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