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Selected Applications of Convex Optimization electronic resource by Li Li.

By: Li, Li [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextSeries: Springer Optimization and Its ApplicationsPublication details: 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 resourceISBN: 9783662463567Subject(s): mathematics | Computer software | Mathematical models | Operations research | Management science | Mathematics | Operations Research, Management Science | Mathematical Software | Mathematical Modeling and Industrial MathematicsDDC classification: 519.6 LOC classification: QA402-402.37T57.6-57.97Online resources: Click here to access online
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.
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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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