© 2015

Selected Applications of Convex Optimization


Part of the Springer Optimization and Its Applications book series (SOIA, volume 103)

Table of contents

  1. Front Matter
    Pages i-x
  2. Li Li
    Pages 1-15
  3. Li Li
    Pages 17-52
  4. Li Li
    Pages 53-78
  5. Li Li
    Pages 115-126
  6. Li Li
    Pages 127-138
  7. Back Matter
    Pages 139-140

About this book


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.


Convex Optimization Convex Relaxation Expectation Maximization Linear Matrix Inequalities Support Vector Machines data mining

Authors and affiliations

  1. 1.Department of AutomationTsinghua UniversityBeijingChina

Bibliographic information

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