Description
Book SynopsisThis 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.
Trade Review“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, maa.org, December, 2015)
Table of ContentsPreliminary Knowledge.- Support Vector Machines.- Parameter Estimations.- Norm Approximation and Regulariztion.- Semi-Definite Programing and Linear Matrix Inequalities.- Convex Relaxation.- Geometric Problems.
