Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction
to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book
begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization
problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various
geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization
problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and
then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises
and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics,
statistics, finance and economics.
Gives comprehensive details on how to recognize convex optimization problems in a wide variety of settings
Provides a broad range of practical algorithms for solving real problems
Contains hundreds of worked examples and homework exercises
