Convex Optimization

Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text 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.

• Numerical Optimization (Springer Series in Operations Research and Financial Engineering)
• Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
• Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
• Deep Learning (Adaptive Computation and Machine Learning series)
• Introduction to Linear Optimization (Athena Scientific Series in Optimization and Neural Computation, 6)
• Pattern Recognition and Machine Learning (Information Science and Statistics)
• Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
• Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
• Elements of Information Theory 2nd Edition (Wiley Series in Telecommunications and Signal Processing)