Nonlinear Programming: 3rd Edition

The third edition of the book is a thoroughly rewritten version of the 1999 2nd edition. New material was included, some of the old material was discarded, and a large portion of the remainder was reorganized or revised.

This book provides a comprehensive and accessible presentation of algorithms for solving continuous optimization problems. It relies on rigorous mathematical analysis, but also aims at an intuitive exposition that makes use of visualization where possible. It places particular emphasis on modern developments, and their widespread applications in fields such as large-scale resource allocation problems, signal processing, and machine learning.

The book was developed through instruction at MIT, focuses on nonlinear and other types of optimization: iterative algorithms for constrained and unconstrained optimization, Lagrange multipliers and duality, large scale problems, and the interface between continuous and discrete optimization.

Among its special features, the book: 1) provides extensive coverage of iterative optimization methods within a unifying framework 2) provides a detailed treatment of interior point methods for linear programming 3) covers in depth duality theory from both a variational and a geometrical/convex analysis point of view 4) includes much new material on a number of topics, such as neural network training, large-scale optimization, signal processing, machine learning, and optimal control 5) includes a large number of examples and exercises detailed solutions of many of which are posted on the internet. Much supplementary/support material can be found at the publisher's and the author's web sites

• Convex Optimization Algorithms
• Convex Optimization, With Corrections 2008
• Dynamic Programming and Optimal Control (2 Vol Set)
• Numerical Optimization (Springer Series in Operations Research and Financial Engineering)
• Introduction to Linear Optimization (Athena Scientific Series in Optimization and Neural Computation, 6)
• Dynamic Programming and Optimal Control, Vol. I, 4th Edition
• Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
• Convex Analysis and Optimization
• Dynamic Programming and Optimal Control, Vol. II, 4th Edition: Approximate Dynamic Programming
• Convex Optimization Theory