This textbook investigates the complex landscape of nonlinear and other types of optimization, focusing on the development and application of iterative algorithms. Developed through instruction at MIT, the text provides a rigorous examination of constrained and unconstrained optimization, Lagrange multipliers and duality, large-scale problems, and the intersection of continuous and discrete optimization. It aims to offer a unifying framework for understanding iterative optimization methods and presents advanced topics with extensive examples and exercises.

This rigorous textbook, developed from MIT instruction, is recognized for its comprehensive treatment of nonlinear and other optimization types. It is particularly noted for its unifying framework for iterative algorithms and its in-depth exploration of duality theory. The inclusion of new material on topics like neural network training and large-scale optimization, alongside numerous examples and exercises with online solutions, makes it a valuable resource for advanced study.