There are a large number of textbooks devoted to the topic of optimization. A few of them are listed on this page. The course will draw upon material from several books, as no single book will cover all the material. Students are encouraged to get the Sra et al. MIT Press text, which provides a state of the art overview of optimization in machine learning. Boyd's textbook is a good text covering some of the background material and is available freely on the author’s web site.

1. • Optimization for Machine Learning, edited by Sra, Nowozin, and Wright, MIT Press 2011: State of the art overview of optimization, with emphasis on machine learning.
   

2. • Numerical Optimization, by Nocedal and Wright, Springer, 1999: Highly readable overview of general optimization methods.
   

3. • Convex analysis, by Rockafellar: Classic landmark text.
   

4. • Convex optimization, by Boyd and Vandenberghe: modern treatment of optimization theory
   

5. • Nonlinear programming, Bertsekas: Classic text on nonlinear optimization. 2nd edition is recommended.
   

6. • Parallel optimization, by Censor and Zenios: covers generalized distance measures
   

7. • Optimization in vector spaces, by Luenberger: Classic text on optimization in vector spaces.