Textbooks. We recommend the following books, although we will not follow them too closely.
A First Course in Convex Optimization Theory, Ernest Ryu and Wotao Yin, in preparation, 2019.
First-Order Methods in Optimization, Amir Beck, MOS-SIAM Series on Optimization, 2017.
Convex Optimization: Algorithms and Complexity, Sebastien Bubeck, Foundations and Trends in Optimization, 2015.
Lectures on Optimization Methods for Machine Learning, Guanghui (George) Lan, 2019.
References. The following references also contain topics relevant to this course, and you might want to consult them.
Lectures on Convex Optimization, Yurii Nesterov, Springer, 2018.
Nonlinear Programming (3rd Edition), Dimitri Bertsekas, Athena scientific, 2016.
Optimization Methods for Large-Scale Machine Learning, Leon Bottou, Frank Curtis, and Jorge Nocedal, 2016.
Proximal Algorithms, Neal Parikh and Stephen Boyd, Foundations and Trends in Optimization, 2008.
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Stephen Boyd, Neal Parikh, Eric Chu, Foundations and Trends in Machine Learning, 2011.
Convex Optimization, Stephen Boyd, and Lieven Vandenberghe, Cambridge University Press, 2004.
Numerical Optimization, Jorge Nocedal and Stephen Wright, Springer 2006.
Numerical Linear Algebra, Lloyd Trefethen and David Bau, SIAM 1997.
Lectures on Modern Convex Optimization, Aharon Ben-Tal and Arkadi Nemirovski, SIAM 2001.