General Information
- Instructor: Anthony Man-Cho So (manchoso at se.cuhk.edu.hk)
- Office Hours: By appointment, in ERB 604 or online
- Lecture Time/Location:
- Mondays 12:30pm - 2:15pm, in ERB LT
- Thursdays 12:30pm - 2:15pm, in YIA LT3
- Teaching Assistants:
- Yuen Man Pun (ympun at se.cuhk.edu.hk)
- Jinxin Wang (jxwang at se.cuhk.edu.hk)
- Jiaojiao Zhang (jjzhang at se.cuhk.edu.hk)
- Linglingzhi Zhu (llzzhu at se.cuhk.edu.hk)
- Office Hours: Thursdays 2:30pm - 4:00pm, in ERB 905 or online
- Online Q&A Forum: Follow this link.
Course Description
In this course we will develop the basic machinery for formulating and analyzing various optimization problems. Topics include convex analysis, linear and conic linear programming, nonlinear programming, optimality conditions, Lagrangian duality theory, and basics of optimization algorithms. Applications from different fields, such as combinatorial optimization, communications, computational economics and finance, machine learning, and signal and image processing, will be used to complement the theoretical developments. No prior optimization background is required for this class. However, students should have workable knowledge in multivariable calculus, real analysis, linear algebra and matrix theory.
Course Requirements
Homework sets (35%), midterm examination (30%), and final examination (35%).
General References
- Ben-Tal, Nemirovski: Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications, SIAM, 2001.
- Boyd, Vandenberghe: Convex Optimization, Cambridge University Press, 2004.
- Güler: Foundations of Optimization, Springer, 2010.
- Luenberger, Ye: Linear and Nonlinear Programming (4th Edition), Springer, 2016.
- Nesterov: Introductory Lectures on Convex Optimization: A Basic Course, Kluwer Academic Publishers, 2004.
Handouts
Lecture Notes
Homework Sets (Assignment Box: D11, ERB 5/F)
Last Updated: December 19, 2021