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 3:30pm - 5:15pm, in ERB LT
- Thursdays 2:30pm - 4:15pm, in MMW LT1
- Teaching Assistants:
- He Chen (hchen at se.cuhk.edu.hk)
- Lemin Kong (lkong at se.cuhk.edu.hk)
- Shangyuan Liu (shangyuanliu at link.cuhk.edu.hk)
- Office Hours: Thursdays 4:30pm - 6:30pm, 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 New York, 2010.
- Luenberger, Ye: Linear and Nonlinear Programming (5th Edition), Springer Cham, 2021.
- Nesterov: Lectures on Convex Optimization (2nd Edition), Springer Cham, 2018.
Handouts
Lecture Notes
Homework Sets (Assignment Box: B21, ERB 5/F)
Last Updated: October 2, 2024