ENGG 5501: Foundations of Optimization
2018-19 First Term
Announcements
- NEW: Here are the practice finals from 2015-16 and 2017-18.
- NEW: The final examination will be held on December 14, 2018, from 7:00pm to 9:00pm, in ELB LT1. You can bring the course handouts, homeworks, homework solutions, and the notes you took during lectures to the exam. No other material will be allowed. If you have questions about the rules of the exam, please clarify with the teaching staff as soon as possible.
- NEW: Homework 6 is posted. It is due on December 10, 2018. The solution will be posted soon after the due date, so no late homework will be accepted.
- Handout 7 is posted.
- Here is the midterm solution.
- The University has announced that all classes will be suspended on September 17, 2018. A make-up class will be arranged later.
- Welcome to ENGG 5501!
- Here is the slides deck from the first lecture.
- To better facilitate discussions and Q&As, we have set up an online platform. Please follow this link to sign up.
General Information
- Instructor: Anthony Man-Cho So (manchoso at se.cuhk.edu.hk)
- Office Hours: Tuesdays 3:30pm - 5:00pm or by appointment, in ERB 604
- Lecture Time/Location:
- Mondays 4:30pm - 6:15pm, in ELB LT4
- Wednesdays 3:30pm - 5:15pm, in LSB LT1
- Teaching Assistants:
- Shixiang Chen (sxchen at se.cuhk.edu.hk)
- Office Hours: Mondays 2:30pm - 4:00pm, in ERB 614
- Zengde Deng (zddeng at se.cuhk.edu.hk)
- Office Hours: Thursdays 2:30pm - 4:00pm, in ERB 810C
- Conghui Tan (chtan at se.cuhk.edu.hk)
- Office Hours: Fridays 10:00am - 11:30am, in ERB 614
- Peng Wang (wangpeng at se.cuhk.edu.hk)
- Office Hours: Wednesdays 10:30am - 12:00pm, in ERB 810A
- 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 (30%), an in-class midterm examination (30%), and an in-class final examination (40%).
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
Homework Sets (Assignment Box: C11, 5th floor of ERB)
Last Updated: December 11, 2018