ENGG 5501: Foundations of Optimization

2017-18 First Term

Announcements

NEW: The final examination will be held on December 7, 2017, from 7:00pm to 9:00pm, in LSB 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 5 is posted. It is due on December 5, 2017.
To better facilitate discussions and Q&As, we have set up an online platform. Please follow this link to sign up.
PLEASE READ: Student/Faculty Expectations on Teaching and Learning, from the Faculty of Engineering, The Chinese University of Hong Kong.

General Information

Instructor: Anthony Man-Cho So (manchoso at se.cuhk.edu.hk)
Office Hours: Thursdays 3:30pm - 5:00pm or by appointment, in ERB 604
Lecture Time/Location:
- Mondays 4:30pm - 6:15pm, in MMW LT2
- Wednesdays 3:30pm - 5:15pm, in LSB LT1

Teaching Assistants:
- Shixiang Chen (sxchen at se.cuhk.edu.hk)
  - Office Hours: Tuesdays 10:00am - 11:30am, in ERB 614
- Huikang Liu (hkliu at se.cuhk.edu.hk)
  - Office Hours: Thursdays 10:00am - 11:30am, in ERB 905
- Xueying Ni (xyni at se.cuhk.edu.hk)
  - Office Hours: Wednesdays 1:00pm - 2:30pm, in ERB 905
- Conghui Tan (chtan at se.cuhk.edu.hk)
  - Office Hours: Mondays 10:00am - 11:30am, in ERB 614
- Qi Zhang (qzhang at se.cuhk.edu.hk)
  - Office Hours: Fridays 11:00am - 12:30pm, in ERB 905

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 (60%) 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

Handout A: Information Sheet
Handout B: Linear Algebra Cheat Sheet
Handout C: Real Analysis Cheat Sheet

The mathematical prerequisites for this course are summarized in Handouts B and C. Students are strongly advised to go through them carefully.
Handout 1: Introduction
Handout 2: Elements of Convex Analysis
Handout 3: Elements of Linear Programming
Handout 4: Some Applications of Linear Programming
Handout 5: Elements of Conic Linear Programming
Handout 6: Some Applications of Conic Linear Programming
Handout 7: Optimality Conditions and Lagrangian Duality

Homework Sets (Assignment Box: A17, 5th floor of ERB)

Homework 1 (due September 29, 2017) Solution
Homework 2 (due October 20, 2017) Solution
Homework 3 (due November 13, 2017) Solution
Homework 4 (due November 24, 2017) Solution
Homework 5 (due December 5, 2017) Solution

Last Updated: December 6, 2017