ENGG 5501: Foundations of Optimization

2016-17 First Term

Announcements

NEW: The final examination will be held on December 12, 2016, from 7:15pm to 9:15pm, in YIA LT3. 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: Here is the practice final.
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: Tuesdays 3:30pm - 5:00pm or by appointment, in ERB 604
Lecture Time/Location:
- Mondays 4:30pm - 6:15pm, in LSK LT3
- Wednesdays 3:30pm - 5:15pm, in LSK LT1

Teaching Assistants:
- Huikang Liu (hkliu at se.cuhk.edu.hk)
  - Office Hours: Mondays 9:30am - 11:30am, in ERB 905
- Xueying Ni (xyni at se.cuhk.edu.hk)
  - Office Hours: Tuesdays 9:30am - 11:30am, in ERB 905
- Man-Chung Yue (mcyue at se.cuhk.edu.hk)
  - Office Hours: Thursdays 9:30am - 11:30am, in ERB 905
- Qi Zhang (qzhang at se.cuhk.edu.hk)
  - Office Hours: Wednesdays 9:30am - 11:30am, 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: Optimization I-II: Convex Analysis, Nonlinear Programming Theory, Nonlinear Programming Algorithms, 2004.
Boyd, Vandenberghe: Convex Optimization, Cambridge University Press, 2004.
Cottle: Lecture Notes on Optimization, 2004.
Güler: Foundations of Optimization, Springer, 2010.
Luenberger, Ye: Linear and Nonlinear Programming (3rd Edition), Springer, 2008.
Nemirovski: Lectures on Modern Convex Optimization, 2005.

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
Handout 8: A Quick Tour on Optimization Algorithms

Homework Sets (Assignment Box: A16, 5th floor of ERB)

Homework 1 (due September 30, 2016)
Homework 2 (due October 14, 2016)
Homework 3 (due October 31, 2016)
Homework 4 (due November 21, 2016)
Homework 5 (due December 7, 2016)

Last Updated: December 10, 2016