ECE490: Introduction to Optimization (Spring 2022)
Course Information
TA's Office Hours: Aristomenis Tsopelakos, Wed 911am, via Zoom; Xingang Guo, Mon 13pm, via Zoom
Course Description
This is a senior/first year graduatelevel course on optimization. Topics include necessary and sufficient conditions for local optima;
characterization of convex sets and functions; unconstrained optimization, gradient descent and it variants;
constrained optimization and the gradient projection method; optimization with equality and inequality constraints, Lagrange multipliers, KKT conditions;
penalty and barrier function methods; weak and strong duality and Slater conditions;
augmented Lagrangian methods; subgradient methods; proximal gradient descent; applications.
Textbook
The recommended textbook is Nonlinear Programming by D. Bertsekas (Edition 3). We will closely follow the lecture notes and slides distributed via email.
Grading
Homework: There are roughly biweekly homework assignments. Use entry code N8BJ24 to add the course on Gradescope where you will be submitting assignments.
Collaboration on the homework is permitted, however each student must write and submit independent solutions.
Extensions will be granted with instructor approval in
advance. Otherwise late homeworks without such prior approval will not be accepted.
Grades for the students in Section P3 (3 credits) will be weighted as follows: Class Participation (5%), Homework
(50%), Midterm Exam (20%), and Final Exam (25%).
Grades for the students in Section P4 (4 credits) will be weighted as follows: Class Participation (5%), Homework
(45%), Midterm Exam (15%), Final Exam (20%), and Paper Review Project (15%).
