E0 350: Advanced Convex Optimization

• Term: January - April 2024.

• Credits: 3:1 

• Hours: M/W, 14:00 - 15:30 hrs.

• Venue: B-308 (EE, 2nd floor).

• Instructor: Kunal Chaudhury.  

• Grading: project (30%), mid-term (30%), and final (40%). 

• Prerequisites: linear algebra, basics of multivariate calculus and nonlinear optimization.

• Topics: convex sets and functions, extended-real-valued functions, topological properties; convex projection, supporting and separating hyperplane theorems; fixed-point iterations, convergence; subdifferential calculus, proximal and averaged operators, monotone operator theory; gradient and subgradient descent, proximal gradient descent, and ADMM.

• References:  

1. First-Order Methods in Optimization by Amir Beck.

2. Lectures on Convex Optimization by Yurii Nesterov.

• Lectures notes:  link.