This course covers the fundamentals of convex optimization. We will talk about mathematical fundamentals, modeling (how to set up optimization problems for different applications), and algorithms.
Instructor: Justin Romberg
Course Notes
Notes 1, introduction and example (see also: intro slides)
I. Convexity
Notes 2, convex sets
Notes 3, convex functions
II. Unconstrained Optimization
Notes 4, optimality conditions iterative descent methods, line search
Notes 5, gradient descent
Homework
Homework 1, due Thursday January 16
Homework 2, due Thursday January 23. You will need the file hw02_prob06.py.
Homework 3, due Thursday January 30.