Optimization methods, Department of Innovation and High Technologies, Fall 2018

Seminars on optimization methods

Syllabus

List of basic questions

1. Introduction. Convex sets and cones
2. Matrix calculus
3. Convex functions
4. KKT optimality conditions
5. Duality
6. Midterm
7. Introduction to numerical optimization and gradient descent
8. Beyond gradient descent: heavy ball, conjugate gradient and fast gradient methods
9. Stochastic first-order methods
10. Newton and quasi-Newton methods
11. Projected gradient method and Frank-Wolfe method
12. Linear programming problem + examples of tableau simplex method
13. Semidefinite programming
14. Intro to interior point methods