Homework assignments in mathematical optimization, MIPT, 2018-2019.
I consistently received the best score among fellow students throughout the course.
- Introduction. Convex sets and cones
- Matrix calculus
- Convex functions
- KKT optimality conditions
- Duality
- Midterm
- Introduction to numerical optimization and gradient descent
- Beyond gradient descent: heavy ball, conjugate gradient and fast gradient methods
- Stochastic first-order methods
- Newton and quasi-Newton methods
- Projected gradient method, Frank-Wolfe method and introduction to proximal methods
- Linear programming problem
- Semidefinite programming
- Interior point methods and concept of self-concordance functions