This course will start with gradient based methods in convex optimization starting with gradient descent, proximal point methods and the use of momentum and randomness. We will then look at algorithms for solving convex-concave saddle point problems and finding stationary points in nonconvex optimization. Time permitting, we will also touch briefly on online convex optimization.
