description
- Investigates landmark convex optimization algorithms and their complexity results. Studies theoretical foundations while also surveying current practical state-of-the-art methods. Topics may include Fenchel-Rockafellar duality, KKT conditions, proximal methods, and Nesterov acceleration. Recommended prerequisites: APPM 4440 or equivalent, and familiarity with linear programming.