Theory of Optimization and Continuous Algorithms

4 credits

Theoretical foundations of convex optimization and continuous algorithms. First-order methods, rates of convergence, and acceleration; gradient, subgradient, and mirror descent. Randomization, stochastic descent, leverage scores and sampling. Interior point methods. Linear systems in convex optimization. Algorithmic applications.