Optimization for Machine Learning: Proximal Descent

This lecture covers Proximal and Subgradient Descent in the context of Optimization for Machine Learning. It explains the Proximal Gradient Descent algorithm, composite optimization problems, and the convergence properties of these methods. The lecture also delves into subgradients, convexity, Lipschitz functions, and the optimality of first-order methods.