Proximal and Subgradient Descent

This lecture covers the optimization techniques for machine learning, focusing on Proximal and Subgradient Descent methods. It explains the concept of proximal gradient descent, the equivalence of constrained and unconstrained problems, and the use of subgradients for non-differentiable functions. The lecture also delves into composite optimization problems, convergence analysis, and the optimality conditions for subgradient descent. Additionally, it discusses the convergence rates for Lipschitz convex functions and the importance of strong convexity in achieving faster convergence.