Lecture
This lecture covers proximal and subgradient descent methods in optimization for machine learning. The instructor begins by introducing proximal gradient descent as an extension of projected gradient descent, explaining how it can handle non-differentiable functions. The lecture details the algorithm's update steps and the concept of subgradients, providing examples to illustrate these ideas. The instructor emphasizes the importance of understanding the convergence rates of these methods, particularly in the context of Lipschitz continuous functions and strong convexity. The lecture also discusses the optimality of first-order methods and the conditions under which these convergence rates can be achieved. Throughout the session, the instructor engages with the audience, addressing questions and clarifying concepts related to the algorithms presented. The lecture concludes with a discussion on the implications of bounded subgradients and the practical considerations for implementing these optimization techniques in machine learning applications.