Gradient Descent Convergence

This lecture presents a stronger result about gradient descent, assuming a convex function with a minimum. It explains how the algorithm converges to the function's minimum at a rate of 1 over k, providing a detailed proof of the convergence. The instructor also discusses the importance of choosing the proper step size for convergence and highlights the challenges of determining the Lipschitz constant. Additionally, the lecture introduces the concept of strong convexity and how it can lead to even stronger convergence results, emphasizing the significance of understanding the properties of the function for optimization.