Lectures related to Optimality of Convergence Rates: Accelerated Gradient Descent

Explores optimal transport and gradient flows in Rd, emphasizing convergence and the role of Lipschitz and Picard-Lindelöf theorems.

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.

Covers optimization without constraints using the gradient method to find the function's minimum.

Covers convex optimization problems and gradient-based algorithms to find the global minimum.

Covers Newton's Method for optimization and discusses the caveats of indefiniteness in optimization problems.

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.