Lectures related to Coordinate Descent: Efficient Optimization Techniques

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.

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

Discusses the gradient method for optimization, focusing on its application in machine learning and the conditions for convergence.

Explores stochastic optimization algorithms and methods for convex problems with smooth and nonsmooth risks.