Lectures related to Optimization Methods: Unconstrained Problems Analysis

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

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

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.

Introduces preconditioning in optimization problems and explains steepest descent iteration.