Lectures related to Mirror Prox: Optimization and Norms

Covers optimization and simulation techniques for drawing from multivariate distributions and dealing with correlations.

Covers optimization algorithms, focusing on Proximal Gradient Descent and its variations.

Covers fundamental concepts of optimization and operations research, exploring real-world examples and key topics over a semester.

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

Delves into shape preservation through minimizing elastic energy and optimizing centroid projection.

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Discusses MILP model, typical days, clustering, and extreme periods analysis in energy systems optimization.

Covers the Newton's local method in Python using NumPy for optimization.