Lectures related to Proximal Operators and Constrained Optimization

Explores optimization basics such as norms, convexity, and differentiability, along with practical applications and convergence rates.

Delves into structures in non-convex optimization, emphasizing scalable optimization for deep learning.

Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Covers stochastic optimization, sparsity, and non-smooth minimization via subgradient descent.

Covers non-convex optimization techniques and their applications in machine learning.