Lectures related to Role of Computation in Optimization

Explores the Rockafellar Theorem in optimal transport, focusing on c-cyclical monotonicity and convex functions.

Stochastic Optimization: Algorithms and Methods

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

Proximal Operators and Constrained Optimization

Introduces proximal operators, gradient methods, and constrained optimization, exploring their convergence and practical applications.

Convex Optimization

Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.

Primal-dual Optimization: Extra-Gradient Method

Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.

Stochastic Gradient Descent: Optimization Techniques

Explores Stochastic Gradient Descent with Averaging, comparing it with Gradient Descent, and discusses challenges in non-convex optimization and sparse recovery techniques.

Subgradients and Convex Functions

Explores subgradients in convex functions, emphasizing non-differentiable yet convex scenarios and properties of subdifferentials.