Lectures related to Primal-dual Optimization: Algorithms and Convergence

Explores primal-dual optimization methods, emphasizing Lagrangian gradient techniques and their applications in data optimization.

Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

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

Covers convex optimization problems and gradient-based algorithms to find the global minimum.

Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.

Explores algorithms for composite optimization, including proximal operators and gradient methods, with examples and theoretical bounds.