Fenchel Conjugation: Basics and Applications

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (29)

Page 3 of 3

Faster Gradient Descent: Projected Optimization Techniques

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Convex Optimization: Gradient Flow

Explores convex optimization, emphasizing the importance of minimizing functions within a convex set and the significance of continuous processes in studying convergence rates.

Optimization Basics: Norms, Convexity, Differentiability

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

Convex Optimization: Conjugate Duality

Explores envelope representations, subgradients, conjugate functions, duality gap, and strong duality in convex optimization.

KKT for convex problems and Slater's CQ

Covers the KKT conditions and Slater's condition in convex optimization problems.

Geodesic Convexity: Theory and Applications

Explores geodesic convexity in metric spaces and its applications, discussing properties and the stability of inequalities.

Convexity: Functions and Global Minima

Explores convex functions, global minima, and their relationship with differentiability.

KKT Conditions: Convex Optimization

Explores the KKT conditions in convex optimization, including dual cones, SDP duality, and convex hulls.

Convex Optimization

Introduces the fundamentals of convex optimization, emphasizing the significance of convex functions in simplifying the minimization process.