Conjugate Duality: Understanding Convex Optimization

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

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

Explores envelope representations, subgradients, and the duality gap in convex optimization.

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

Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.

Explores elementary results in convex optimization, including affine, convex, and conic hulls, proper cones, and convex functions.

Covers the concept of convex functions and their applications in optimization problems.

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

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

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