On Convex Optimization covers course organization, mathematical optimization problems, solution concepts, and optimization methods.
Covers an overview of convex optimization, affine sets, polyhedra, ellipsoids, and convex functions.
Introduces convex sets and functions, discussing minimizers, optimality conditions, and characterizations, along with examples and key inequalities.
Explores convex functions, including checking convexity, transformations, examples, minimization, geometric intuition, Schur's Lemma, distance function, perspective function, and relative entropy.
Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.
Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.
Covers the fundamentals of convex optimization, including mathematical problems, minimizers, and solution concepts, with an emphasis on efficient methods and practical applications.
