Explores convex functions, including checking convexity, transformations, examples, minimization, geometric intuition, Schur's Lemma, distance function, perspective function, and relative entropy.
Covers the fundamentals of convex optimization, including mathematical problems, minimizers, and solution concepts, with an emphasis on efficient methods and practical applications.
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.
Explores isometries in Euclidean spaces, including translations, rotations, and linear symmetries, with a focus on matrices.
Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.
