Cutting Plane Methods

Related lectures (30)

Page 2 of 3

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

Discusses proximal and subgradient descent methods for optimization in machine learning.

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

Introduces convex optimization, covering convex sets, solution concepts, and efficient numerical methods in mathematical optimization.

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

Explores optimization on convex sets, including KKT points and tangent cones.

Discusses advanced optimization techniques, focusing on faster and projected gradient descent methods in machine learning.

Covers optimization methods, convergence guarantees, trade-offs, and variance reduction techniques in numerical optimization.

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

Explores unconstrained optimization theory, covering global and local minima, convexity, and gradient concepts.