Integer Programming: Doignon's Theorem

Related lectures (32)

Page 2 of 4

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.

Covers the theory of linear and integer programming, focusing on the integer hull and rational polyhedra.

Covers the modelling and optimization of energy systems, focusing on solving optimization problems with constraints and variables.

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.

Explores integer programming, John's Theorem, and the reduction to shortest vector computations in convex bodies.

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

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

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

Covers Minbowski's Theorem on Diophantine Approximation and Gram-Schmidt orthogonalization.