Convex Polyhedra and Linear Programs

Related lectures (30)

Page 3 of 3

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

Introduces the basics of integer programming, including binary integer programs and constraint strategies.

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

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

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.

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

Explores separating hyperplanes for two sets X and Y, discussing conditions for separation and implications of convexity.

Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.

Explores the complexity of convex hulls and the concept of vertices within them.