Explores linear programming duality, covering constraints, variables, solutions, and the relationship between primal and dual LP.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores strong duality, complementary slackness, economic interpretation, and stochastic problem scenarios in linear programming.
Explores the convexity of Lovász extension and submodular function maximization, focusing on extending functions to convex sets and proving their convexity.
