Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores linear programming duality, covering constraints, variables, solutions, and the relationship between primal and dual LP.
Explores the convexity of Lovász extension and submodular function maximization, focusing on extending functions to convex sets and proving their convexity.
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
