Covers weak and strong duality in optimization problems, focusing on Lagrange multipliers and KKT conditions.
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 Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.
Explores duality in linear programming, strong duality, complementary slackness, and the economic interpretation of dual variables as prices.
Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Explores the convexity of Lovász extension and submodular function maximization, focusing on extending functions to convex sets and proving their convexity.
