Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Explores the concept of duality in linear programming, discussing the relationship between primal and dual problems.
Explores duality in linear programming, strong duality, complementary slackness, and the economic interpretation of dual variables as prices.
Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.
