Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Covers the KKT conditions and convex optimization, discussing constraint qualifications and tangent cones of convex sets.
Covers the formulation and problem definition in optimization, focusing on defining the objective function, constraints, and feasible set.
Covers the fundamentals of Nonlinear Programming and its applications in Optimal Control, exploring techniques, examples, optimality definitions, and necessary conditions.
