Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.
