Covers the basics of linear programming and the simplex method, focusing on finding optimal solutions and handling degeneracy.
Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.
Explores finding the initial Basic Feasible Solution (BFS) in a linear program.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Covers the basics of linear programming, defining corners, extreme points, and feasible solutions within polyhedrons.
Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.
Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.
