Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
Covers basic concepts in linear algebra, such as matrices and systems of linear equations, emphasizing the importance of full-rank matrices.
Explores extreme points, vertices, and basic feasible solutions in polyhedra, emphasizing their properties and construction.
Covers the Simplex Algorithm, explaining optimality, unbounded costs, and degenerate problems.
Explores duality in linear programming, strong duality, complementary slackness, and the economic interpretation of dual variables as prices.
Explores Two-Stage Stochastic Programs, problem reformulation, Benders decomposition, extreme points, and sensitivity analysis.
