Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Introduces linear programming basics, including optimization problems, cost functions, simplex algorithm, geometry of linear programs, extreme points, and degeneracy.
Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.
Explores Linear Programming Duality, covering weak duality, strong duality, Lagrange multipliers interpretation, and optimization constraints.
Explores relations between events, disjunctive constraints, and modeling with binary variables in optimization problems.
Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.
Explores the concept of duality in linear programming, discussing the relationship between primal and dual problems.
Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
