Discrete optimization: Knapsack

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Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Explores finding the initial Basic Feasible Solution (BFS) in a linear program.

Explores the cutset formulation for the MST Problem and Gomory Cutting Planes method.

Covers optimization problems in path finding and portfolio allocation.

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Covers semi-definite programming and optimization over positive semidefinite cones.

Covers sensitivity analysis in linear programming, focusing on optimal solutions and their sensitivities to changes.

Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Introduces linear programming, emphasizing optimization with constraints and practical examples.