Discrete Optimization: Set Covering

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Explains thermodynamic properties, equations of state, and mixture rules for energy systems modeling.

Introduces Yalmip, a MATLAB toolbox for optimization modeling and solving with MOSEK and GUROBI.

Explores solving discrete optimization problems by relaxing integrality constraints.

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

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

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.

Covers the Metropolis algorithm for approximate optimization using lazy chains and exploring state space efficiently.

Covers exercises and solutions in translating text problems into linear programming and optimizing decision-making processes.

Explores optimal decision making through discrete optimization, emphasizing binary variables and practical applications.

Covers integer programming, convex hulls, Gomory cutting planes, and branch and bound methods.