Mixed-Integer Linear Programming: Formulations and Applications

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

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

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

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

Explores relations between events, disjunctive constraints, and modeling with binary variables in optimization problems.

Explores solving discrete optimization problems by relaxing integrality constraints.

Explains the Branch and Bound algorithm for heuristic maximization problems using LP relaxations and pruning techniques.

Covers optimization problems in path finding and portfolio allocation.

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

Explores integer programs, nonconvex optimization, constraints, and geometric aspects of linear programming for optimal solutions.