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The discretized version of the constrained minimization problem that gives the optimal solution is solved by mathematical programming, using the Method of Moving Asymptotes and the finite element method in its displacement-based formulation.
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When discrete decisions are involved in the follower's problem, the resulting lower-level mixed-integer program prohibits direct transformation of the bilevel program into a single-level mathematical program using the KKT conditions.
Specifically, the mathematical program used for evaluating the corresponding performance measure is built with a dynamic system model, which usually consists of a set of differential-algebraic equations (DAEs).
The superstructure is optimized more effectively and reliably by integrating the mathematical programming approach using multi-objective mixed integer non-linear programming techniques and heuristic approach.
The problem is formulated as a mathematical programming model using the group contribution equation of state (GC-EOS) for rigorous phase equilibria predictions.
In addition, the simplest model can also be mapped into the multi-knapsack problem, in combination with dynamic programming using the mathematical model we have mentioned before.
These approaches hybridize metaheuristic strategies and mathematical programming, and use an idea of defining neighborhoods as small mixed integer programs (MIP), and exploring neighborhoods using MIP solvers.
Mathematical programming is used in planning production schedules, in transportation, in military logistics, and in calculating economic growth, by inserting assumed values for the variables in the equations and solving for the unknowns.
To solve this nonlinear mathematical programming we use the sequential quadratic programming (SQP).
Mathematical programming is used as a nonparametric approach to supervised classification.
In order to obtain the optimal design sensitivities analysis and optimization techniques based in the nonlinear mathematical programming are used.
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