The dual maximizer provides information about the primal problem, including sensitivity of the minimum value to changes in the constraints. The dual maximum value is always less than or equal to the primal minimum value, so it provides a lower bound. The primal minimization problem has a related maximization problem that is the Lagrangian dual problem. With LinearOptimization, parameter equations of the form par val, where par is not in vars and val is numerical or an array with numerical values, may be included in the constraints to define parameters used in f or cons.The constraints cons can be specified by:.Vector variable restricted to the geometric region Variable with name and dimensions inferred The variable specification vars should be a list with elements giving variables in one of the following forms:.When the objective function is real valued, LinearOptimization solves problems with by internally converting to real variables, where and.Mixed-integer linear optimization finds and that solve the problem:.Linear optimization finds that solves the primal problem: ».Linear optimization is a convex optimization problem that can be solved globally and efficiently with real, integer or complex variables.Linear optimization is also known as linear programming (LP).
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