an efficient algorithm for finding the optimal solution in a linear programming model is the
Linear programming Otherwise, if a feasible solution exists and if the constraint set is bounded, then the optimum value is always attained on the boundary of the constraint set, by the maximum principle for convex functions (alternatively, by the minimum principle for concave functions) since linear functions are both convex and concave. However, some problems have distinct optimal solutions: For example, the problem of finding a feasible solution to a system of linear inequalities is a linear programming problem in which the objective function is the zero function (that is, the constant function taking the value zero everywhere): For this feasibility problem with the zero-function for its objective-function, if there are two distinct solutions, then every convex combination of the solutions is a solution.
United States Postal Service Furthermore, some economists have argued that because public enterprises may pursue objectives different than profit maximization, they might have more of an incentive than profit-maximizing firms to behave anticompetitively through policies such as predatory pricing, misstating costs, and creating barriers to entry.[111] To resolve those issues, one economist proposes a cost-allocation model that would determine the optimal allocation of USPS's common costs by finding the share of costs that would maximize USPS profits from its competitive products.[112] Postal regulators could use such a cost model to ensure that the Postal Service is not abusing its statutory monopoly by subsidizing price cuts in competitive product markets with revenue obtained from the monopolized market.[113]