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.
Elevator algorithm One variation of this method ensures all requests are serviced in only one direction, that is, once the head has arrived at the outer edge of the disk, it returns to the beginning and services the new requests in this one direction only (or vice versa). This is known as the "Circular Elevator Algorithm" or C-SCAN. Although the time of the return seek is wasted, this results in more equal performance for all head positions, as the expected distance from the head is always half the maximum distance, unlike in the standard elevator algorithm where cylinders in the middle will be serviced as much as twice as often as the innermost or outermost cylinders.