1. Terminology
The function to be maximized or minimized is called the objective function. A vector, x for the standard maximum problem or y for the standard minimum problem, is said to be feasible if it satisfies the corresponding constraints. The set of feasible vectors is called the constraint set. A linear programming problem is said to be feasible if the constraint set is not empty; otherwise it is said to be infeasible. A feasible maximum (resp. minimum) problem is said to be unbounded if the objective function can assume arbitrarily large positive (resp. negative) values at feasible vectors; otherwise, it is said to be bounded. The value of a bounded feasible maximum (resp, minimum) problem is the maximum (resp. minimum)-value of the objective function as the variables range over the constraint set. A feasible vector at which the objective Function achieves the value is called optimal.