Abstract
This chapter describes a geometric approach to linear programming. To solve problems in linear programming, it is important to identify and label the quantities sought; identify the constraints and write them in mathematical form; and write the objective function. Any point (x, y) satisfying the constraints of a linear programming problem is called a feasible solution. A particular feasible solution may not give the largest or smallest value of the objective function. The inequalities (constraints) determine a convex set S of feasible solutions. This convex set S may be bounded or unbounded. If the convex set S is bounded, the objective function will have both a greatest and a least value. The greatest value will occur at some corner point and the least value will occur at a corner point. If the convex set S is not bounded, it may happen that the objective function does not have a maximum or a minimum value.
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