The objective of a linear programming problem is to obtain an optimal solution. Linear programming problems deal with the problem of minimizing or maximizing a linear objective function in the presence of a system of linear inequalities. The linear objective function represents cost or pro t. A large and complex problem can be formulated in the form of a linear programming problem, and users can solve such a large problem in a de finite amount of time
using the simplex method and computer.
In this part we study a graphical method for solving linear programming problems. This method is helpful to choose the best feasible point among the many possible feasible points. A point minimizing the objective function and satisfying the set of linear constraints is called a “feasible point”.