# Simplex Method

**Definition:** The **Simplex Method or Simplex Algorithm** is used for calculating the optimal solution to the linear programming problem. In other words, the simplex algorithm is an iterative procedure carried systematically to determine the optimal solution from the set of feasible solutions.

Firstly, to apply the simplex method, appropriate variables are introduced in the linear programming problem, and the primary or the decision variables are equated to zero. The iterative process begins by assigning values to these defined variables. The value of decision variables is taken as zero since the evaluation in terms of the graphical approach begins with the origin. Therefore, x_{1} and x_{2} is equal to zero.

The decision maker will enter appropriate values of the variables in the problem and find out the variable value that contributes maximum to the objective function and removes those values which give undesirable results. Thus, the value of the objective function gets improved through this method. This procedure of substitution of variable value continues until any further improvement in the value of the objective function is possible.

**Following two conditions need to be met before applying the simplex method:**

- The right-hand side of each constraint inequality should be non-negative. In case, any linear programming problem has a negative resource value, then it should be converted into positive value by multiplying both the sides of constraint inequality by “-1”.
- The decision variables in the linear programming problem should be non-negative.

Thus, the simplex algorithm is efficient since it considers few feasible solutions, provided by the corner points, to determine the optimal solution to the linear programming problem.

## 1 Comment

Easy to understand I like it