This method can reduce the amount of constraints and make the computation more efficient. Because B&B usually uses generalized simplex algorithm to search for optimum solution in solution space, fewer constraints also mean less operations in each generalized simplex operation.
Some constraints do not influence the outcome of the computation; therefore, they could be removed from the constraint set.
Our method will be of great usage while handling the ILP problems with many constraints.
Besides, the proposed procedure could reduce the usage of computer memory.
What if the ILP problems are not with many constraints but with many variables and few constraints?
Then the dual theorem could be applied to transform the primal to its dual.
The variables in the dual problem can be transferred to the constraints in the primal problem.
In the same way, the constraints in the dual problem can be transferred to the variables in the primal problem.
For the case with many variables and less constraints, duality theorem should be used first to transform the original problems to the case with fewer variables and many constraints.
Then our method can reduce the number of constraints by shrinking the solution space.
In proposed method, we may need to search for the other extreme points next to the optimum one by generalized simplex method.
This searching method has already existed and we do not need to develop a new one.