Dual is an alternate formulation, of a linear programming problem which can be used to obtain the optimal solution.
Every linear programming problem, referred to as a primal problem can be converted into a dual problem.
Usefulness of dual:
The number of decision variables in the primal is equal to the number of constraints in the dual. The number of decision variables in the dual is equal to the number of constraints in the primal. Since it is computationally easier to solve problems with fewer constraints in comparison to solving problems with fewer variables, the dual gives us the flexibility to choose which problem to solve.
In matrix form, we can express the primal problem as:
1) Maximize CTx subject to Ax ≤ B, x ≥ 0;
And corresponding dual problem is,
Minimize BTy subject to ATy ≥ C, y ≥ 0.
2) An alternative primal formulation is
Maximize CTx subject to Ax ≤ C;
with the corresponding asymmetric dual problem,
Minimize BTy subject to ATy = C, y ≥ 0.
The primal is minimizing
40x1 + 44 x2 +48x3 subject to
x1 + 2 x2 + 3x3 >= 20
4 x1+4 x2+4 x3 >=30
x1, x2 and x3 >=0
The dual of the problem is
Maximize 20y1 + 30y2 subject to
y1+ 4 y2 <= 40
2y1 + 4y2 <= 44
3y1 + 4y2 <= 48
y1,y2 >= 0
Hence, this concludes the definition of Dual along with its overview.
This article has been researched & authored by the Business Concepts Team. It has been reviewed & published by the MBA Skool Team. The content on MBA Skool has been created for educational & academic purpose only.
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