Finding dual optimum of a linear problem

In summary, "Finding dual optimum of a linear problem" involves solving a linear programming problem by identifying its dual formulation. The dual problem provides insights into the constraints and objective of the primal problem. The relationship between the primal and dual solutions helps in determining the optimal values efficiently, ensuring that both solutions align according to the principles of duality in linear programming. This approach is crucial for understanding resource allocation, economic interpretation, and sensitivity analysis within linear optimization contexts.
  • #1
Trollfaz
144
14
A simple linear problem goes
min c'x such that f_i(x)<= 0 and Ax=b
x
Suppose we make all constraints affine. Then

Dx-e<=0 and Ax-b =0
We get the Langrangian function as
c'x + λ'(Dx-e) +ν'(Ax-b) and since Ax-b is 0,
we reduce L to
c'x + λ'(Dx-e)
The dual function g is
inf L(x,λ)
x
Then I differentiate L against x to get c=-D'λ
With that we get g(λ) as -λ.e
So I conclude that d*=sup g(λ) against λ.
Does differentiation of the Lagrangian function give me the dual optimum and does it work for all convex inequality constraints and convex objective functions?
 
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