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
141
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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?
 

FAQ: Finding dual optimum of a linear problem

What is a dual problem in linear programming?

A dual problem in linear programming is derived from the primal problem, where the objective coefficients of the primal become the right-hand side constants in the dual, and the right-hand side constants of the primal become the objective coefficients in the dual. The dual provides insights into the primal's constraints and can be solved to find bounds on the primal's objective value.

How do you find the dual of a given linear programming problem?

To find the dual of a linear programming problem, you need to follow these steps: identify the primal's objective function (maximization or minimization), determine the constraints (inequalities or equalities), and then construct the dual by switching the roles of the objective function and constraints. Specifically, if the primal is a maximization problem with '≤' constraints, the dual will be a minimization problem with '≥' constraints, and vice versa.

What does it mean to find the dual optimum?

Finding the dual optimum refers to solving the dual linear programming problem to obtain the best possible value of the dual objective function. This value provides a bound for the primal problem's optimal value, and under certain conditions (strong duality), the optimal values of both the primal and dual problems will be equal.

How can you interpret the dual variables in the context of the primal problem?

The dual variables can be interpreted as shadow prices or marginal values of the resources represented by the primal constraints. They indicate how much the objective function of the primal problem would improve if the right-hand side of a constraint were increased by one unit. This interpretation helps in understanding the economic implications of the constraints in the primal problem.

What are the conditions for strong duality to hold?

Strong duality holds when both the primal and dual problems are feasible, and it guarantees that the optimal values of the primal and dual problems are equal. For linear programming problems, strong duality is generally ensured under the assumption of boundedness of the primal problem. If either the primal or dual is infeasible, or if the primal is unbounded, then strong duality does not hold.

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