Basic Concepts and Principles

Given a linear programming problem as

Primary Problem

     Min Ctx
Subject to
     Ax ≤ B
     x ≥ 0
     A∈ Mmxn, rank(A)=m, b∈Rm, C∈Rn

It is associated with another problem which we call Dual form

Dual Problem

     Max btw
Subject to
     Atw ≥ C
     w ≥ 0
     A∈ Mmxn, con rank(A)=m, b∈Rm, C∈Rn Bur now w ∈Rm

That is, what was the cost vector, now is constraints vector and vice versa. Constraints matrix is transposed in the dual. In some cases it is easier to solve one than another, or it is possible to give conditions for one that determine properties of solutions of the another one.

Extended Theory

More generally, if we have a Linear Programming problem as

     Max Ctx
     Subject to
     ∑nj=1aijxj ≤ bi, i ∈M1
     ∑nj=1aijxj = bi, i ∈M - M1
     xj ≥ 0, j ∈N1

That is, we have only restricted in sign someone of the variables and only in some of the restrictions we have inequality. Then the dual problem becomes:

     Min btw
     Subject to
     ∑mi=1ajiwi ≥ Ci, j ∈N1
     ∑mi=1ajiwi = Ci, j ∈N - N1
     wi ≥ 0, i ∈M1

ie, we have
1. An inequality constraint in the primal variable means restricted in sign variable in the dual.
2. Equality constraint in the primal, resulting in a variable unrestricted in sign in the dual.

Existence Theorem
a) If one of the two problems have finite optimal solution, the other also does.
b) If the primal problem has unbounded solution, then the dual has not feasible optimal solution (the converse is not true).

Corollary 1
Given a pair of dual problems, only one of these conditions is true:
1) Neither have feasible optimal solution.
2) One have solution feasible but is unbounded and the other has no solution.
3) They both have finite optimal solutions.

Corollary 2 (duality theorem)
Given a pair of dual problems, a necessary and sufficient condition for the primal optimal solution has finite x, is that there is a dual feasible solution w such that
Cx = bw

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2013-11-05 18:08:36
hello,very thanks

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