Tal vez vale la pena hablar a través de donde el doble. Esto tomará un tiempo, pero esperemos que el doble no parece tan misterioso cuando hayamos terminado.
Supongamos que queremos utilizar las restricciones del primal como una forma de encontrar un límite superior en el valor óptimo de la primaria. Si multiplicamos la primera restricción por 9, la segunda restricción por 1, y añadirlos juntos, llegamos 9(2x1−x2)+1(x1+3x2) para el lado izquierdo y 9(1)+1(9) para la mano derecha. Desde la primera restricción es una igualdad y la segunda es una desigualdad, esto implica 19x1−6x2≤18.
Pero desde x1≥0, también es cierto que 5x1≤19x1, y por lo 5x1−6x2≤19x1−6x2≤18.
Por lo tanto, 18 es un-límite superior en el valor óptimo del problema primal.
Seguro que podemos hacer algo mejor que eso, sin embargo. En lugar de sólo una suposición 9 1 como los multiplicadores, dejemos que sean variables. Por lo tanto estamos buscando multiplicadores y1 y2 a la fuerza 5x1−6x2≤y1(2x1−x2)+y2(x1+3x2)≤y1(1)+y2(9).
Now, in order for this pair of inequalities to hold, what has to be true about y1 and y2? Let's take the two inequalities one at a time.
The first inequality: 5x1−6x2≤y1(2x1−x2)+y2(x1+3x2)
We have to track the coefficients of the x1 and x2 variables separately. First, we need the total x1 coefficient on the right-hand side to be at least 5. Getting exactly 5 would be great, but since x1≥0, anything larger than 5 would also satisfy the inequality for x1. Mathematically speaking, this means that we need 2y1+y2≥5.
On the other hand, to ensure the inequality for the x2 variable we need the total x2 coefficient on the right-hand side to be exactly −6. Since x2 could be positive, we can't go lower than −6, and since x2 could be negative, we can't go higher than −6 (as the negative value for x2 would flip the direction of the inequality). So for the first inequality to work for the x2 variable, we've got to have −y1+3y2=−6.
The second inequality: y1(2x1−x2)+y2(x1+3x2)≤y1(1)+y2(9)
Here we have to track the y1 and y2 variables separately. The y1 variables come from the first constraint, which is an equality constraint. It doesn't matter if y1 is positive or negative, the equality constraint still holds. Thus y1 is unrestricted in sign. However, the y2 variable comes from the second constraint, which is a less-than-or-equal to constraint. If we were to multiply the second constraint by a negative number that would flip its direction and change it to a greater-than-or-equal constraint. To keep with our goal of upper-bounding the primal objective, we can't let that happen. So the y2 variable can't be negative. Thus we must have y2≥0.
Finally, we want to make the right-hand side of the second inequality as small as possible, as we want the tightest upper-bound possible on the primal objective. So we want to minimize y1+9y2.
Putting all of these restrictions on y1 and y2 together we find that the problem of using the primal's constraints to find the best upper-bound on the optimal primal objective entails solving the following linear program:
Minimize y1+9y2subject to 2y1+y2≥5−y1+3y2=−6y2≥0.
Y eso es el doble.
Es probablemente vale la pena resumir las implicaciones de este argumento para todas las posibles formas del primal y dual. La siguiente tabla es tomada de la página. 214 Introducción a la investigacion de Operaciones, 8th edition, por Hillier y Lieberman. Se refieren a esto como el hijo de puta de método, donde SOB representa Sensible, Raro o Extraño, dependiendo de la probabilidad de que uno podría encontrar que la limitación particular o variable de la restricción en la maximización o minimización del problema.
Primal Problem Dual Problem
(or Dual Problem) (or Primal Problem)
Maximization Minimization
Sensible <= constraint paired with nonnegative variable
Odd = constraint paired with unconstrained variable
Bizarre >= constraint paired with nonpositive variable
Sensible nonnegative variable paired with >= constraint
Odd unconstrained variable paired with = constraint
Bizarre nonpositive variable paired with <= constraint