Re: P=NP: Linear Programming Formulation of the TSP



1.- When N is small your formulation has a number of constraints that
are greater of 2^N so you can have an exact LP relaxation then. That
is, you formulation is only exact when its complexity is greater than
brute force search.

2.- The total flow is, of course, positive. It is forced to be one. I
state that your formulation allows for a solutions formed by adding
c.a.s.s. paths with negative and positive "flows", because the sum can
still have positive values in all the variables, and a total flow of 1.
I repeat here the constructive proof of my first reply in a more visual
way:

You can have a solution formed by considering the following terms:

- 1/M x ( 1 1 1 0 0 0 1 1 1 0 0 0 1 1 1 .......)
+ 1/M x ( 1 1 1 .............................................)
+ 1/M x ( ....................1 1 1 ........................ )
+ 1/M x ( .................... 1 1 1 .... )
........................................................................

_________________________________________
0 <= xi <= 1

It is obvious that, if each of the lines (terms) corresponds to a
c.a.s.s. path, the resulting sum is a feasible solution under your (any
TSP) LP formulation. It also easy to see that such solutions exist
because you can allways find a c.a.a.s. path that compensate the
negative values of the first term in one or more of the remaing
variables.
¿Can you prove no solution like that is a feasible solution to your
formulation?

The LP formulation constraints the total flow and the total sum for
each real valued variable of the solution to be positive, but it DOES
NOT constraint the contribution of each c.a.s.s. in a solution as the
shown above.

.



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