How does the No free lunch theorem apply in linear programming?
Given a linear Programm.
Calculate the optimal solution.
Then you can calculate with the simplex method the solution in finite steps.
My understand of this theorem is now averaged over all possible linear programm choosing any value for your variables is as good as doing the algorithm.
But you never know if your solution is optimal or even feasible with guessing and your are almost always wrong.