The solver generates a relaxed lower bound that indicates how far they could be from the global optimal solution. The moment that the lower bound improves enough to match a path they can guarantee that it's the global optimum
If you can prove, as they claim, that you have an algorithm that gives you the optimal solution (aside from the obvious, brute-forced one), you might be one stone throw away to make an argument for some P == NP, that would be HUGE.
But it seems that some people get offended when you tell them their perpetual motion machines are not real.
The branch-and-bound algorithm does provide a proven optimal solution. This does not mean that P=NP because the size of the proof is not bounded by a polynomial in the input size, and neither is the algorithm runtime. Also, Euclidean TSP is known to be easier than TSP on arbitrary graphs: there are polynomial-time approximation schemes that can produce solutions with an (1+epsilon) factor of the optimum in polynomial time, for any value of epsilon. Thus it is not surprising that a proof of full optimality can be constructed for some instances.
The solver generates a relaxed lower bound that indicates how far they could be from the global optimal solution. The moment that the lower bound improves enough to match a path they can guarantee that it's the global optimum
(I don't know)
But I would guess the answer is "no".
If you can prove, as they claim, that you have an algorithm that gives you the optimal solution (aside from the obvious, brute-forced one), you might be one stone throw away to make an argument for some P == NP, that would be HUGE.
But it seems that some people get offended when you tell them their perpetual motion machines are not real.
The branch-and-bound algorithm does provide a proven optimal solution. This does not mean that P=NP because the size of the proof is not bounded by a polynomial in the input size, and neither is the algorithm runtime. Also, Euclidean TSP is known to be easier than TSP on arbitrary graphs: there are polynomial-time approximation schemes that can produce solutions with an (1+epsilon) factor of the optimum in polynomial time, for any value of epsilon. Thus it is not surprising that a proof of full optimality can be constructed for some instances.