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by yobbo
1556 days ago
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> asking it to give you back something in 1 second, with a lot of constraints, gives back solutions that are not feasible. Try to limit the search to only feasible solutions. > the algorithm makes use of the relationship between vertices But these do not stay they same between problem instances; anything you learn from solving one problem is not helpful when solving the next problem. |
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But nothing in ML stays the same between instances. The reason why ML works is because there are redundancies in the training set. I am pretty sure that distribution wise, set of TSP instances still has a lot of redundancies.
You would want your model to learn to execute something like MST or to approximate alpha-nearness or to remap the instance into a relaxation that when solved by a simpler algorithm results in a solution that, when remapped back to original, is feasible and optimal.