I'm researching ways of solving constrained optimization problems on a cloud platform. I stumbled across this:
Where they claim that gradient descent (and potentially even Tensorflow) can be used to solve MIP.
Is that a good idea?
My understanding is that even though MIP is NP-Hard, it has enough structure that you are better off using Branch-and-X and other linear specific solvers than search methods or heuristic methods. Is this correct?