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I want to use artificial neural network(ANN) for solving non-convex optimisation problems. How do I map the constraints and the objective functions in the ANN architecture?

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    $\begingroup$ can you share more details about the function you're trying to optimize? neural nets do solve non-convex problems - the standard "sum-of-squares" (= max-likelihood if the target vectors are normally distributed) error function is non-convex (because the function computed by the ANN is nonlinear), and what you've converged to after training is a local minimum. $\endgroup$ – GoHokies Aug 2 '18 at 20:19

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