Operation research gives us efficient algorithms that we can use to solve optimization problems by finding the global optimum (the global minimum point) if the problems are expressed as a function with well-defined characteristics (for instance, convex optimization requires the function to be a convex).
Artificial neural networks are universal function approximators; therefore, it is not possible to make assumptions about the shape of the function the neural network is approximating. Moreover, the most common optimization methods exploit geometric considerations, but we know from Chapter 1, What is Machine Learning?, that geometry works in an unusual way when dimensionality is high due to the curse of dimensionality.
For these reasons, it is not possible to use operation research methods that are capable of finding the global optimum of an optimization (minimization...
