  We are interested in assessing the use of neural networks as surrogate models
to approximate and minimize objective functions in optimization problems. While
neural networks are widely used for machine learning tasks such as
classification and regression, their application in solving optimization
problems has been limited. Our study begins by determining the best activation
function for approximating the objective functions of popular nonlinear
optimization test problems, and the evidence provided shows that~SiLU has the
best performance. We then analyze the accuracy of function value, gradient, and
Hessian approximations for such objective functions obtained through
interpolation/regression models and neural networks. When compared to
interpolation/regression models, neural networks can deliver competitive zero-
and first-order approximations (at a high training cost) but underperform on
second-order approximation. However, it is shown that combining a neural net
activation function with the natural basis for quadratic
interpolation/regression can waive the necessity of including cross terms in
the natural basis, leading to models with fewer parameters to determine.
Lastly, we provide evidence that the performance of a state-of-the-art
derivative-free optimization algorithm can hardly be improved when the gradient
of an objective function is approximated using any of the surrogate models
considered, including neural networks.
