  Trained ML models are commonly embedded in optimization problems. In many
cases, this leads to large-scale NLPs that are difficult to solve to global
optimality. While ML models frequently lead to large problems, they also
exhibit homogeneous structures and repeating patterns (e.g., layers in ANNs).
Thus, specialized solution strategies can be used for large problem classes.
Recently, there have been some promising works proposing specialized
reformulations using mixed-integer programming or reduced space formulations.
However, further work is needed to develop more efficient solution approaches
and keep up with the rapid development of new ML model architectures.
