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Trained ML models are commonly embedded in optimization problems. In manycases, this leads to large-scale NLPs that are difficult to solve to globaloptimality. While ML models frequently lead to large problems, they alsoexhibit 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 specializedreformulations using mixed-integer programming or reduced space formulations.However, further work is needed to develop more efficient solution approachesand keep up with the rapid development of new ML model architectures.

Optimization with Trained Machine Learning Models Embedded