MathOptAI.jl: Embed trained machine learning predictors into JuMP models

We present \texttt{this http URL}, an open-source Julia library for embedding trained machine learning predictors into a JuMP model. \texttt{this http URL} can embed a wide variety of neural networks, decision trees, and Gaussian Processes into a larger mathematical optimization model. In addition to interfacing a range of Julia-based machine learning libraries such as \texttt{this http URL} and \texttt{this http URL}, \texttt{this http URL} uses Julia's Python interface to provide support for PyTorch models. When the PyTorch support is combined with \texttt{this http URL}'s gray-box formulation, the function, Jacobian, and Hessian evaluations associated with the PyTorch model are offloaded to the GPU in Python, while the rest of the nonlinear oracles are evaluated on the CPU in Julia. \MathOptAI is available atthis https URLunder a BSD-3 license.