arXiv:2507.03159v2 Announce Type: replace Abstract: We present \texttt{MathOptAI.jl}, an open-source Julia library for embedding trained machine learning predictors into a JuMP model. \texttt{MathOptAI.jl} 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{Lux.jl} and \texttt{Flux.jl}, \texttt{MathOptAI.jl} uses Julia's Python interface to provide support for PyTorch models. When the PyTorch support is combined with \texttt
The proliferation of AI models across engineering and scientific domains necessitates tools for their transparent and optimizable integration into existing mathematical frameworks.
This development enables machine learning models to be embedded directly into optimization problems, making AI outputs verifiable, controllable, and subject to hard constraints, crucial for high-stakes applications.
Machine learning predictors can now be treated as first-class components within mathematical optimization models, moving beyond simple prediction to integrated decision-making.
- · Julia programming language ecosystem
- · Operations research
- · AI safety researchers
- · Industries requiring verifiable AI (e.g., aerospace, finance)
- · Proprietary ML optimization tools
- · Black-box AI systems without transparent integration
Increased adoption of Julia and JuMP for advanced scientific and engineering tasks involving AI.
Development of new optimization algorithms tailored for hybrid AI-optimization models.
Enhanced trust and deployability of AI in critical infrastructure and regulated industries due to embedded verifiability.
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Read at arXiv cs.LG