Optimizing Computer Algebra Systems through Constrained Neural Networks and Interpretable Heuristics

A new methodology for utilizing machine learning to optimize symbolic computation research by representing a well-known human-designed heuristic as a constrained neural network, and then using machine learning to further optimize the heuristic, leading to new networks of similar size and complexity as the original.