Constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems

• URL: http://arxiv.org/abs/2404.17508v1
• Date: Fri, 26 Apr 2024 16:20:04 GMT
• Title: Constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems
• Authors: Dorian Florescu, Matthew England,
• Abstract summary: We present a new methodology for utilising machine learning technology in symbolic computation research. We explain how a well known human-designed variable ordering in cylindrical decomposition may be represented as a constrained neural network. This allows us to then use machine learning methods to further optimise, leading to new networks of similar size as the original human-designed one.
• Score: 2.8402080392117757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a new methodology for utilising machine learning technology in symbolic computation research. We explain how a well known human-designed heuristic to make the choice of variable ordering in cylindrical algebraic decomposition may be represented as a constrained neural network. This allows us to then use machine learning methods to further optimise the heuristic, leading to new networks of similar size, representing new heuristics of similar complexity as the original human-designed one. We present this as a form of ante-hoc explainability for use in computer algebra development.

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