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

arXiv:2404.17508v1 Announce Type: cross
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 …

abstract algebra arxiv computation computer cs.lg cs.sc human machine machine learning machine learning technology methodology network networks neural network neural networks research systems technology type