Lessons on Datasets and Paradigms in Machine Learning for Symbolic Computation: A Case Study on CAD
This work addresses resource optimization in symbolic computation algorithms, though it is incremental as it builds on existing datasets and methods.
The study tackled the problem of selecting optimal variable orderings for cylindrical algebraic decomposition in symbolic computation by analyzing and augmenting an imbalanced dataset, which improved machine learning results by 28% and 38% on average, and explored recasting the methodology from classification to regression to broaden applicability.
Symbolic Computation algorithms and their implementation in computer algebra systems often contain choices which do not affect the correctness of the output but can significantly impact the resources required: such choices can benefit from having them made separately for each problem via a machine learning model. This study reports lessons on such use of machine learning in symbolic computation, in particular on the importance of analysing datasets prior to machine learning and on the different machine learning paradigms that may be utilised. We present results for a particular case study, the selection of variable ordering for cylindrical algebraic decomposition, but expect that the lessons learned are applicable to other decisions in symbolic computation. We utilise an existing dataset of examples derived from applications which was found to be imbalanced with respect to the variable ordering decision. We introduce an augmentation technique for polynomial systems problems that allows us to balance and further augment the dataset, improving the machine learning results by 28\% and 38\% on average, respectively. We then demonstrate how the existing machine learning methodology used for the problem $-$ classification $-$ might be recast into the regression paradigm. While this does not have a radical change on the performance, it does widen the scope in which the methodology can be applied to make choices.