Machine Learning to Improve Cylindrical Algebraic Decomposition in Maple

Matthew England, Dorian Florescu

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

1 Citation (Scopus)


Many algorithms in computer algebra systems can have their performance improved through the careful selection of options that do not affect the correctness of the end result. Machine Learning (ML) is suited for making such choices: the challenge is to select an appropriate ML model, training dataset, and scheme to identify features of the input. In this extended abstract we survey our recent work to use ML to select the variable ordering for Cylindrical Algebraic Decomposition (CAD) in Maple: experimentation with a variety of models, and a new flexible framework for generating ML features from polynomial systems. We report that ML allows for significantly faster CAD than with the default Maple ordering, and discuss some initial results on adaptability.

Original languageEnglish
Title of host publicationMaple in Mathematics Education and Research - 3rd Maple Conference, MC 2019, Proceedings
EditorsJürgen Gerhard, Ilias Kotsireas
Number of pages4
ISBN (Print)9783030412579
Publication statusPublished - 2020
Event3rd Maple Conference, MC 2019 - Waterloo, Canada
Duration: 15 Oct 201917 Oct 2019

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference3rd Maple Conference, MC 2019

Bibliographical note

The authors are supported by EPSRC Project EP/R019622/1: Embedding Machine

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)


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