Machine Learning to Improve Cylindrical Algebraic Decomposition in Maple

Matthew England, Dorian Florescu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

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
PublisherSpringer
Pages330-333
Number of pages4
ISBN (Print)9783030412579
DOIs
Publication statusPublished - 2020
Event3rd Maple Conference, MC 2019 - Waterloo, Canada
Duration: 15 Oct 201917 Oct 2019

Publication series

NameCommunications in Computer and Information Science
Volume1125
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd Maple Conference, MC 2019
CountryCanada
CityWaterloo
Period15/10/1917/10/19

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • Cite this

    England, M., & Florescu, D. (2020). Machine Learning to Improve Cylindrical Algebraic Decomposition in Maple. In J. Gerhard, & I. Kotsireas (Eds.), Maple in Mathematics Education and Research - 3rd Maple Conference, MC 2019, Proceedings (pp. 330-333). (Communications in Computer and Information Science; Vol. 1125 ). Springer. https://doi.org/10.1007/978-3-030-41258-6_25