Machine Learning to Improve Cylindrical Algebraic Decomposition in Maple

Matthew England; Dorian Florescu

doi:10.1007/978-3-030-41258-6_25

Machine Learning to Improve Cylindrical Algebraic Decomposition in Maple

Matthew England, Dorian Florescu

College of Engineering, Environment & Science

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	Maple in Mathematics Education and Research - 3rd Maple Conference, MC 2019, Proceedings
Editors	Jürgen Gerhard, Ilias Kotsireas
Publisher	Springer
Pages	330-333
Number of pages	4
ISBN (Print)	9783030412579
DOIs	https://doi.org/10.1007/978-3-030-41258-6_25
Publication status	Published - 2020
Event	3rd Maple Conference, MC 2019 - Waterloo, Canada Duration: 15 Oct 2019 → 17 Oct 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1125
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	3rd Maple Conference, MC 2019
Country/Territory	Canada
City	Waterloo
Period	15/10/19 → 17/10/19

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

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-030-41258-6_25

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

Machine Learning to Improve Cylindrical Algebraic Decomposition in Maple. / England, Matthew; Florescu, Dorian.
Maple in Mathematics Education and Research - 3rd Maple Conference, MC 2019, Proceedings. ed. / Jürgen Gerhard; Ilias Kotsireas. Springer, 2020. p. 330-333 (Communications in Computer and Information Science; Vol. 1125 ).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

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. Communications in Computer and Information Science, vol. 1125 , Springer, pp. 330-333, 3rd Maple Conference, MC 2019, Waterloo, Canada, 15/10/19. https://doi.org/10.1007/978-3-030-41258-6_25

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Machine Learning to Improve Cylindrical Algebraic Decomposition in Maple

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this