The Liouville Generator for Producing Integrable Expressions

Rashid Barket, Matthew England, Juergen Gerhard

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

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Abstract

There has been a growing need to devise processes that can create comprehensive datasets in the world of Computer Algebra, both for accurate benchmarking and for new intersections with machine learning technology. We present here a method to generate integrands that are guaranteed to be integrable, dubbed the LIOUVILLE method. It is based on Liouville's theorem and the Parallel Risch Algorithm for symbolic integration. We show that this data generation method retains the best qualities of previous data generation methods, while overcoming some of the issues built into that prior work. The LIOUVILLE generator is able to generate sufficiently complex and realistic integrands, and could be used for benchmarking or machine learning training tasks related to symbolic integration.
Original languageEnglish
Title of host publicationComputer Algebra in Scientific Computing
Subtitle of host publication26th International Workshop, CASC 2024, Rennes, France, September 2–6, 2024, Proceedings
EditorsFrançois Boulier, Chenqi Mou, Timur M. Sadykov, Evgenii V. Vorozhtsov
PublisherSpringer
Chapter4
Pages47-62
Number of pages16
Volume14938
Edition1
ISBN (Electronic)978-3-031-69070-9
ISBN (Print)978-3-031-69069-3
DOIs
Publication statusPublished - 21 Aug 2024
Event26th International Computer Algebra in Scientific Computing - Rennes, France
Duration: 2 Sept 20246 Sept 2024
https://casc-conference.org/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Computer Algebra in Scientific Computing
Abbreviated titleCASC 2024
Country/TerritoryFrance
CityRennes
Period2/09/246/09/24
Internet address

Bibliographical note

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-031-69070-9_4

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Funding

The authors would like to thank James H. Davenport for helpful discussion on the Parallel Risch algorithm. Matthew England is supported by EPSRC Project EP/T015748/1, Pushing Back the Doubly-Exponential Wall of Cylindrical Algebraic Decomposition (the DEWCAD Project). Rashid Barket is supported by a scholarship provided by Maplesoft and Coventry University.

FundersFunder number
Engineering and Physical Sciences Research CouncilEP/T015748/1
Maplesoft
Coventry University

Keywords

  • Computer algebra
  • Symbolic computation
  • Symbolic integration
  • Data generation
  • Machine learning

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