Machine Learning for Mathematical Software

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

While there has been some discussion on how Symbolic Computation could be used for AI there is little literature on applications in the other direction. However, recent results for quantifier elimination suggest that, given enough example problems, there is scope for machine learning tools like Support Vector Machines to improve the performance of Computer Algebra Systems. We survey the author’s own work and similar applications for other mathematical software.

It may seem that the inherently probabilistic nature of machine learning tools would invalidate the exact results prized by mathematical software. However, algorithms and implementations often come with a range of choices which have no effect on the mathematical correctness of the end result but a great effect on the resources required to find it, and thus here, machine learning can have a significant impact.
Original languageEnglish
Title of host publicationMathematical Software
Subtitle of host publicationProceedings of the International Congress on Mathematical Software (ICMS 2018)
EditorsJ.H. Davenport, M. Kauers, G. Labahn, J. Urban
PublisherSpringer
Pages165-174
Number of pages10
ISBN (Electronic)978-3-319-96418-8
ISBN (Print)978-3-319-96417-1
DOIs
Publication statusPublished - 14 Jul 2018
EventInternational Congress on Mathematical Software - University of Notre Dame, South Bend, United States
Duration: 24 Jul 201827 Jul 2018
Conference number: 6
http://icms-conference.org/2018/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10931

Conference

ConferenceInternational Congress on Mathematical Software
Abbreviated titleICMS 2018
CountryUnited States
CitySouth Bend
Period24/07/1827/07/18
Internet address

Fingerprint

Learning systems
Algebra
Support vector machines

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-96418-8_20

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10931)
ISSN 0302-9743

Keywords

  • Machine Learning
  • Mathematical Software

Cite this

England, M. (2018). Machine Learning for Mathematical Software. In J. H. Davenport, M. Kauers, G. Labahn, & J. Urban (Eds.), Mathematical Software : Proceedings of the International Congress on Mathematical Software (ICMS 2018) (pp. 165-174). (Lecture Notes in Computer Science; Vol. 10931). Springer. https://doi.org/10.1007/978-3-319-96418-8_20

Machine Learning for Mathematical Software. / England, Matthew.

Mathematical Software : Proceedings of the International Congress on Mathematical Software (ICMS 2018). ed. / J.H. Davenport; M. Kauers; G. Labahn; J. Urban. Springer, 2018. p. 165-174 (Lecture Notes in Computer Science; Vol. 10931).

Research output: Chapter in Book/Report/Conference proceedingChapter

England, M 2018, Machine Learning for Mathematical Software. in JH Davenport, M Kauers, G Labahn & J Urban (eds), Mathematical Software : Proceedings of the International Congress on Mathematical Software (ICMS 2018). Lecture Notes in Computer Science, vol. 10931, Springer, pp. 165-174, International Congress on Mathematical Software, South Bend, United States, 24/07/18. https://doi.org/10.1007/978-3-319-96418-8_20
England M. Machine Learning for Mathematical Software. In Davenport JH, Kauers M, Labahn G, Urban J, editors, Mathematical Software : Proceedings of the International Congress on Mathematical Software (ICMS 2018). Springer. 2018. p. 165-174. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-96418-8_20
England, Matthew. / Machine Learning for Mathematical Software. Mathematical Software : Proceedings of the International Congress on Mathematical Software (ICMS 2018). editor / J.H. Davenport ; M. Kauers ; G. Labahn ; J. Urban. Springer, 2018. pp. 165-174 (Lecture Notes in Computer Science).
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