Machine Learning for Mathematical Software

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    44 Downloads (Pure)


    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
    Number of pages10
    ISBN (Electronic)978-3-319-96418-8
    ISBN (Print)978-3-319-96417-1
    Publication statusPublished - 14 Jul 2018
    EventInternational Congress on Mathematical Software: ICMS 2018 - University of Notre Dame, South Bend, United States
    Duration: 24 Jul 201827 Jul 2018
    Conference number: 6

    Publication series

    NameLecture Notes in Computer Science


    ConferenceInternational Congress on Mathematical Software
    Abbreviated titleICMS 2018
    Country/TerritoryUnited States
    CitySouth Bend
    Internet address

    Bibliographical note

    The final publication is available at Springer via

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


    • Machine Learning
    • Mathematical Software


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