### Abstract

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 language | English |
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Title of host publication | Mathematical Software |

Subtitle of host publication | Proceedings of the International Congress on Mathematical Software (ICMS 2018) |

Editors | J.H. Davenport, M. Kauers, G. Labahn, J. Urban |

Publisher | Springer |

Pages | 165-174 |

Number of pages | 10 |

ISBN (Electronic) | 978-3-319-96418-8 |

ISBN (Print) | 978-3-319-96417-1 |

DOIs | |

Publication status | Published - 14 Jul 2018 |

Event | International Congress on Mathematical Software: ICMS 2018 - University of Notre Dame, South Bend, United States Duration: 24 Jul 2018 → 27 Jul 2018 Conference number: 6 http://icms-conference.org/2018/ |

### Publication series

Name | Lecture Notes in Computer Science |
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Publisher | Springer |

Volume | 10931 |

### Conference

Conference | International Congress on Mathematical Software |
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Abbreviated title | ICMS 2018 |

Country | United States |

City | South Bend |

Period | 24/07/18 → 27/07/18 |

Internet address |

### Fingerprint

### Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-96418-8_20Part of the Lecture Notes in Computer Science book series (LNCS, volume 10931)

ISSN 0302-9743

### Keywords

- Machine Learning
- Mathematical Software

### Cite this

*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.

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

*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

}

TY - GEN

T1 - Machine Learning for Mathematical Software

AU - England, Matthew

N1 - 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

PY - 2018/7/14

Y1 - 2018/7/14

N2 - 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.

AB - 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.

KW - Machine Learning

KW - Mathematical Software

U2 - 10.1007/978-3-319-96418-8_20

DO - 10.1007/978-3-319-96418-8_20

M3 - Conference proceeding

SN - 978-3-319-96417-1

T3 - Lecture Notes in Computer Science

SP - 165

EP - 174

BT - Mathematical Software

A2 - Davenport, J.H.

A2 - Kauers, M.

A2 - Labahn, G.

A2 - Urban, J.

PB - Springer

ER -