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 language | English |
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Title of host publication | Computer Algebra in Scientific Computing |
Subtitle of host publication | 26th International Workshop, CASC 2024, Rennes, France, September 2–6, 2024, Proceedings |
Editors | François Boulier, Chenqi Mou, Timur M. Sadykov, Evgenii V. Vorozhtsov |
Publisher | Springer |
Chapter | 4 |
Pages | 47-62 |
Number of pages | 16 |
Volume | 14938 |
Edition | 1 |
ISBN (Electronic) | 978-3-031-69070-9 |
ISBN (Print) | 978-3-031-69069-3 |
DOIs | |
Publication status | Published - 21 Aug 2024 |
Event | 26th International Computer Algebra in Scientific Computing - Rennes, France Duration: 2 Sept 2024 → 6 Sept 2024 https://casc-conference.org/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 26th International Computer Algebra in Scientific Computing |
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Abbreviated title | CASC 2024 |
Country/Territory | France |
City | Rennes |
Period | 2/09/24 → 6/09/24 |
Internet address |
Keywords
- Computer algebra
- Symbolic computation
- Symbolic integration
- Data generation
- Machine learning