The Liouville Generator for Producing Integrable Expressions
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913478524010496 |
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| author | Barket, Rashid England, Matthew Gerhard, Jürgen |
| author_facet | Barket, Rashid England, Matthew Gerhard, Jürgen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_11631 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Liouville Generator for Producing Integrable Expressions Barket, Rashid England, Matthew Gerhard, Jürgen Symbolic Computation Machine Learning 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. |
| title | The Liouville Generator for Producing Integrable Expressions |
| topic | Symbolic Computation Machine Learning |
| url | https://arxiv.org/abs/2406.11631 |