Machine Learning Parameter Systems, Noether
Normalisations and Quasi-stable Positions
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Co-author(s): Amir Hashemi and Mahshid Mirhashemi |
Reference: Journal of Symbolic Computation, 126 (2025) 102345 (23 pages) |
Description: We study how different machine learning models may be used to put ideals into
quasi-stable position (a generic position that shares many properties with the much
harder to reach generic initial ideal - see
[90]
). Based on a batch
of 10.000 random ideals, we conclude that machine learning is much more efficient
than human heuristics for this problem. |
PDF File: (416 kB)
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