Machine Learning Parameter Systems, Noether Normalisations and Quasi-stable Positions

Co-author(s): Amir Hashemi and Mahshid Mirhashemi

Reference: Journal of Symbolic Computation, 126 (2025) 102345

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 [87] ). Based on a batch of 10.000 random ideals, we conclude that machine learning is much more efficient than human heuristics for this problem.

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