SC-Square: Future Progress with Machine Learning?
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Abstract (arXiv)
The algorithms employed by our communities are often underspecified, and thus have multiple implementation choices, which do not effect the correctness of the output, but do impact the efficiency or even tractability of its production. In this extended abstract, to accompany a keynote talk at the 2021 SC-Square Workshop, we survey recent work (both the author's and from the literature) on the use of Machine Learning technology to improve algorithms of interest to SC-Square.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 38 references that are in the map. First posted on arXiv 9 Sep 2022.
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14 of 38 references.
References · 14
- 2019Deep Learning for Symbolic Mathematicscited by 266
- 2019Guiding High-Performance SAT Solvers with Unsat-Core Predictionscited by 61
- 2021Machine-Learning Mathematical Structurescited by 25
- 2020Learning selection strategies in Buchberger's algorithmcited by 20
- 2019The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)cited by 11
- 2014Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decompositioncited by 10
- 2019Comparing machine learning models to choose the variable ordering for cylindrical algebraic decompositioncited by 7
- 2017Improve SAT-solving with Machine Learningcited by 6
- 2018Using Machine Learning to Improve Cylindrical Algebraic Decompositioncited by 6
- 2019Algorithmically generating new algebraic features of polynomial systems for machine learningcited by 6
- 2019Improved cross-validation for classifiers that make algorithmic choices to minimise runtime without compromising output correctnesscited by 5
- 2020A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputscited by 4
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