# SC-Square: Future Progress with Machine Learning?

arXiv: https://arxiv.org/abs/2209.04361 · HTML: https://mlsanity.com/paper/2209.04361/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 9 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Learning for Symbolic Mathematics](https://mlsanity.com/paper/1912.01412/) (2019)
- [Guiding High-Performance SAT Solvers with Unsat-Core Predictions](https://mlsanity.com/paper/1903.04671/) (2019)
- [Machine-Learning Mathematical Structures](https://mlsanity.com/paper/2101.06317/) (2021)
- [Learning selection strategies in Buchberger's algorithm](https://mlsanity.com/paper/2005.01917/) (2020)
- [The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)](https://mlsanity.com/paper/1912.05752/) (2019)
- [Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition](https://mlsanity.com/paper/1404.6369/) (2014)
- [Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition](https://mlsanity.com/paper/1904.11061/) (2019)
- [Improve SAT-solving with Machine Learning](https://mlsanity.com/paper/1710.11204/) (2017)
- [Using Machine Learning to Improve Cylindrical Algebraic Decomposition](https://mlsanity.com/paper/1804.10520/) (2018)
- [Algorithmically generating new algebraic features of polynomial systems for machine learning](https://mlsanity.com/paper/1906.01455/) (2019)
- [Improved cross-validation for classifiers that make algorithmic choices to minimise runtime without compromising output correctness](https://mlsanity.com/paper/1911.12672/) (2019)
- [A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs](https://mlsanity.com/paper/2005.11251/) (2020)
- [Deciding the Consistency of Non-Linear Real Arithmetic Constraints with a Conflict Driven Search Using Cylindrical Algebraic Coverings](https://mlsanity.com/paper/2003.05633/) (2020)
- [Symbolic integration by integrating learning models with different strengths and weaknesses](https://mlsanity.com/paper/2103.05497/) (2021)

## 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.