Exploring Alternative Machine Learning Models for Variable Ordering in Cylindrical Algebraic Decomposition
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64529-7_20
Reference16 articles.
1. Bradford, R., Davenport, J., England, M., Wilson, D.: Optimising problem formulation for cylindrical algebraic decomposition. In: Carette, J., et al. (eds.) Proceedings CICM 2013, pp. 19–34 (2013). https://doi.org/10.1007/978-3-642-39320-4_2
2. Brown, C.: Tutorial handout at ISSAC 2004 (2004). https://www.usna.edu/Users/cs/wcbrown/research/ISSAC04/Tutorial.html
3. Brown, C., Davenport, J.: The complexity of quantifier elimination and cylindrical algebraic decomposition. In: Brown, C. (ed.) Proceedings of ISSAC 2007, pp. 54–60 (2007).https://doi.org/10.1145/1277548.1277557
4. Chen, C., et al.: Computing the real solutions of polynomial systems with the RegularChains library in MAPLE: ISSAC 2011 software demo. Commun. Comput. Algebra 3(45), 166–168 (2011). https://doi.org/10.1145/2110170.2110174
5. Lecture Notes in Computer Science;GE Collins,1975
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