Using Machine Learning to Improve Cylindrical Algebraic Decomposition

Cylindrical Algebraic Decomposition (CAD) is a key tool in computational algebraic geometry, best known as a procedure to enable Quantifier Elimination over real-closed fields. However, it has a worst case complexity doubly exponential in the size of the input, which is often encountered in practice...

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Veröffentlicht in:	Mathematics in computer science 2019-12, Vol.13 (4), p.461-488
Hauptverfasser:	Huang, Zongyan, England, Matthew, Wilson, David J., Bridge, James, Davenport, James H., Paulson, Lawrence C.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algebra Algorithms Artificial intelligence Complexity Computer Science Decomposition Heuristic Machine learning Mathematics Mathematics and Statistics Preconditioning Software Support vector machines
Online-Zugang:	Volltext
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