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 Jg. 13; H. 4; S. 461 - 488
Hauptverfasser:	Huang, Zongyan, England, Matthew, Wilson, David J., Bridge, James, Davenport, James H., Paulson, Lawrence C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2019 Springer Nature B.V
Schlagworte:	Algebra Algorithms Artificial intelligence Complexity Computer Science Decomposition Heuristic Machine learning Mathematics Mathematics and Statistics Preconditioning Software Support vector machines Gröbner Basis Computer Algebra Parameter Selection 68W30 (Symbolic Computation and Algebraic Computation) Symbolic Computation Support Vector Machine 68T05 (Learning and Adaptive Systems) Machine Learning Cylindrical Algebraic Decomposition
ISSN:	1661-8270, 1661-8289
Online-Zugang:	Volltext
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