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Variable Ordering Selection for Cylindrical Algebraic Decomposition with Artificial Neural Networks


ABSTRACT: Cylindrical algebraic decomposition (CAD) is a fundamental tool in computational real algebraic geometry. Previous studies have shown that machine learning (ML) based approaches may outperform traditional heuristic ones on selecting the best variable ordering when the number of variables

SUBMITTER: Bigatti A 

PROVIDER: S-EPMC7340889 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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