Variable Ordering Selection for Cylindrical Algebraic Decomposition with Artificial Neural Networks
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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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