Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition
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Publication:2287902
DOI10.1007/978-3-030-23250-4_7zbMath1428.68399arXiv1904.11061OpenAlexW2941893175MaRDI QIDQ2287902
Dorian Florescu, Matthew England
Publication date: 22 January 2020
Full work available at URL: https://arxiv.org/abs/1904.11061
symbolic computationcomputer algebracylindrical algebraic decompositionmachine learningnon-linear real arithmetic
Symbolic computation and algebraic computation (68W30) Learning and adaptive systems in artificial intelligence (68T05)
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Variable Ordering Selection for Cylindrical Algebraic Decomposition with Artificial Neural Networks ⋮ Applying Machine Learning to Heuristics for Real Polynomial Constraint Solving ⋮ A Machine Learning Based Software Pipeline to Pick the Variable Ordering for Algorithms with Polynomial Inputs ⋮ An augmented MetiTarski dataset for real quantifier elimination using machine learning ⋮ Explainable AI insights for symbolic computation: a case study on selecting the variable ordering for cylindrical algebraic decomposition ⋮ Deciding the consistency of non-linear real arithmetic constraints with a conflict driven search using cylindrical algebraic coverings ⋮ Choosing better variable orderings for cylindrical algebraic decomposition via exploiting chordal structure ⋮ Improved Cross-Validation for Classifiers that Make Algorithmic Choices to Minimise Runtime Without Compromising Output Correctness ⋮ New heuristic to choose a cylindrical algebraic decomposition variable ordering motivated by complexity analysis
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