Variable Ordering Selection for Cylindrical Algebraic Decomposition with Artificial Neural Networks

DOI10.1007/978-3-030-52200-1_28zbMath1503.68310OpenAlexW3041150369MaRDI QIDQ5041065

Haoyu Chi, Zhangpeng Zhu, Changbo Chen

Publication date: 13 October 2022

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-52200-1_28

zbMATH Keywords

neural networkcylindrical algebraic decompositionmachine learningvariable ordering

Mathematics Subject Classification ID

Symbolic computation and algebraic computation (68W30) Learning and adaptive systems in artificial intelligence (68T05) Computational real algebraic geometry (14Q30)

Related Items (3)

Explainable AI insights for symbolic computation: a case study on selecting the variable ordering for cylindrical algebraic decomposition ⋮ Choosing better variable orderings for cylindrical algebraic decomposition via exploiting chordal structure ⋮ New heuristic to choose a cylindrical algebraic decomposition variable ordering motivated by complexity analysis

Uses Software

• RegularChains
• CylindricalAlgebraicDecompose

Cites Work

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• Computing cylindrical algebraic decomposition via triangular decomposition
• Efficient projection orders for CAD
• Improved Cross-Validation for Classifiers that Make Algorithmic Choices to Minimise Runtime Without Compromising Output Correctness
• Cylindrical Algebraic Decomposition I: The Basic Algorithm
• Quantifier elimination by cylindrical algebraic decomposition based on regular chains
• Improved projection for cylindrical algebraic decomposition
• Truth table invariant cylindrical algebraic decomposition

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