Applying Machine Learning to Heuristics for Real Polynomial Constraint Solving

DOI10.1007/978-3-030-52200-1_29zbMath1503.68307OpenAlexW3042061643MaRDI QIDQ5041067

Christopher W. Brown, Glenn Christopher Daves

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_29

zbMATH Keywords

machine learningnonlinear polynomial constraints

Mathematics Subject Classification ID

Symbolic computation and algebraic computation (68W30) Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Computational real algebraic geometry (14Q30)

Related Items (2)

Levelwise construction of a single cylindrical algebraic cell ⋮ New heuristic to choose a cylindrical algebraic decomposition variable ordering motivated by complexity analysis

Uses Software

• SMT-LIB
• Keras

Cites Work

• Polynomial constraints and unsat cores in \textsc{Tarski}
• Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition
• From simplification to a partial theory solver for non-linear real polynomial constraints
• Efficient Subformula Orders for Real Quantifier Elimination of Non-prenex Formulas
• Open Non-uniform Cylindrical Algebraic Decompositions
• Applying Machine Learning to the Problem of Choosing a Heuristic to Select the Variable Ordering for Cylindrical Algebraic Decomposition

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