Comparing machine learning models to choose the variable ordering for
cylindrical algebraic decomposition

v1v2 (latest)

Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition

24 April 2019

Matthew England

Dorian Florescu

ArXiv (abs)PDF HTML

Papers citing "Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition"

8 / 8 papers shown

Title
Constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems Dorian Florescu Matthew England 37 1 0 26 Apr 2024
Lessons on Datasets and Paradigms in Machine Learning for Symbolic Computation: A Case Study on CAD Tereso del Río Matthew England 42 1 0 24 Jan 2024
Data Augmentation for Mathematical Objects Tereso Del Rio Almajano Matthew England 29 4 0 13 Jul 2023
Explainable AI Insights for Symbolic Computation: A case study on selecting the variable ordering for cylindrical algebraic decomposition Lynn Pickering Tereso Del Rio Almajano Matthew England Kelly Cohen 40 13 0 24 Apr 2023
Revisiting Variable Ordering for Real Quantifier Elimination using Machine Learning John Hester Briland Hitaj Grant Passmore S. Owre N. Shankar Eric Yeh 33 1 0 27 Feb 2023
A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs Dorian Florescu Matthew England 8 7 0 22 May 2020
Improved cross-validation for classifiers that make algorithmic choices to minimise runtime without compromising output correctness Dorian Florescu Matthew England 39 12 0 28 Nov 2019
Algorithmically generating new algebraic features of polynomial systems for machine learning Dorian Florescu Matthew England 29 19 0 03 Jun 2019