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Abstract
Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over realclosed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.
Original language  English 

Title of host publication  Intelligent Computer Mathematics 
Editors  Stephen M. Watt, James H. Davenport, Alan P. Sexton, Petr Sojka, Joesf Urban 
Publisher  Springer 
Pages  92107 
Number of pages  16 
Volume  8543 
ISBN (Electronic)  9783319084343 
ISBN (Print)  9783319084336 
DOIs  
Publication status  Published  2014 
Publication series
Name  Lecture Notes in Artificial Intelligence 

Publisher  Springer 
Keywords
 machine learning
 support vector machine
 symbolic computation
 cylindrical algebraic decomposition
 problem formulation
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Dive into the research topics of 'Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition'. Together they form a unique fingerprint.Projects
 1 Finished

Real Geometry and Connectedness via Triangular Description
Davenport, J., Bradford, R., England, M. & Wilson, D.
Engineering and Physical Sciences Research Council
1/10/11 → 31/12/15
Project: Research council