Projects per year
Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed 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.
|Title of host publication||Intelligent Computer Mathematics|
|Editors||Stephen M. Watt, James H. Davenport, Alan P. Sexton, Petr Sojka, Joesf Urban|
|Number of pages||16|
|Publication status||Published - 2014|
|Name||Lecture Notes in Artificial Intelligence|
- machine learning
- support vector machine
- symbolic computation
- cylindrical algebraic decomposition
- problem formulation
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- 1 Finished
1/10/11 → 31/12/15
Project: Research council