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Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition


Reference:

Huang, Z., England, M., Wilson, D., Davenport, J. H., Paulson, L. and Bridge, J., 2014. Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition. In: Watt, S. M., Davenport, J. H., Sexton, A. P., Sojka, P. and Urban, J., eds. Intelligent Computer Mathematics.Vol. 8543. Springer, pp. 92-107. (Lecture Notes in Artificial Intelligence)

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    Official URL:

    http://cicm-conference.org/2014/cicm.php?event=&menu=general

    Abstract

    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.

    Details

    Item Type Book Sections
    CreatorsHuang, Z., England, M., Wilson, D., Davenport, J. H., Paulson, L. and Bridge, J.
    EditorsWatt, S. M., Davenport, J. H., Sexton, A. P., Sojka, P. and Urban, J.
    DOI10.1007/978-3-319-08434-3_8
    Uncontrolled Keywordsmachine learning,support vector machine,symbolic computation,cylindrical algebraic decomposition,problem formulation
    DepartmentsFaculty of Science > Computer Science
    Publisher StatementCADMachineLearning.pdf: The final publication will be available from http://link.springer.com/
    StatusPublished
    ID Code39232

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