### Abstract

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 | 92-107 |

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 |
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Publisher | Springer |

### Fingerprint

### Keywords

- machine learning
- support vector machine
- symbolic computation
- cylindrical algebraic decomposition
- problem formulation

### Cite this

*Intelligent Computer Mathematics*(Vol. 8543, pp. 92-107). (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-319-08434-3_8

**Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition.** / Huang, Zongyan; England, Matthew; Wilson, David; Davenport, James H.; Paulson, Lawrence; Bridge, James.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Intelligent Computer Mathematics.*vol. 8543, Lecture Notes in Artificial Intelligence, Springer, pp. 92-107. https://doi.org/10.1007/978-3-319-08434-3_8

}

TY - CHAP

T1 - Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

AU - Huang, Zongyan

AU - England, Matthew

AU - Wilson, David

AU - Davenport, James H.

AU - Paulson, Lawrence

AU - Bridge, James

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - machine learning

KW - support vector machine

KW - symbolic computation

KW - cylindrical algebraic decomposition

KW - problem formulation

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

UR - http://dx.doi.org/10.1007/978-3-319-08434-3_8

U2 - 10.1007/978-3-319-08434-3_8

DO - 10.1007/978-3-319-08434-3_8

M3 - Chapter

SN - 9783319084336

VL - 8543

T3 - Lecture Notes in Artificial Intelligence

SP - 92

EP - 107

BT - Intelligent Computer Mathematics

A2 - Watt, Stephen M.

A2 - Davenport, James H.

A2 - Sexton, Alan P.

A2 - Sojka, Petr

A2 - Urban, Joesf

PB - Springer

ER -