Dataset Open Access
Dataset supporting "Using Machine Learning to decide when to Precondition Cylindrical Algebraic Decomposition with Groebner Bases"
Huang, Zongyan; England, Matthew; Davenport, James H.; Paulson, Lawrence C.
Dataset supporting the paper:
Z. Huang, M. England, J.H. Davenport and L.C. Paulson
Using Machine Learning to decide when to Precondition Cylindrical Algebraic Decomposition with Groebner Bases.
Proceedings of the 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC '16), pp. 45--52. IEEE, 2016. Digital Object Identifier: 10.1109/SYNASC.2016.020
Files
(21.9 MB)
| Name | Size | |
|---|---|---|
|
22.txt
md5:5452e4f34d688e83a7df640661021906 |
2.2 MB | Download |
|
23.txt
md5:e5ebfa2469ff6b40c3d00a8083030a29 |
2.5 MB | Download |
|
24.txt
md5:f190b3b5636f25b8e14264183209e797 |
3.5 MB | Download |
|
32.txt
md5:5c4bea93c0fa55daaddd4f9b5e0e1d6b |
2.1 MB | Download |
|
33.txt
md5:2cbce53d545e1b031bcf219e862d914f |
2.5 MB | Download |
|
34.txt
md5:73f8e0aa8b0713563dc5a35756ded0e0 |
4.7 MB | Download |
|
42.txt
md5:09baf9259f9e6b4478a96cde63c70a7e |
776.0 kB | Download |
|
43.txt
md5:e18d3b09c7ef7edfd336a05b490abfed |
1.3 MB | Download |
|
44.txt
md5:439778b9c73d8d311e69496c65e55355 |
2.1 MB | Download |
|
beforeScale.txt
md5:a051b1fbafd38739f9e174c68d7e5cc9 |
59.8 kB | Download |
|
README.txt
md5:4a3914fae8229f48eab6d68e106b0bf5 |
2.1 kB | Download |
60
761
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 60 | 60 |
| Downloads | 761 | 761 |
| Data volume | 2.1 GB | 2.1 GB |
| Unique views | 58 | 58 |
| Unique downloads | 675 | 675 |
Indexed in
- Publication date:
- February 28, 2017
- DOI:
-
- Keyword(s):
- Symbolic Computation Machine Learning Cylindrical Algebraic Decomposition
- Grants:
-
European Commission:
- SC-square - SC-square - Satisfiability Checking and Symbolic Computation: uniting two communities to solve real problems (712689)
- Related identifiers:
- Supplement to
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International