|
|||||
 |
|
|
|
|
|
|
|
Learning a
performance metric of Buchberger's algorithm
Jelena Mojsilović, Dylan Peifer and Sonja Petrović |
|
Vol. 16 (2023), No. 2, 227–248
|
Abstract |
|
What can be (machine) learned about the performance of Buchberger’s algorithm? Given a system of polynomials, Buchberger’s algorithm computes a Gröbner basis of the ideal these polynomials generate using an iterative procedure based on multivariate long division. The runtime of each step of the algorithm is typically dominated by a series of polynomial additions, and the total number of these additions is a hardware-independent performance metric that is often used to evaluate and optimize various implementation choices. We attempt to predict, using just the starting input, the number of polynomial additions that take place during one run of Buchberger’s algorithm. Good predictions are useful for quickly estimating difficulty and understanding what features make a Gröbner basis computation hard. Our features and methods could also be used for value models in the reinforcement learning approach to optimize Buchberger’s algorithm introduced in Peifer’s thesis. We show that a multiple linear regression model built from a set of easy-to-compute ideal generator statistics can predict the number of polynomial additions somewhat well, better than an uninformed model, and better than regression models built on some intuitive commutative algebra invariants that are more difficult to compute. We also train a simple recursive neural network that outperforms these linear models. Our work serves as a proof of concept, demonstrating that predicting the number of polynomial additions in Buchberger’s algorithm is a feasible problem from the point of view of machine learning. |
PDF Access Denied
We have not been able to recognize your IP address
18.224.215.101
as that of a subscriber to this journal.
Online access to the content of recent issues is by
subscription, or purchase of single articles.
Please contact your institution's librarian suggesting a subscription, for example by using our journal-recommendation form. Or, visit our subscription page for instructions on purchasing a subscription.
You may also contact us at
contact@msp.org
or by using our
contact form.
Or, you may purchase this single article for USD 30.00:
Keywords
Groebner basis, machine learning
|
Mathematical Subject Classification
Primary: 13P10, 14Q20
Secondary: 62J20
|
Milestones
Received: 23 June 2021
Revised: 31 May 2022
Accepted: 12 June 2022
Published: 26 May 2023
Communicated by Kenneth S. Berenhaut
|
Authors |
| Jelena Mojsilović | |
| Department of Mathematics Purdue University West Lafayette, IN United States |
|
| Dylan Peifer | |
| Department of Mathematics Cornell University Ithaca, NY United States |
|
| Sonja Petrović | |
| Department of Applied
Mathematics Illinois Institute of Technology Chicago, IL United States |
|
| © 2023 MSP (Mathematical Sciences Publishers). |
