Date of Award
Spring 4-26-2019
Document Type
Honors Project
Degree Name
Bachelor of Science
Department
Mathematics
Department Chair or Program Director
Randall Helmstutler, Ph.D.
First Advisor
James Collins, Ph.D.
Second Advisor
Julius Esunge, Ph.D.
Third Advisor
Jangwoon Lee, Ph.D.
Major or Concentration
Mathematics
Abstract
The purpose of this research is to decrease the run time of Bertini, a program that approximates solutions of polynomial systems. Bertini can be run more efficiently if it is known whether a polynomial is singular or non-singular. In this research, we focus on polynomials in one variable. We use a machine learning algorithm to classify polynomials into these two categories. To do so, we create and use a set of polynomials to train a neural network and create a model. Then, we create and use a test set to assess the accuracy of the model. By changing the hyper-parameters of the system and by changing the functions used in the system, the accuracy of the model is able to be increased.
Recommended Citation
Anderson, Riley, "Improving Bertini 2.0: Classifying Singular Polynomials with Machine Learning" (2019). Student Research Submissions. 286.
https://scholar.umw.edu/student_research/286