Numerically Computing Galois Groups of Minimal Problems
Abstract
I discuss a seemingly unlikely confluence of topics in algebra, numerical computation, and computer vision. The motivating problem is that of solving multiples instances of a parametric family of systems of algebraic (polynomial or rational function) equations. No doubt already of interest to ISSAC attendees, this problem arises in the context of robust model-fitting paradigms currently utilized by the computer vision community (namely "Random Sampling and Consensus", aka "RanSaC".) This talk will give an overview of work in the last 5+ years that aspires to measure the intrinsic difficulty of solving such parametric systems, and makes strides towards practical solutions.
Cite
@article{arxiv.2507.10407,
title = {Numerically Computing Galois Groups of Minimal Problems},
author = {Timothy Duff},
journal= {arXiv preprint arXiv:2507.10407},
year = {2025}
}
Comments
abstract accompanying invited tutorial at ISSAC 2025; 10 pages w/ references