Numerically Computing Galois Groups of Minimal Problems

Fuente: arXiv
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Main Author: Duff, Timothy
Format: Preprint
Published: 2025
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author Duff, Timothy
author_facet Duff, Timothy
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2507_10407
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Numerically Computing Galois Groups of Minimal Problems
Duff, Timothy
Computer Vision and Pattern Recognition
Symbolic Computation
Algebraic Geometry
68W30
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.
title Numerically Computing Galois Groups of Minimal Problems
topic Computer Vision and Pattern Recognition
Symbolic Computation
Algebraic Geometry
68W30
url https://arxiv.org/abs/2507.10407