Conditional Euclidean distance optimization via relative tangency
Abstract
We introduce a theory of relative tangency for projective algebraic varieties. The dual variety of a variety relative to a subvariety is the set of hyperplanes tangent to at a point of . We also introduce the concept of polar classes of relative to . We explore the duality of varieties of low rank matrices relative to special linear sections. In this framework, we study the critical points of the Euclidean Distance function from a data point to , lying on . The locus where the number of such conditional critical points is positive is called the ED data locus of given . The generic number of such critical points defines the conditional ED degree of given . We show the irreducibility of ED data loci, and we compute their dimensions and degrees in terms of relative characteristic classes.
Cite
@article{arxiv.2310.16766,
title = {Conditional Euclidean distance optimization via relative tangency},
author = {Sandra Di Rocco and Lukas Gustafsson and Luca Sodomaco},
journal= {arXiv preprint arXiv:2310.16766},
year = {2025}
}
Comments
41 pages, 4 figures. Accepted for publication on Mathematics of Computation