A Perturbation Approach to Inverting Projective Degenerate Quadrics and Recovering Lost Geometric Information

This article presents a general perturbation approach to retrieving geometric information about the duals of degenerate quadrics obscured by the singular matrices that represent them. By embedding singular quadrics within a one-parameter family of neighbouring non-degenerate quadrics, and then applying standard techniques from analytic perturbation theory, we rigorously isolate the finite remainder from the singular pole residue to extract well-defined pseudo-distance metrics and polarity maps, and reveal how the degenerate limit of this family of non-degenerate quadrics retains an ``asymptotic memory'' of the directions of the perturbation matrices.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-12
DOI
https://doi.org/10.5281/zenodo.22723293
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

A Perturbation Approach to Inverting Projective Degenerate Quadrics and Recovering Lost Geometric Information

RongHan Dominic Foo
Zenodo (CERN European Organization for Nuclear Research)
Matrix Theory and Algorithms
article

A Perturbation Approach to Inverting Projective Degenerate Quadrics and Recovering Lost Geometric Information

RongHan Dominic Foo
article en

Abstract

This article presents a general perturbation approach to retrieving geometric information about the duals of degenerate quadrics obscured by the singular matrices that represent them. By embedding singular quadrics within a one-parameter family of neighbouring non-degenerate quadrics, and then applying standard techniques from analytic perturbation theory, we rigorously isolate the finite remainder from the singular pole residue to extract well-defined pseudo-distance metrics and polarity maps, and reveal how the degenerate limit of this family of non-degenerate quadrics retains an ``asymptotic memory'' of the directions of the perturbation matrices.

Zenodo (CERN European Organization for Nuclear Research)
Western Illinois University (US)
Openalex Percentile: Top 8%
Matrix Theory and Algorithms
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