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Approximate and discrete Euclidean vector bundles

Published online by Cambridge University Press:  21 March 2023

Luis Scoccola
Affiliation:
Department of Mathematics, Northeastern University, 43 Leon St, Boston, MA 02115, USA; E-mail: l.scoccola@northeastern.edu
Jose A. Perea
Affiliation:
Department of Mathematics and Khoury College of Computer Sciences, Northeastern University, 43 Leon St, Boston, MA 02115, USA; E-mail: j.pereabenitez@northeastern.edu

Abstract

We introduce $\varepsilon $-approximate versions of the notion of a Euclidean vector bundle for $\varepsilon \geq 0$, which recover the classical notion of a Euclidean vector bundle when $\varepsilon = 0$. In particular, we study Čech cochains with coefficients in the orthogonal group that satisfy an approximate cocycle condition. We show that $\varepsilon $-approximate vector bundles can be used to represent classical vector bundles when $\varepsilon> 0$ is sufficiently small. We also introduce distances between approximate vector bundles and use them to prove that sufficiently similar approximate vector bundles represent the same classical vector bundle. This gives a way of specifying vector bundles over finite simplicial complexes using a finite amount of data and also allows for some tolerance to noise when working with vector bundles in an applied setting. As an example, we prove a reconstruction theorem for vector bundles from finite samples. We give algorithms for the effective computation of low-dimensional characteristic classes of vector bundles directly from discrete and approximate representations and illustrate the usage of these algorithms with computational examples.

Information

Type
Computational Mathematics
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (https://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use.
Copyright
© The Author(s), 2023. Published by Cambridge University Press
Figure 0

Figure 1 2D projections of an unknown 3D shape.

Figure 1

Table 1 Pseudocode for the algorithm $\mathsf {sw}_1$.

Figure 2

Table 2 Pseudocode for the algorithm $\mathsf {eu}$.

Figure 3

Table 3 Pseudocode for the algorithm $\mathsf {sw}_2$.

Figure 4

Table 4 Runtime of the algorithms on laptop with a 2.20 GHz Intel Core i7 and 16GB of RAM.

Figure 5

Figure 2 2D projections of samples approximating two attractors of the double-gyre dynamical system. Consecutive samples are joined by an edge.

Figure 6

Figure 3 Persistence diagrams of the Vietoris–Rips cohomology of finite samples approximating two attractors of the double-gyre dynamical system, with Stiefel–Whitney classes highlighted.

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Figure 4 Samples of a dataset parametrized by the real projective plane and the persistence diagram of its Vietoris–Rips cohomology, with Stiefel–Whitney classes highlighted.

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Figure 5 A sample of a synchronization problem parametrized by a bundle over the sphere and the persistence diagram of the base of the bundle with the Stiefel–Whitney classes highlighted.