General Topology – Why Topology Rarely Comes Up Outside of Topology

Question

I am currently taking topology and it seems like a completely different branch of math than anything else I have encountered previously.

I find it a little strange that things are not defined more concretely. For example, a topological space is defined as a set $X$ with a collection of open sets $\tau$ satisfying some properties such as the empty set and $X$ are in $\tau$, intersection of two open sets are in $\tau$, and unions of open sets is in $\tau$.

So, it seems that a lot of things are topological spaces, such as the real line equipped with a collection of open sets. But I have not seen anyone bringing this up in other areas of mathematics such as linear algebra, calculus, differential equations or analysis or complex analysis. Sure, open sets and closed sets are brought up but the concept of "topology", "base", etc. etc. are missing entirely.

As you scratch the surface a little more you encounter things such as the subspace topology, product topology, order topology and open sets are defined differently with respect to each of them. But nonetheless outside of a course in topology, you never encounter these concepts.

Is there a reason why topology is not essential for other courses that I have mentioned? Is there a good reference that meshes serious topology (as in Munkres) with more applied area of mathematics?

Answer 1

In 1830 Jacobi wrote a letter to Legendre after the death of Fourier (for an account, see Fourier, Legendre and Jacobi, Kahane, 2011). In it he writes about "L'honneur de l’esprit humain" (The honour of the human mind), which later became a motto for pure mathematics, and the title of a fabulous book by Dieudonné. The translation of the quote exists under different forms, I chose:

Mathematics exists solely for the honour of the human mind.

Which does not prevent unforeseen practical uses of abstract theories: group derivations inspired from polynomial root solving had unexpected everyday-life applications in chemistry and cryptography.

Since you have high expectations about topology, you ought to have a look at a recent application of analysis situs to the world of digital data processing, named Topological data analysis (TDA), driven by people like G. Carlsson (not forgetting people like Edelsbrunner, Frosini, Ghrist, Robins), even in an industrial way (e.g. with Ayasdi company). In a few words, it may extract barcodes from point clouds, based on the concept of persistent homology.

EDIT: on request, I am adding a few relevant links (not advertising)

• Barcodes: The Persistent Topology Of Data, Robert Ghrist, 2008
• Topological Data Analysis, Afra Zomorodian, 2011
• Persistent Homology: Theory and Practice, Herbert Edelsbrunner and Dmitriy Morozov, 2012
• A Short Course in Computational Geometry and Topology, Herbert Edelsbrunner, 2014
• Elementary Applied Topology, Robert Ghist, 2014
• Persistence Theory: From Quiver Representations to Data Analysis, Steve Y. Oudot, 2015
• The site Applied Topology - Qualitative data analysis with a set of preprints

Those methods could be overrated (in practice) yet, from my data processing point of view, topology is pervasive in many applied fields, even when not directly mentioned. Most of the groundbreaking works in signal processing, image analysis, machine learning and data science performed in the past years rely on optimization and convergence proofs, with different norms, pseudo-norms, quasi-norms, divergences... hence topology, somewhat.

Regarding sampling and sensor networks, let me add the presentation Sensors, sampling, and scale selection: a homological approach by Don Sheehy, with slides and abstract:

In their seminal work on homological sensor networks, de Silva and Ghrist showed the surprising fact that its possible to certify the coverage of a coordinate free sensor network even with very minimal knowledge of the space to be covered. We give a new, simpler proof of the de Silva-Ghrist Topological Coverage Criterion that eliminates any assumptions about the smoothness of the boundary of the underlying space, allowing the results to be applied to much more general problems. The new proof factors the geometric, topological, and combinatorial aspects of this approach. This factoring reveals an interesting new connection between the topological coverage condition and the notion of weak feature size in geometric sampling theory. We then apply this connection to the problem of showing that for a given scale, if one knows the number of connected components and the distance to the boundary, one can also infer the higher betti numbers or provide strong evidence that more samples are needed. This is in contrast to previous work which merely assumed a good sample and gives no guarantees if the sampling condition is not met.