[resource0x]

My arguments is: whoever understands linear algebra has to be able to explain it to anyone having a sufficient math background. The failure to do so signals the lack of understanding. Presenting it as a pure algebraic game cleverly avoids the problems of interpretation, but when you proceed to applications, it leads to conceptual confusion. One "discovery" I made while learning LA is that most applications are based on mathematical coincidence. Namely, the formula for the scalar product of 2 vectors is identical to the formula for the correlation between 2 series of data. There's no apparent connection between the "orthogonality" in one sense and "orthogonality" (as a lack of correlation) in another.

I submit that not only the subject is not well understood, but even the name of the subject is wrong. It should be called "The study of orthogonality". This change of perspective will naturally lead to discussion of orthogonal polynomials, orthogonal functions, create a bridge to representation theory and (on the other end) to the applications in data science. What say you? :-)

[ravi-delia]

I think that "when you proceed to applications" is the issue there. Applications where? For applications in field theory, the spatial metaphor is exactly incorrect! For applications in various spectral theories, it's worse than useless.

What you say regarding the seeming coincidental nature of "real world" applications is basically correct (with correlation specifically there's some other stuff going on, it isn't that surprising, but in general), but unavoidable for any aspect of pure mathematics. Math is the study of formal systems, and the real world wasn't cooked up on a black board. If we can demonstrate that some component of reality obeys laws which map onto axioms, we can apply math to the world. But re-framing an entire field to work with one specific real world use (not even imo the most important real world use!) is just silly.

I love the idea of encouraging students early on to look at different areas of math and see the connections. But linear algebra is connected in more ways to more things than just using an inner product to pull out a nice basis. Noticing that polynomials, measurable functions, etc are vectors is possible without reframing the entire field, and there are lots of uses of linear algebra that don't require a norm! Hell representation theory only does in some situations.

[resource0x]

You start with a controversial statement ("Math is the study of formal systems"), and the rest follows. Not everyone agrees with this viewpoint. I think algebraic formalization provides just one perspective of looking at things, but there are other perspectives, and their interplay (superposition) constitutes the "knowledge". Focusing just on albegraic perspective is a pedagogical mistake IMO. Some say it's all a kind of hangover from bourbakinism though. (Treating math as a game of symbols is equivalent to artificial restriction to use just 1% of your brain capacity IMO)

[ravi-delia]

Hmm, I do see where you're coming from. To me, saying math is the study of formal systems is a statement of acceptance and neutrality- we can welcome ultrafinitists and non-standard analysts under one big tent. But you correctly point out that it's still a boundary I've drawn, and it happens to be drawn around stuff I enjoy. I'm by no means saying that there isn't room for practical, grounded math pedagogy with less emphasis on rigor.

However, there's plenty of value in the formal systems stuff. Algebraic formalization is just one way of looking at the simplest forms on linear algebra, but there really isn't any other way of looking at abstract algebra. Or model theory, or the weirder spectral stuff. Or algebraic topology. And when linear algebra comes up in those contexts (which it does often, it's the most well developed field of mathematics), it's best understood from an abstract, formal perspective.

And, just as a personal note, I personally would never have pursued mathematics if it were presented any other way. I'm not trying to use that as an argument- as we've discussed, the problem with math pedagogy certainly isn't a lack of abstract definitions and rigor. But there are people who think like me, and the reason the textbooks are written like that is because that's what was helpful to the authors when they were learning. It wasn't inflicted on our species from the outside.

[resource0x]

> the reason the textbooks are written like that is because that's what was helpful to the authors when they were learning

The author writing a book after 30 years of learning, thinking, talking with other people cannot easily reconstruct what was helpful and what wasn't. Creating 1-dimensional representation of the state of mind (which constitues "understanding") is a virtually impossible task. And here algebraic formalism comes to the rescue. "Definition" - "Theorem" - "Corollary" structure looks like a silver bullet, it fits very well in a linear format of a book. Unfortunately, this format is totally inadequate when it comes to passing knowledge. Very often, you can't understand A before you understand B, and you can't understand B before understanding A - the concepts in math are very often "entangled" (again, I'm talking about understanding, not formal consistency). You need examples, motivations, questions and answers - the whole arsenal of pedagogical tricks.

Some other form of presentation must be found to make it easier to encode the knowledge. Not sure what this form might be. Maybe some annotated book format will do, not sure. It should be a result of a collective effort IMO. Please think about it.

BTW, this is not a criticism of LADR book in particular. The proofs are concise and beautiful. But... the compression is very lossy in terms of representing knowledge.

[ravi-delia]

> "Definition" - "Theorem" - "Corollary" structure looks like a silver bullet, it fits very well in a linear format of a book. Unfortunately, this format is totally inadequate when it comes to passing knowledge.

I really can't emphasize enough that this is exactly how I learn things. I don't claim to be a majority! But saying that no one can learn from that sort of in-order definition-first method is like saying no one can do productive work before 6am. It sucks that morning people control the world, but its hardly a human universal to sleep in.

> Some other form of presentation must be found to make it easier to encode the knowledge. Not sure what this form might be. Maybe some annotated book format will do, not sure. It should be a result of a collective effort IMO.

I 100% agree. Have you seen the napkin project? I don't love the exposition on everything, but it builds up ideas pretty nicely, showing uses and motivation mixed in with the definitions. I've been trying to write some resources of my own intended for interested laymen, so more focus on motivation and examples and less on proofs and such. I like the challenge of trying to cut to the core of why we define things a certain way- though I'm biased towards "because it makes the formal logic nice" as an explanation.

[resource0x]

> Have you seen the napkin project?

Will take a look, thanks! I think I understand your point. I want to thank you for the productive discussion!

[ndriscoll]

What do you mean with correlation and orthogonality? Like with signal processing, you might calculate the cross-correlation of two signals, and it basically tells you at each possible shifted value, to what extent does one signal project onto the other (so what's their dot product). Orthogonality is not invariant under permuting/shifting entries in just one of the vectors, obviously (e.g. in your standard 2-d arrows space, x-hat is orthogonal to y-hat but not x-hat).

Linear algebra studies linearity, not (just) orthogonality. Orthogonality requires an inner product, and there isn't a canonical one on a linear structure, nor is there any one on e.g. spaces over finite fields. Mathematics, like programming, has an interface segregation principle. By writing implementations to a more minimal interface, we can reuse them for e.g. modules or finite spaces. It also makes it clear that questions like "are these orthogonal" depend on "what's the product", which can be useful to make sense of e.g. Hermite polynomials, where you use a weighted inner product.

[nyssos]

> Namely, the formula for the scalar product of 2 vectors is identical to the formula for the correlation between 2 series of data. There's no apparent connection between the "orthogonality" in one sense and "orthogonality" (as a lack of correlation) in another.

Of course there is. Covariance looks like an L2 norm (what you're calling the scalar product) because it is an L2 norm. They're the exact same object.