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468 points scapbi | 5 comments | | HN request time: 0.999s | source
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andrewla ◴[] No.45106778[source]
It's crazy that Linear Algebra is one of the deepest and most interesting areas of mathematics, with applications in almost every field of mathematics itself plus having practical applications in almost every quantitative field that uses math.

But it is SOOO boring to learn the basic mechanics. There's almost no way to sugar coat it either; you have to learn the basics of vectors and scalars and dot products and matrices and Gaussian elimination, all the while bored out of your skull, until you have the tools to really start to approach the interesting areas.

Even the "why does matrix multiplication look that way" is incredibly deep but practically impossible to motivate from other considerations. You just start with "well that's the way it is" and grind away until one day when you're looking at a chain of linear transformations you realize that everything clicks.

This "little book" seems to take a fairly standard approach, defining all the boring stuff and leading to Gaussian elimination. The other approach I've seen is to try to lead into it by talking about multi-linear functions and then deriving the notion of bases and matrices at the end. Or trying to start from an application like rotation or Markov chains.

It's funny because it's just a pedagogical nightmare to get students to care about any of this until one day two years later it all just makes sense.

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jcranmer ◴[] No.45108661[source]
> Even the "why does matrix multiplication look that way" is incredibly deep but practically impossible to motivate from other considerations.

It's only difficult if you are wedded to a description of matrices and vectors as seas of numbers that you grind your way through without trying to instill a fuller understanding of what those numbers actually mean. The definition makes a lot more sense when you see a matrix as a description of how to convert one sense of basis vectors to another set of basis vectors, and for that, you first need to understand how vectors are described in terms of basis vectors.

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nh23423fefe ◴[] No.45109621[source]
I dont agree with this. Matrices don't convert sets of basis vectors to sets of basis vectors. What would you say about singular matrices for example?

The natural motivation of matrices is as representing systems of equations.

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1. griffzhowl ◴[] No.45110434[source]
There's no single best way to understand any of this, but the action of a matrix on the standard basis vectors is a totally reasonable place to start because of its simplicity, and then the action on any vector can be built out of that because they're linear combinations of basis vectors.
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2. nh23423fefe ◴[] No.45130089[source]
i don't agree because this seems circular. You cant even define a matrix as something that acts on vectors meaningfully until you have some machinery.

if you start with a set S and then make it vector space V over field K. Then by definition, linear combinations (and its not an algebra so nonlinear isn't even defined) are closed in V.

You can then define spanning sets and linear independence to get bases. From bases you can define coordinate vectors over K^n as isomorphic to V. Then given some linear function f : V->W by definition f(v) = f(v^i * b_i) = v^i * f(b_i)

Only here is when you can even define a matrix meaningfully as a tuple of coordinate vectors which are the image of some basis vectors.

Then you need to prove that what was function application of linear functions on vectors is the same as a new operation of multiplication of matrices with coordinate vectors.

And then to prove the multiplication rule (which is inherently coordinate based) you are going make the same argument I made in sibling comment. But I could prove the rule directly by substitution using only systems of linear equations as the starting point.

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3. griffzhowl ◴[] No.45130943[source]
Where's the circularity?

What you're saying is fine as an abstract presentation, but I was talking about how students might initially come to learn about matrices, so just introducing column vectors as representing points in 2 and 3 dimensional space and how matrices transform them is fine.

Beginning with the field and vector space axioms might be fine for sophisticated students, but I don't think it would make for an optimal learning experience for most students. We also don't teach kids the Peano axioms before they learn to add and multiply

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4. nh23423fefe ◴[] No.45132108{3}[source]
But the question was about deriving the multiplication rule. I said you could derive it from systems of equations directly and gave a proof.

> and how matrices transform them is fine

this is circular. You are introducing/assuming the multiplication rule right here. You can't then derive it

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5. griffzhowl ◴[] No.45136603{4}[source]
Yes, I was assuming it, not deriving it, so there's no circularity.

I realise though that I was answering more "How does it work?" (application) rather than "Why does it work?" (derivation)

For the latter, something involving sets of linear equations is probably best, as you initially said