Mryntu

I'm so confused on how we can have a 2x3 matrix A, multiply it by a vector in $Bbb R^3$ and then end up with a vector in $Bbb R^2$. Is it possible to visualize this at all or do I need to sort of blindly accept this concept as facts that I'll accept and use?
Can someone give a very brief summarization on why this makes sense? Because I just see it as, in a world (dimension) in $Bbb R^3$, we multiply it by a vector in $Bbb R^3$, and out pops a vector in $Bbb R^2$.

Thanks!

For the moment don't think about multiplication and matrices.

You can imagine starting from a vector $(x,y,z)$ in $mathbbR^3$ and mapping it to a vector in $mathbbR^2$ this way, for example:
$$
(x, y, z) mapsto (2x+ z, 3x+ 4y).
$$

Mathematicians have invented a nice clean way to write that map. It's the formalism you've learned for matrix multiplication. To see what $(1,2,3)$ maps to, calculate the matrix product
$$
beginbmatrix
2 & 0 & 1 \
3 & 4 & 0
endbmatrix
beginbmatrix
1 \
2 \
3
endbmatrix
=
beginbmatrix
5\
11
endbmatrix.
$$

You will soon be comfortable with this, just as you are now with whatever algorithm you were taught for ordinary multiplication. Then you will be free to focus on understanding what maps like this are useful for.

A more intuitive way is to think of a matrix "performing" on a vector, instead of a matrix "multiplying" with a vector.

Let's give an example. You have some triples of real numbers:

(1,2,3), (2,5,1), (3,5,9), (2,9,8)

and you "forget" the third coordinate:

(1,2), (2,5), (3,5), (2,9)

Surprisingly, this is an example of "matrix performance." Can you find
a matrix $M$ that "forgets" the second coordinate?

Answer:

The matrix is $$left(beginarrayl1 & 0 & 0 \ 0 & 1 & 0 endarrayright)$$

Explanation:

To get the first row, think about what happens under matrix multiplication to the vector $(1,0,0)$. The next two rows are similar.

We call such a matrix $M$ a projection.
We may visualize the projection as such.

Can you see what it means to "forget" the
third coordinate?

The important part of
a projection is linearity:

• You may project the addition of two vectors, or you may
  add the projection of two vectors and you get the same result.


• Similarly, you may project a scaled vector, or scale the vector
  and then project it, and you get the same result.

We call a function with the linearity property a linear function.

In symbols, for any linear $f$,

• $f(v + w) = f(v) + f(w)$


• $f(cv) = cf(v)$

We see that the projection defined above is a
linear function.
Actually, you can check that every matrix is a linear function.
Perhaps it is more surprising that every linear function is a matrix. You may think of a matrix as a way to represent some linear function.

A linear mapping has the property that it maps subspaces to subspaces.

So it will map a line to a line or $0$, a plane to a plane, a line, or $0$, and so on.

By definition, linear mappings “play nice” with addition and scaling. These properties allow us to reduce statements about entire vector spaces down to bases, which are quite “small” in the finite dimensional case.