When multiplying two matrices, there's a manual procedure we all know how to go
through. Each result cell is computed separately as the dot-product of a row in
the first matrix with a column in the second matrix. While it's the easiest way
to compute the result manually, it may obscure a very interesting property of
the operation: *multiplying A by B is the linear combination of A's columns
using coefficients from B*. Another way to look at it is that it's a *linear
combination of the rows of B using coefficients from A*.

In this quick post I want to show a colorful visualization that will make this easier to grasp.

## Right-multiplication: combination of columns

Let's begin by looking at the right-multiplication of matrix `X` by a column
vector:

Representing the columns of `X` by colorful boxes will help visualize this:

Sticking the white box with `a` in it to a vector just means: multiply this
vector by the scalar `a`. The result is another column vector - a linear
combination of `X`'s columns, with `a`, `b`, `c` as the coefficients.

Right-multiplying `X` by a matrix is more of the same. Each resulting column
is a different linear combination of `X`'s columns:

Graphically:

If you look hard at the equation above and squint a bit, you can recognize this column-combination property by examining each column of the result matrix.

## Left-multiplication: combination of rows

Now let's examine left-multiplication. Left-multiplying a matrix `X` by a row
vector is a linear combination of `X`'s *rows*:

Is represented graphically thus:

And left-multiplying by a matrix is the same thing repeated for every result
row: it becomes the linear combination of the rows of `X`, with the
coefficients taken from the rows of the matrix on the left. Here's the equation
form:

And the graphical form:

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