Posts tagged with matrix
In many showers there are two taps:Hot
andCold.
You use them to control both the pressure and the temperature of the water, but you do so indirectly: the pressure is controlled by the sum of the (position of the) two taps, while the temperature is controlled by their difference. Thus, the basis you are given:Hot = (1,0) Cold = (0,1)
Isn’t the basis you want:Pressure = (1,1) Temperature = (1,−1)
( would be the basis isomorphism)
other bases:
 L/R channels (mono, stereo)
 any ℝ→ℝ function can be split into odd and even parts
 The bound states of electrons are the eigenfunctions of the hydrogen atom Hamiltonian and its angular momentum, so describing them is changing the basis (from a standard spatial representation to the physically relevant one).
 (i.e., switching from rectangular to polar coördinates sometimes makes things much much easier. Imagine solving the Schrödinger PDE in rectangular coordinates!)
 handwriting may have an infinite basis
 pastries basically have a basis of
{flour, butter, eggs, sugar}
 faces
(Source: qr.ae)
Here’s a physically intuitive reason that rotations ↺
(which seem circular) are in fact linear maps.
If you have two independent wheels that can only roll straight forward and straight back, it is possible to turn the luggage. By doing both linear maps at once (which is what a matrix
or Lie action does) and opposite each other, two straights ↓↑ make a twist ↺.
Or if you could get a car  luggage  segway with split (= independent = disconnected) axles
to roll the right wheel(s) independently and opposite to the left wheel(s)
, then you would spin around in place.
This is trippy, and profound.
The determinant — which tells you the change in size after a matrix transformation 𝓜 — is just an Instance of the Alternating Multilinear Map.
(Alternating meaning it goes + − + − + − + − ……. Multilinear meaning linear in every term, ceteris paribus:
)
Now we trip. The inner product — which tells you the “angle” between 2 things, in a super abstract sense — is also an instantiation of the Alternating Multilinear Map.
In conclusion, mathematics proves that Size is the same kind of thing as Angle
Say whaaaaaat? I’m going to go get high now and watch Koyaanaasqatsi.
In the world of linear approximations of multiple parameters and multiple outputs, the Jacobian is a matrix that tells you: if I twist this knob, how does that part of the output change?
(The Jacobian is defined at a point. If the space not flat, but instead only approximated by flat things that are joined together, then you would stitch together different Jacobians as you stitch together different flats.)
Pretend that a through z are parameters, or knobs you can twist. Let’s not say whether you have control over them (endogenous variables) or whether the environment / your customers / your competitors / nature / external factors have control over them (exogenous parameters).
And pretend that F¹ through Fⁿ are the separate kinds of output. You can think in terms of a real number or something else, but as far as I know the outputs cannot be linked in a lattice or anything other than a matrix rectangle.
In other words this matrix is just an organised list of “how parameter c affects output F⁹”.
Notan bene — the Jacobian is just a linear approximation. It doesn’t carry any of the info about mutual influence, connections between variables, curvature, wiggle, womp, kurtosis, cyclicity, or even interaction effects.
A Jacobian tensor would tell you how twisting knob a knocks on through parameters h, l, and p. Still linear but you could work out the outcome better in a difficult system — or figure out what happens if you twist two knobs at once.
In maths jargon: the Jacobian is a matrix filled with partial derivatives.
You know what’s surprising?

Rotations are linear transformations.
I guess lo conocí but no entendí. Like, I could write you the matrix formula for a rotation by θ degrees:
But why is that linear? Lines are straight and circles bend. When you rotate something you are moving it along a circle. So how can that be linear?
I guess 2D linear mappings ℝ²→ℝ² surprise our natural 1D way of thinking about “straightness”.
Well I thought the outer product was more complicated than this.
An inner product is constructed by multiplying vectors A and B like Aᵀ × B. (ᵀ is for turned.) In other words, timesing each a guy from A by his corresponding b guy from B.
After summing those products, the result is just one number. In other words the total effect was to convert two lengthn vectors into just one number. Thus mapping a large space onto a small space, Rⁿ→R. Hence inner.
Outer product, you just do A × Bᵀ. That has the effect of filling up a matrix with the contents of every possible multiplicative combination of a's and b's. Which maps a large space onto a much larger space — maybe squared as large, for instance putting two Rⁿ vectors together into an Rⁿˣⁿ matrix.
No operation was done to consolidate them, rather they were left as individual pieces.
So the inner product gives a “brief” answer (two vectors ↦ a number), and the outer product gives a “longwinded” answer (two vectors ↦ a matrix). Otherwise — procedurally — they are very similar.
(Source: Wikipedia)