Added: http://qr.ae/E6fuI is probably what you should read instead. Maybe I’ll merge the two someday, and add some comments about diagonalising an operator.
The eigenvectors of a matrix summarise what it does.
- Think about a large, not-sparse matrix. A lot of computations are implied in that block of numbers. Some of those computations might overlap each other–2 steps forward, 1 step back, 3 steps left, 4 steps right … that kind of thing, but in 400 dimensions. The eigenvectors aim at the end result of it all.** **
The eigenvectors point in the same direction before & after a linear transformation is applied. (& they are the only vectors that do so)
For example, consider a shear
repeatedly applied to ℝ².
In the above, 
and . (The red arrow is not an eigenvector because it shifted over.)
The eigenvalues say how their eigenvectors scale during the transformation, and if they turn around.
If λᵢ = 1.3 then |eig****ᵢ| grows by 30%. If λᵢ = −2
then 
doubles in length and points backwards. If λᵢ = 1 then |eig****ᵢ| stays the same. And so on. Above, λ₁ = 1 since stayed the same length.It’s nice to add that
and
.
For a long time I wrongly thought an eigenvector was, like, its own thing. But it’s not. Eigenvectors are a way of talking about a (linear) transform / operator. So eigenvectors are always the eigenvectors of some transform. Not their own thing.
Put another way: eigenvectors and eigenvalues are a short, universally comparable way of summarising a square matrix. Looking at just the eigenvalues (the spectrum) tells you more relevant detail about the matrix, faster, than trying to understand the entire block-of-numbers and how the parts of the block interrelate. Looking at the eigenvectors tells you where repeated applications of the transform will “leak” (if they leak at all).
To recap: eigenvectors are unaffected by the matrix transform; they simplify the matrix transform; and the λ’s tell you how much the |eig|’s change under the transform.
Now a payoff.
Dynamical Systems make sense now.
If repeated applications of a matrix = a dynamical system, then the eigenvalues explain the system’s long-term behaviour.
I.e., they tell you whether and how the system stabilises, or … doesn’t stabilise.
Dynamical systems model interrelated systems like ecosystems, human relationships, or weather. They also unravel mutual causation.
What else can I do with eigenvectors?
Eigenvectors can help you understand:
- helicopter stability
- quantum particles (the Von Neumann formalism)
- guided missiles
- PageRank 1 2
- the fibonacci sequence
- your Facebook friend network
- eigenfaces
- lots of academic crap
- graph theory
- mathematical models of love
- electrical circuits
- JPEG compression 1 2
- markov processes
- operators & spectra
- weather
- fluid dynamics
- systems of ODE’s … well, they’re just continuous-time dynamical systems
- principal components analysis in statistics
- for example principal components (eigenvalues after varimax rotation of the correlation matrix) were used to try to identify the dimensions of brand personality
Plus, maybe you will have a cool idea or see something in your life differently if you understand eigenvectors intuitively.
