She broke up with me last week.
Not because I cheated.
Not because I was broke.
Not even because I forgot her birthday.
But because, in her words:
“No matter what I do, you never change your direction.”
At first, I thought she was just calling me stubborn. Then my inner math brain clicked... She was literally describing an eigenvector.
See, in math, when you apply a transformation (matrix A) to a vector (v), most vectors get spun around, twisted, thrown somewhere else. They change direction and magnitude.
But an eigenvector is different - it keeps the same direction. The only thing that changes is its scale, given by something called an eigenvalue (λ).
If λ = 2 → The vector doubles in size.
If λ = 0.5 → It shrinks.
If λ = -1 → It flips direction.
If λ = 1 → It stays the same size.
Apparently… in her eyes, I was λ = 1. Always same size. Always same direction.
Now the math part (because unlike my ex, I actually explain things):
Here’s how you find eigenvalues and eigenvectors, using a 2×2 matrix example:
Let’s say our “relationship matrix” was:
A =
[ 2 1 ]
[ 1 2 ]
Step 1: Find eigenvalues (λ)
We solve:
A·v = λ·v
→ (A − λI)·v = 0
→ det(A − λI) = 0
Subtract λ from each diagonal entry of A:
A − λI =
[ 2−λ 1 ]
[ 1 2−λ ]
Set determinant = 0 and solve for λ:
Determinant:
(2−λ)(2−λ) − 1 = (2−λ)² − 1 = 0
(2−λ)² = 1
2−λ = ±1
Case 1: 2−λ = 1 → λ = 1
Case 2: 2−λ = −1 → λ = 3
So, eigenvalues are: λ₁ = 1, λ₂ = 3
Step 2: Find eigenvectors (v)
For λ = 1:
(A − λI)·v = 0
[ 2−λ 1 ] [ x ] = [ 0 ]
[ 1 2−λ ] [ y ] [ 0 ]
[ 2−1 1 ] [ x ] = [ 0 ]
[ 1 2−1 ] [ y ] [ 0 ]
[ 1 1 ] [ x ] = [ 0 ]
[ 1 1 ] [ y ] [ 0 ]
From the first row:
x + y = 0
y = −x
From the second row:
x + y = 0
y = −x
So eigenvector = any scalar multiple of [ 1, −1 ]ᵀ
For λ = 3:
(A − λI)·v = 0
[ 2−λ 1 ] [ x ] = [ 0 ]
[ 1 2−λ ] [ y ] [ 0 ]
[ 2−3 1 ] [ x ] = [ 0 ]
[ 1 2−3 ] [ y ] [ 0 ]
[ -1 1 ] [ x ] = [ 0 ]
[ 1 -1 ] [ y ] [ 0 ]
From the first row:
−x + y = 0
y = x
From the second row:
x + (-y) = 0
x - y = 0
x = y
So eigenvector = any scalar multiple of [ 1, 1 ]ᵀ
Final result:
λ = 1 → v = [ 1, −1 ]
λ = 3 → v = [ 1, 1 ]
Congratulations 🎉, you have just learned how to find the eigenvectors and eigenvalues of a matrix.
Bonus:
Why does AI-ML care?
Eigenvalues & eigenvectors are everywhere in AI/ML:
PCA → Reduce dimensions by keeping top eigenvectors of covariance matrix (largest eigenvalues = most variance).
Spectral Clustering → Graph Laplacian eigenvalues help find clusters.
Neural Stability → Eigenvalues of weight matrices can indicate exploding/vanishing gradients.
Markov Chains → Long-term behaviour = eigenvector of eigenvalue 1.
In short:
Eigenvectors tell you the “unchangeable direction” under a transformation.
Eigenvalues tell you “how much” that direction is stretched.
In ML, this is how we find patterns, compress data, and understand model behaviour.
I am waiting for a matrix that multiplies me by λ > 1 and actually makes me grow.
I'm so old I wrote that!
That's assuming it's the Windows version, which is the one I worked on. The Win9x game, art, and original code, were done by Maxis/Cinematronics. I ported it to Windows NT, converted the x86 asm to C, made it work on RISC, and so on.
Success has many fathers, and all credit should really go to the original designers... I'm just the fun uncle that brought it to the masses.
I love when I write something really great and the only people who like it are pr0n bots. I find solace in their loyalty. They recognize the radiant ascendancy of my genius while the world turns away. They are my only fans