LINEAR TRANSFORMATION SIMULATION

A 2×2 matrix holds just four numbers, and those four numbers say nothing except where the two unit arrows î and ĵ end up. The whole coordinate grid follows them: the determinant is the factor by which every cell’s area grows, a negative determinant means the plane has been mirrored, and a determinant of zero collapses the entire plane onto a single line, leaving the map non-invertible. Eigenvectors are the rare directions that are not swung aside, which is why a 30-degree rotation has none of them among the reals.

▸ Building the coordinate grid & basis vectors î, ĵ…
▸ Loading the target matrix [[a, b], [c, d]]
▸ Interpolating smoothly: identity matrix → target matrix
▸ Computing the determinant det = ad − bc (area factor)
▸ Solving eigenvalues λ & drawing eigenvectors…
▸ Ready — Online. ✅
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Simulation room Matrices & linear transforms

Linear Transforms · 2×2 Matrix
Online
î · ĵ · det · eigenvector · λ
Matrix & invariants
▦ 2×2 matrix
î (green) · ĵ (red) · unit cell → image
î → (a, c)—
ĵ → (b, d)—
Determinant det—
Trace—
Eigenvalue λ₁—
Eigenvalue λ₂—
Tip
Column 1 = where î lands, column 2 = where ĵ lands. det = ad − bc is the signed area-scaling factor (negative = flips like a mirror). An eigenvector = a direction that does not change, only stretched by λ.
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det · λ₁ · λ₂ over the morph progress (0 → target → 0) detλ₁λ₂
About Linear Transforms

A 2×2 matrix holds just four numbers, and those four numbers say nothing except where the two unit arrows î and ĵ end up. The whole coordinate grid follows them: the determinant is the factor by which every cell’s area grows, a negative determinant means the plane has been mirrored, and a determinant of zero collapses the entire plane onto a single line, leaving the map non-invertible. Eigenvectors are the rare directions that are not swung aside, which is why a 30-degree rotation has none of them among the reals.

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