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2x2 eigenvectors as you drag the matrix

What you are seeing: a 2x2 matrix M=(abcd)M = \begin{pmatrix}a & b \\ c & d\end{pmatrix} acting on the unit circle. The blue ellipse is the image of the unit circle under MM (semi-axes equal to the singular values). The accent arrows are the eigenvectors of MM, scaled by their eigenvalues. The eigenvalue formula λ±=(a+d)/2±(a+d)2/4(adbc)\lambda_\pm = (a+d)/2 \pm \sqrt{(a+d)^2/4 - (ad - bc)} has real solutions when the discriminant is non-negative.

Drag the sliders for a,b,c,da, b, c, d. With a=da = d and b=cb = -c you get a rotation; the eigenvalues become complex and the eigenvector arrows disappear (the readout flags the complex spectrum). With b=cb = c (symmetric MM) the eigenvectors are exactly orthogonal. With b=c=0b = c = 0 the eigenvectors stay axis-aligned regardless of a,da, d.

Figure 1. 2x2 eigendecomposition: unit circle, its image, and eigenvectors. Method: closed-form quadratic for eigenvalues; null-space row pick for eigenvectors.
a2.00
b1.00
c1.00
d3.00

WHAT TO TRY

  • Vary each control and watch the rail readouts respond.
  • Compare the diagnostic plot against the live scene.