[Math] Interpretation of eigenvector as axis of rotation of matrix tranformation

eigenvalues-eigenvectorslinear algebramatrices

https://youtu.be/PFDu9oVAE-g?t=4m11s

a matrix can change the direction a vector is pointing.

However, some vectors don't get their directions changed, but instead are scaled.

It was described that those vectors are eigen vectors and because every other vector is moving, and the eigen vector isn't, the eigenvector can be seen as the axis of rotation for the matrix.

The visual representation of what the matrix is doing, "rotating around the eigen vector" is shown literally. However, I know for a fact matrices can have multiple eigenvectors, but how can something have multiple axis of rotations?

Best Answer

Leaving aside adjoined vector cases, things about 3×3 matrices over real space is like this.

Every matrix has a characteristic polynomial of 3rd order. This polynomial has 3 roots (eigenvalues). Due to the main theorem of algebra, either all of them are real, or one of them is real and other are 2 conjugated complex numbers.

In the first case, each of the 3 real eigenvalues $\lambda_i$ has corresponding eigenvector $v_i$ (direction). These directions are perpendicular, and transformation can be seen as scaling in each of the direction by the corresponding $\lambda_i$.

In case of only one real eigenvector $\lambda_1$, there only one corresponding eigenvector $v_1$ and for the pair of complex eigenvectors $\lambda_{2,3}$, there is a corresponding plane $p$ (perpendicular to the aforementioned eigenvector). The transformation is scaling by $\lambda_1$ in the direction of $v_1$, scaling by $|\lambda_{2,3}|$ in the plane $p$ and rotation around $v_1$ by angle $\arg \lambda_{2,3}$.