To obtain the eigenvalues of \( \mathbf{A}\in {\mathbb{R}}^{n\times n} \), the strategy is to perform a series of similarity transformations on the original matrix \( \mathbf{A} \), in order to reduce it either into a diagonal form as above or into a tridiagonal form.

We say that a matrix \( \mathbf{B} \) is a similarity transform of \( \mathbf{A} \) if $$ \mathbf{B}= \mathbf{S}^T \mathbf{A}\mathbf{S}, \hspace{1cm} \mathrm{where} \hspace{1cm} \mathbf{S}^T\mathbf{S}=\mathbf{S}^{-1}\mathbf{S} =\mathbf{I}. $$ The importance of a similarity transformation lies in the fact that the resulting matrix has the same eigenvalues, but the eigenvectors are in general different.