# Finding SVD for a square, symmetric matrix?

The only technique I know is to find the eigenvalues and eigenvectors. The singular value decomposition of the square matrix A is the factorisation $A=P^{\,\textsc{t}}DP$, where D is the diagonal matrix whose entries are the eigenvalues of A, and P is the orthogonal matrix whose columns are the corresponding normalised eigenvectors.