Why is SVD more stable than eigen decomposition?

SVD works for all matrices and is numerically more stable under perturbations.

Eigen decomposition requires square matrices and may be unstable for non-symmetric cases. SVD decomposes any matrix into orthogonal components, minimizing numerical instability and providing optimal low-rank approximations.