Compute full SVD

For a matrix of shape `R, C` nalgebra's SVD decomposition currently computes the singular vector matrices `U` and `V_t` with shapes `R, M` and `M, C` where `M = min(R, C)`. Although this is sufficient for many tasks like solving linear equation systems, in a lot of applications (e.g. in computer vision for homography or fundamental matrix decomposition) it is required to have a full decomposition where `U` and `V_t` have shape `R, R` and `C, C`.
This is also what [Eigen](https://eigen.tuxfamily.org/dox/classEigen_1_1JacobiSVD.html), [nalgebra-lapack](https://docs.rs/nalgebra-lapack/0.10.0/nalgebra_lapack/struct.SVD.html) and [SciPy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.svd.html) compute by default.

It would be great if nalgebra provided this as well, at least optionally!