Right now the SVD, which computes A = USV^T for a mxn matrix A with m >= n, produces a square V n x n and rectangular U m x min(n,m). However, for many applications it is necessary to compute the extra columns of U, such that U is m x m. For example, in computer vision a (very) common operation is to take the SVD of a 3x4 matrix (call it P), row-deficient, in order to find the null vector. With a SVD that requires m >= n, this is done by taking the SVD of P^T and using U to find the nullvector X such that PX = 0 and ||X|| = 1. However, if U is not computed fully, this is not possible. In other words, the current Eigen SVD isn't useful for solving underdetermined homogeneous linear systems (Ax = 0 with A wide rather than tall).
I looked into extending the existing SVD code but sadly it's pretty opaque. The various papers about implementing the SVD aren't easy either, and I don't have a week sink into it. Is anyone working on eigen familiar with the SVD algorithms? I may be able to do it in less time with a bit of help.