On Wed, Jul 3, 2013 at 6:25 PM, Dmitriy Lyubimov <dlie...@gmail.com> wrote:
> On Wed, Jun 19, 2013 at 12:20 AM, Ted Dunning <ted.dunn...@gmail.com> > wrote: > > > > > As far as in-memory solvers, we have: > > > > 1) LR decomposition (tested and kinda fast) > > > > 2) Cholesky decomposition (tested) > > > > 3) SVD (tested) > > > > Ted, > so we don't have an eigensolver for the in-core Matrix? > Yes. We do. See org.apache.mahout.math.solver.EigenDecomposition Looking at the history, I am slightly surprised to see that I was the one who copied it from JAMA, replacing the Colt version and adding tests. > I understand that svd can be solved with an eigen decomposition but not the > other way around, right? > Well, the eigen decomposition of the normal matrix can give the SVD, but this is often not recommended due to poor conditioning. In fact, the eigen decomposition of any positive definite matrix is the same as the SVD. Where eigen values are complex, it is common to decompose to a block diagonal form where real values are on the diagonal and complex eigen-values are represented as 2x2 blocks. Our decomposition does this.
