Hi, I am looking for an efficient linear CG to be put inside the Quadratic Minimization algorithms we added for Spark mllib.
With a good linear CG, we should be able to solve kernel SVMs with this solver in mllib... I use direct solves right now using cholesky decomposition which has higher complexity as matrix sizes become large... I found out some jblas example code: https://github.com/mikiobraun/jblas-examples/blob/master/src/CG.java I was wondering if mllib developers have any experience using this solver and if this is better than apache commons linear CG ? Thanks. Deb
