I have no ideas on benchmarks, but breeze has a CG solver: https://github.com/scalanlp/breeze/tree/master/math/src/main/scala/breeze/optimize/linear/ConjugateGradient.scala
https://github.com/scalanlp/breeze/blob/e2adad3b885736baf890b306806a56abc77a3ed3/math/src/test/scala/breeze/optimize/linear/ConjugateGradientTest.scala It's based on the code from TRON, and so I think it's more targeted for norm-constrained solutions of the CG problem. On Fri, Jun 27, 2014 at 5:54 PM, Debasish Das <debasish.da...@gmail.com> wrote: > 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 >
