(-incubator, +user) If your matrix is symmetric (and real I presume), and if my linear algebra isn't too rusty, then its SVD is its eigendecomposition. The SingularValueDecomposition object you get back has U and V, both of which have columns that are the eigenvectors.
There are a few SVDs in the Spark code. The one in mllib is not distributed (right?) and is probably not an efficient means of computing eigenvectors if you really just want a decomposition of a symmetric matrix. The one I see in graphx is distributed? I haven't used it though. Maybe it could be part of a solution. On Thu, Aug 7, 2014 at 2:21 PM, yaochunnan <yaochun...@gmail.com> wrote: > Our lab need to do some simulation on online social networks. We need to > handle a 5000*5000 adjacency matrix, namely, to get its largest eigenvalue > and corresponding eigenvector. Matlab can be used but it is time-consuming. > Is Spark effective in linear algebra calculations and transformations? Later > we would have 5000000*5000000 matrix processed. It seems emergent that we > should find some distributed computation platform. > > I see SVD has been implemented and I can get eigenvalues of a matrix through > this API. But when I want to get both eigenvalues and eigenvectors or at > least the biggest eigenvalue and the corresponding eigenvector, it seems > that current Spark doesn't have such API. Is it possible that I write > eigenvalue decomposition from scratch? What should I do? Thanks a lot! > > > Miles Yao > > ________________________________ > View this message in context: How can I implement eigenvalue decomposition > in Spark? > Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
