I don't recall any code in Spark that computes a matrix inverse. There is code that solves linear systems Ax = b with a decomposition. For example from looking at the code recently, I think the regression implementation actually solves AtAx = Atb using a Cholesky decomposition. But, A = n x k, where n is large but k is smallish (number of features), so AtA is k x k and can be solved in-memory with a library.
On Tue, Sep 27, 2016 at 3:05 AM, Cooper <ahmad.raban...@gmail.com> wrote: > How is the problem of large-scale matrix inversion approached in Apache Spark > ? > > This linear algebra operation is obviously the very base of a lot of other > algorithms (regression, classification, etc). However, I have not been able > to find a Spark API on parallel implementation of matrix inversion. Can you > please clarify approaching this operation on the Spark internals ? > > Here <http://ieeexplore.ieee.org/abstract/document/7562171/> is a paper on > the parallelized matrix inversion in Spark, however I am trying to use an > existing code instead of implementing one from scratch, if available. > > > > -- > View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Large-scale-matrix-inverse-in-Spark-tp27796.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe e-mail: user-unsubscr...@spark.apache.org >
