Do you not just want to use linear regression? https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
Of course it requires a DataFrame-like input but that may be more natural to begin with. If the data set is small, then putting it on the driver and solving locally with a library is pretty easy. The Cholesky decomposition above doesn't solve the linear system itself, but helps solve AtAx = Atb, because AtA and Atb are small and so that part can be done locally. On Thu, Oct 6, 2016 at 6:49 AM Cooper <ahmad.raban...@gmail.com> wrote: > I have a system of linear equations in the form of Ax = b to solve in > Spark. > > A is n by n > > b is n by 1 > > I represent 'A' in the form of IndexedRowMatrix or RowMatrix and 'b' in the > form of DenseMatrix or DenseVector. > > How can I solve this system to calculate the 'x' vector? > > If the suggested solution is Cholesky Decomposition > < > https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/linalg/CholeskyDecomposition.scala > > > , would you please guide me through doing it as it is not part of the > public > API ? For example if the original matrix A is: > > 1,2,3,4 > 2,1,5,6 > 3,5,1,7 > 4,6,7,1 > > and b is: > > 5,6,7,8 > > What is passed as argument to the "solve" method ? > > Any other solution other than inversing 'A' would be very helpful. > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Solve-system-of-linear-equations-in-Spark-tp27847.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe e-mail: user-unsubscr...@spark.apache.org > >
