I think the issue was NOT with spark. I was running a spark program that
dumped output to a binary file and then calling a scala program to read it
and write out Matrix Market format files. The issue seems to have been with
the classpath on the scala program, and went away when I added the spark
I get the same error even when I define covOperator not to use a matrix at
all:
def covOperator(v : BDV[Double]) :BDV[Double] = { v }
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I'm trying to compute the eigendecomposition of a matrix in a portion of my
code, using mllib.linalg.EigenValueDecomposition
(https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala
)
as follows:
val tol = 1e-10
val maxIter
