Sparse SVD

# Singular Value Decomposition
Given an *m x n* matrix *A*, compute matrices *U, S, V* such that

*A = U * S * V^T*

There is no restriction on m, but we require n^2 doubles to fit in memory.
Further, n should be less than m.

The decomposition is computed by first computing *A^TA = V S^2 V^T*,
computing svd locally on that (since n x n is small),
from which we recover S and V.
Then we compute U via easy matrix multiplication
as *U =  A * V * S^-1*

Only singular vectors associated with singular values
greater or equal to MIN_SVALUE are recovered. If there are k
such values, then the dimensions of the return will be:

* *S* is *k x k* and diagonal, holding the singular values on diagonal.
* *U* is *m x k* and satisfies U^T*U = eye(k).
* *V* is *n x k* and satisfies V^TV = eye(k).

All input and output is expected in sparse matrix format, 1-indexed
as tuples of the form ((i,j),value) all in RDDs. 

# Testing
Tests included. They test:
- Decomposition promise (A = USV^T)
- For small matrices, output is compared to that of jblas
- Rank 1 matrix test included
- Full Rank matrix test included
- Middle-rank matrix forced via min_svalue included

# Example Usage

import org.apache.spark.SparkContext
import org.apache.spark.mllib.linalg.SVD

// Load and parse the data file
val data = sc.textFile("mllib/data/als/test.data").map { line =>
      val parts = line.split(',')
      ((parts(0).toInt, parts(1).toInt), parts(2).toDouble)
}
val m = 4
val n = 4

// recover singular vectors for singular values at or above 1e-5
val (u, s, v) = SVD.sparseSVD(data, m, n, 1e-5)

println("singular values = " + s.toArray.mkString)