diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala
index 07a01b61b5b59..bbf385229081a 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala
@@ -24,21 +24,13 @@ import org.scalatest.FunSuite
 import org.jblas.{DoubleMatrix, SimpleBlas, NativeBlas}
 
 class NNLSSuite extends FunSuite {
-  /** Generate a NNLS problem whose optimal solution is the all-ones vector. */
+  /** Generate an NNLS problem whose optimal solution is the all-ones vector. */
   def genOnesData(n: Int, rand: Random): (DoubleMatrix, DoubleMatrix) = {
-    val A = new DoubleMatrix(n, n)
-    val b = new DoubleMatrix(n, 1)
-    for (i <- 0 until n; j <- 0 until n) {
-      val aij = rand.nextDouble()
-      A.put(i, j, aij)
-      b.put(i, b.get(i, 0) + aij)
-    }
-
-    val ata = new DoubleMatrix(n, n)
-    val atb = new DoubleMatrix(n, 1)
+    val A = new DoubleMatrix(n, n, Array.fill(n*n)(rand.nextDouble()): _*)
+    val b = A.mmul(DoubleMatrix.ones(n, 1))
 
-    NativeBlas.dgemm('T', 'N', n, n, n, 1.0, A.data, 0, n, A.data, 0, n, 0.0, ata.data, 0, n)
-    NativeBlas.dgemv('T', n, n, 1.0, A.data, 0, n, b.data, 0, 1, 0.0, atb.data, 0, 1)
+    val ata = A.transpose.mmul(A)
+    val atb = A.transpose.mmul(b)
 
     (ata, atb)
   }
@@ -53,17 +45,13 @@ class NNLSSuite extends FunSuite {
     // can legitimately fail to solve these anywhere close to exactly.  So we grab a considerable
     // sample of these matrices and make sure that we solved a substantial fraction of them.
 
-    for (kase <- 0 until 100) {
+    for (k <- 0 until 100) {
       val (ata, atb) = genOnesData(n, rand)
-      val x = NNLS.solve(ata, atb, ws)
+      val x = new DoubleMatrix(NNLS.solve(ata, atb, ws))
       assert(x.length === n)
-      var error = 0.0
-      var solved = true
-      for (i <- 0 until n) {
-        error = error + (x(i) - 1) * (x(i) - 1)
-        if (Math.abs(x(i) - 1) > 1e-3) solved = false
-      }
-      if (error > 1e-2) solved = false
+      val answer = DoubleMatrix.ones(n, 1)
+      SimpleBlas.axpy(-1.0, answer, x)
+      val solved = (x.norm2 < 1e-2) && (x.normmax < 1e-3)
       if (solved) numSolved = numSolved + 1
     }
 
@@ -72,16 +60,13 @@ class NNLSSuite extends FunSuite {
 
   test("NNLS: nonnegativity constraint active") {
     val n = 5
-    val M = Array(
-      Array( 4.377, -3.531, -1.306, -0.139,  3.418, -1.632),
-      Array(-3.531,  4.344,  0.934,  0.305, -2.140,  2.115),
-      Array(-1.306,  0.934,  2.644, -0.203, -0.170,  1.094),
-      Array(-0.139,  0.305, -0.203,  5.883,  1.428, -1.025),
-      Array( 3.418, -2.140, -0.170,  1.428,  4.684, -0.636))
-    val ata = new DoubleMatrix(n, n)
-    val atb = new DoubleMatrix(n, 1)
-    for (i <- 0 until n; j <- 0 until n) ata.put(i, j, M(i)(j))
-    for (i <- 0 until n) atb.put(i, M(i)(n))
+    val ata = new DoubleMatrix(Array(
+      Array( 4.377, -3.531, -1.306, -0.139,  3.418),
+      Array(-3.531,  4.344,  0.934,  0.305, -2.140),
+      Array(-1.306,  0.934,  2.644, -0.203, -0.170),
+      Array(-0.139,  0.305, -0.203,  5.883,  1.428),
+      Array( 3.418, -2.140, -0.170,  1.428,  4.684)))
+    val atb = new DoubleMatrix(Array(-1.632, 2.115, 1.094, -1.025, -0.636))
 
     val goodx = Array(0.13025, 0.54506, 0.2874, 0.0, 0.028628)