Github user yanboliang commented on a diff in the pull request:
    --- Diff: 
mllib/src/main/scala/org/apache/spark/ml/optim/NormalEquationSolver.scala ---
    @@ -0,0 +1,165 @@
    + * Licensed to the Apache Software Foundation (ASF) under one or more
    + * contributor license agreements.  See the NOTICE file distributed with
    + * this work for additional information regarding copyright ownership.
    + * The ASF licenses this file to You under the Apache License, Version 2.0
    + * (the "License"); you may not use this file except in compliance with
    + * the License.  You may obtain a copy of the License at
    + *
    + *
    + *
    + * Unless required by applicable law or agreed to in writing, software
    + * distributed under the License is distributed on an "AS IS" BASIS,
    + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    + * See the License for the specific language governing permissions and
    + * limitations under the License.
    + */
    +import breeze.linalg.{DenseVector => BDV}
    +import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => 
BreezeLBFGS, OWLQN => BreezeOWLQN}
    +import scala.collection.mutable
    +import{BLAS, DenseVector, Vectors}
    +import org.apache.spark.mllib.linalg.CholeskyDecomposition
    +private[ml] class NormalEquationSolution(
    +    val fitIntercept: Boolean,
    +    private val _coefficients: Array[Double],
    +    val aaInv: Option[DenseVector],
    --- End diff --
    I'd use ```Option[Array[Double]]``` to represent ```aaInv```, since we will 
scale it to original space at ```WeightedLeastSquares``` L263.
    BTW, should we use also expose ```coefficients``` as ```Array[Double]``` 
rather than ```DenseVector```?
    I found we did in-place update to scale back at ```WeightedLeastSquares``` 
L255. I'm more prefer to get coefficients in array by solver, then scale back 
and wrap it with ```DenseVector``` at upper layer. You can refer the 
```LinearRegression``` solved by ```L-BFGS```. Only suggestion, not big deal.

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