Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/353#discussion_r11379843
 Diff: mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala 
@@ 0,0 +1,251 @@
+/*
+ * 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
+ *
+ * http://www.apache.org/licenses/LICENSE2.0
+ *
+ * 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.
+ */
+
+package org.apache.spark.mllib.optimization
+
+import scala.Array
+import scala.collection.mutable.ArrayBuffer
+
+import breeze.linalg.{DenseVector => BDV}
+import breeze.optimize.{CachedDiffFunction, DiffFunction}
+
+import org.apache.spark.Logging
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
+
+/**
+ * Class used to solve an optimization problem using Limitedmemory BFGS.
+ * @param gradient Gradient function to be used.
+ * @param updater Updater to be used to update weights after every iteration.
+ */
+class LBFGS(var gradient: Gradient, var updater: Updater)
+ extends Optimizer with Logging
+{
+ private var numCorrections: Int = 10
+ private var lineSearchTolerance: Double = 0.9
+ private var convTolerance: Double = 1E4
+ private var maxNumIterations: Int = 100
+ private var regParam: Double = 0.0
+ private var miniBatchFraction: Double = 1.0
+
+ /**
+ * Set the number of corrections used in the LBFGS update. Default 10.
+ * Values of m less than 3 are not recommended; large values of m
+ * will result in excessive computing time. 3 < m < 10 is recommended.
+ * Restriction: m > 0
+ */
+ def setNumCorrections(corrections: Int): this.type = {
+ assert(corrections > 0)
+ this.numCorrections = corrections
+ this
+ }
+
+ /**
+ * Set the tolerance to control the accuracy of the line search in mcsrch step. Default
0.9.
+ * If the function and gradient evaluations are inexpensive with respect to the cost
of
+ * the iteration (which is sometimes the case when solving very large problems) it
may
+ * be advantageous to set to a small value. A typical small value is 0.1.
+ * Restriction: should be greater than 1e4.
+ */
+ def setLineSearchTolerance(tolerance: Double): this.type = {
+ this.lineSearchTolerance = tolerance
+ this
+ }
+
+ /**
+ * Set fraction of data to be used for each LBFGS iteration. Default 1.0.
+ */
+ def setMiniBatchFraction(fraction: Double): this.type = {
+ this.miniBatchFraction = fraction
+ this
+ }
+
+ /**
+ * Set the convergence tolerance of iterations for LBFGS. Default 1E4.
+ * Smaller value will lead to higher accuracy with the cost of more iterations.
+ */
+ def setConvTolerance(tolerance: Int): this.type = {
+ this.convTolerance = tolerance
+ this
+ }
+
+ /**
+ * Set the maximal number of iterations for LBFGS. Default 100.
+ */
+ def setMaxNumIterations(iters: Int): this.type = {
+ this.maxNumIterations = iters
+ this
+ }
+
+ /**
+ * Set the regularization parameter. Default 0.0.
+ */
+ def setRegParam(regParam: Double): this.type = {
+ this.regParam = regParam
+ this
+ }
+
+ /**
+ * Set the gradient function (of the loss function of one single data example)
+ * to be used for LBFGS.
+ */
+ def setGradient(gradient: Gradient): this.type = {
+ this.gradient = gradient
+ this
+ }
+
+ /**
+ * Set the updater function to actually perform a gradient step in a given direction.
+ * The updater is responsible to perform the update from the regularization term as
well,
+ * and therefore determines what kind or regularization is used, if any.
+ */
+ def setUpdater(updater: Updater): this.type = {
+ this.updater = updater
+ this
+ }
+
+ def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector = {
+ val (weights, _) = LBFGS.runMiniBatchLBFGS(
+ data,
+ gradient,
+ updater,
+ numCorrections,
+ lineSearchTolerance,
+ convTolerance,
+ maxNumIterations,
+ regParam,
+ miniBatchFraction,
+ initialWeights)
+ weights
+ }
+
+}
+
+// Toplevel method to run LBFGS.
+object LBFGS extends Logging {
+ /**
+ * Run Limitedmemory BFGS (LBFGS) in parallel using mini batches.
+ * In each iteration, we sample a subset (fraction miniBatchFraction) of the total
data
+ * in order to compute a gradient estimate.
+ * Sampling, and averaging the subgradients over this subset is performed using one
standard
+ * spark mapreduce in each iteration.
+ *
+ * @param data  Input data for LBFGS. RDD of the set of data examples, each of
+ * the form (label, [feature values]).
+ * @param gradient  Gradient object (used to compute the gradient of the loss function
of
+ * one single data example)
+ * @param updater  Updater function to actually perform a gradient step in a given
direction.
+ * @param numCorrections  The number of corrections used in the LBFGS update.
+ * @param lineSearchTolerance  The tolerance to control the accuracy of the line search.
+ * @param convTolerance  The convergence tolerance of iterations for LBFGS
+ * @param maxNumIterations  Maximal number of iterations that LBFGS can be run.
+ * @param regParam  Regularization parameter
+ * @param miniBatchFraction  Fraction of the input data set that should be used for
+ * one iteration of LBFGS. Default value 1.0.
+ *
+ * @return A tuple containing two elements. The first element is a column matrix containing
+ * weights for every feature, and the second element is an array containing
the loss
+ * computed for every iteration.
+ */
+ def runMiniBatchLBFGS(
+ data: RDD[(Double, Vector)],
+ gradient: Gradient,
+ updater: Updater,
+ numCorrections: Int,
+ lineSearchTolerance: Double,
+ convTolerance: Double,
+ maxNumIterations: Int,
+ regParam: Double,
+ miniBatchFraction: Double,
+ initialWeights: Vector): (Vector, Array[Double]) = {
+
+ val lossHistory = new ArrayBuffer[Double](maxNumIterations)
+
+ val nexamples: Long = data.count()
+ val miniBatchSize = nexamples * miniBatchFraction
+ var i = 0
+
+ val costFun = new DiffFunction[BDV[Double]] {
 End diff 
Better create a private class for the cost function.

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