Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/460#discussion_r11984932
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/NNLSbyPCG.scala ---
@@ -0,0 +1,183 @@
+/*
+ * 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/LICENSE-2.0
+ *
+ * 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 org.jblas.{DoubleMatrix, SimpleBlas}
+
+import org.apache.spark.annotation.DeveloperApi
+
+
+/**
+ * :: DeveloperApi ::
+ * Object used to solve nonnegative least squares problems using a modified
+ * projected gradient method.
+ */
+@DeveloperApi
+private[mllib] object NNLSbyPCG {
+ class Workspace(val n: Int) {
+ val scratch = new DoubleMatrix(n, 1)
+ val grad = new DoubleMatrix(n, 1)
+ val x = new DoubleMatrix(n, 1)
+ val dir = new DoubleMatrix(n, 1)
+ val lastDir = new DoubleMatrix(n, 1)
+ val res = new DoubleMatrix(n, 1)
+
+ def wipe() {
+ var i: Int = 0
+ while (i < n) {
+ scratch.data(i) = 0.0
+ grad.data(i) = 0.0
+ x.data(i) = 0.0
+ dir.data(i) = 0.0
+ lastDir.data(i) = 0.0
+ res.data(i) = 0.0
+ i = i + 1
+ }
+ }
+ }
+
+ def createWorkspace(n: Int): Workspace = {
+ new Workspace(n)
+ }
+
+ /**
+ * Solve a least squares problem, possibly with nonnegativity
constraints, by a modified
+ * projected gradient method. That is, find x minimising ||Ax - b||_2
given A^T A and A^T b.
+ *
+ * We solve the problem
+ * min_x 1/2 x^T ata x^T - x^T atb
+ * subject to x >= 0 (if nonnegative == true)
+ *
+ * The method used is similar to one described by Polyak (B. T. Polyak,
The conjugate gradient
+ * method in extremal problems, Zh. Vychisl. Mat. Mat. Fiz. 9(4)(1969),
pp. 94-112) for bound-
+ * constrained nonlinear programming. Polyak unconditionally uses a
conjugate gradient
+ * direction, however, while this method only uses a conjugate gradient
direction if the last
+ * iteration did not cause a previously-inactive constraint to become
active.
+ */
+ def solve(ata: DoubleMatrix, atb: DoubleMatrix, nonnegative: Boolean,
+ ws: Workspace): Array[Double] = {
+ ws.wipe()
+
+ val n = atb.rows
+ val scratch = ws.scratch
+
+ // find the optimal unconstrained step
--- End diff --
`unconstrained step` -> `unconstrained CG step`? Both gradient descent and
CG are used.
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