MATH-1596: Remove dependency on "RandomVectorGenerator".

commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/BaseMultiStartMultivariateOptimizer.java

@@ -16,10 +16,10 @@
  */
 package org.apache.commons.math4.legacy.optim;
 
+import java.util.function.Supplier;
 import org.apache.commons.math4.legacy.exception.MathIllegalStateException;
 import org.apache.commons.math4.legacy.exception.NotStrictlyPositiveException;
 import org.apache.commons.math4.legacy.exception.TooManyEvaluationsException;
-import org.apache.commons.math4.legacy.random.RandomVectorGenerator;
 
 /**
  * Base class multi-start optimizer for a multivariate function.
@@ -41,9 +41,9 @@ public abstract class BaseMultiStartMultivariateOptimizer<PAIR>
     /** Number of evaluations already performed for all starts. */
     private int totalEvaluations;
     /** Number of starts to go. */
-    private int starts;
+    private final int starts;
-    /** Random generator for multi-start. */
+    /** Generator of start points ("multi-start"). */
-    private RandomVectorGenerator generator;
+    private final Supplier<double[]> generator;
     /** Optimization data. */
     private OptimizationData[] optimData;
     /**
@@ -72,12 +72,12 @@ public abstract class BaseMultiStartMultivariateOptimizer<PAIR>
      * @param starts Number of starts to perform. If {@code starts == 1},
      * the {@link #optimize(OptimizationData[]) optimize} will return the
      * same solution as the given {@code optimizer} would return.
-     * @param generator Random vector generator to use for restarts.
+     * @param generator Generator to use for restarts.
      * @throws NotStrictlyPositiveException if {@code starts < 1}.
      */
     public BaseMultiStartMultivariateOptimizer(final BaseMultivariateOptimizer<PAIR> optimizer,
                                                final int starts,
-                                               final RandomVectorGenerator generator) {
+                                               final Supplier<double[]> generator) {
         super(optimizer.getConvergenceChecker());
 
         if (starts < 1) {
@@ -185,7 +185,7 @@ public abstract class BaseMultiStartMultivariateOptimizer<PAIR>
                         if (attempts++ >= getMaxEvaluations()) {
                             throw new TooManyEvaluationsException(getMaxEvaluations());
                         }
-                        s = generator.nextVector();
+                        s = generator.get();
                         for (int k = 0; s != null && k < s.length; ++k) {
                             if ((min != null && s[k] < min[k]) || (max != null && s[k] > max[k])) {
                                 // reject the vector

commons-math-legacy/src/main/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/MultiStartMultivariateOptimizer.java

@@ -20,12 +20,12 @@ import java.util.ArrayList;
 import java.util.Collections;
 import java.util.Comparator;
 import java.util.List;
+import java.util.function.Supplier;
 
 import org.apache.commons.math4.legacy.exception.NotStrictlyPositiveException;
 import org.apache.commons.math4.legacy.exception.NullArgumentException;
 import org.apache.commons.math4.legacy.optim.BaseMultiStartMultivariateOptimizer;
 import org.apache.commons.math4.legacy.optim.PointValuePair;
-import org.apache.commons.math4.legacy.random.RandomVectorGenerator;
 
 /**
  * Multi-start optimizer.
@@ -50,16 +50,14 @@ public class MultiStartMultivariateOptimizer
      * @param starts Number of starts to perform.
      * If {@code starts == 1}, the result will be same as if {@code optimizer}
      * is called directly.
-     * @param generator Random vector generator to use for restarts.
+     * @param generator Generator to use for restarts.
      * @throws NullArgumentException if {@code optimizer} or {@code generator}
      * is {@code null}.
      * @throws NotStrictlyPositiveException if {@code starts < 1}.
      */
     public MultiStartMultivariateOptimizer(final MultivariateOptimizer optimizer,
                                            final int starts,
-                                           final RandomVectorGenerator generator)
+                                           final Supplier<double[]> generator) {
-        throws NullArgumentException,
-        NotStrictlyPositiveException {
         super(optimizer, starts, generator);
         this.optimizer = optimizer;
     }

commons-math-legacy/src/test/java/org/apache/commons/math4/legacy/optim/nonlinear/scalar/MultiStartMultivariateOptimizerTest.java

@@ -16,6 +16,7 @@
  */
 package org.apache.commons.math4.legacy.optim.nonlinear.scalar;
 
+import java.util.function.Supplier;
 import org.apache.commons.geometry.euclidean.twod.Vector2D;
 import org.apache.commons.math4.legacy.analysis.MultivariateFunction;
 import org.apache.commons.math4.legacy.optim.InitialGuess;
@@ -30,7 +31,6 @@ import org.apache.commons.rng.UniformRandomProvider;
 import org.apache.commons.rng.simple.RandomSource;
 import org.apache.commons.rng.sampling.distribution.GaussianSampler;
 import org.apache.commons.rng.sampling.distribution.ZigguratNormalizedGaussianSampler;
-import org.apache.commons.math4.legacy.random.RandomVectorGenerator;
 import org.junit.Assert;
 import org.junit.Test;
 
@@ -49,9 +49,9 @@ public class MultiStartMultivariateOptimizerTest {
         GradientMultivariateOptimizer underlying
             = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                       new SimpleValueChecker(1e-10, 1e-10));
-        final RandomVectorGenerator generator = gaussianRandom(new double[] { 50, 50 },
+        final Supplier<double[]> generator = gaussianRandom(new double[] { 50, 50 },
-                                                               new double[] { 10, 10 },
+                                                            new double[] { 10, 10 },
-                                                               RandomSource.create(RandomSource.MT_64));
+                                                            RandomSource.create(RandomSource.MT_64));
         int nbStarts = 10;
         MultiStartMultivariateOptimizer optimizer
             = new MultiStartMultivariateOptimizer(underlying, nbStarts, generator);
@@ -88,9 +88,9 @@ public class MultiStartMultivariateOptimizerTest {
                 { 0.9, 1.2 } ,
                 {  3.5, -2.3 }
             });
-        final RandomVectorGenerator generator = gaussianRandom(new double[] { 0, 0 },
+        final Supplier<double[]> generator = gaussianRandom(new double[] { 0, 0 },
-                                                               new double[] { 1, 1 },
+                                                            new double[] { 1, 1 },
-                                                               RandomSource.create(RandomSource.MT_64));
+                                                            RandomSource.create(RandomSource.MT_64));
         int nbStarts = 10;
         MultiStartMultivariateOptimizer optimizer
             = new MultiStartMultivariateOptimizer(underlying, nbStarts, generator);
@@ -136,18 +136,18 @@ public class MultiStartMultivariateOptimizerTest {
      * @return a random array generator where each element is a Gaussian
      * sampling with the given mean and standard deviation.
      */
-    private RandomVectorGenerator gaussianRandom(final double[] mean,
+    private Supplier<double[]> gaussianRandom(final double[] mean,
-                                                 final double[] stdev,
+                                              final double[] stdev,
-                                                 final UniformRandomProvider rng) {
+                                              final UniformRandomProvider rng) {
         final ZigguratNormalizedGaussianSampler normalized = new ZigguratNormalizedGaussianSampler(rng);
         final GaussianSampler[] samplers = new GaussianSampler[mean.length];
         for (int i = 0; i < mean.length; i++) {
             samplers[i] = new GaussianSampler(normalized, mean[i], stdev[i]);
         }
 
-        return new RandomVectorGenerator() {
+        return new Supplier<double[]>() {
             @Override
-            public double[] nextVector() {
+            public double[] get() {
                 final double[] s = new double[mean.length];
                 for (int i = 0; i < mean.length; i++) {
                     s[i] = samplers[i].sample();