Package org.apache.commons.math3.optim.nonlinear.scalar

Examples of org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction

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        int dim = startPoint.length;
        final int numIterpolationPoints = 2 * dim + 1 + additionalInterpolationPoints;
        BOBYQAOptimizer optim = new BOBYQAOptimizer(numIterpolationPoints);
        PointValuePair result = boundaries == null ?
            optim.optimize(new MaxEval(maxEvaluations),
                           new ObjectiveFunction(func),
                           goal,
                           SimpleBounds.unbounded(dim),
                           new InitialGuess(startPoint)) :
            optim.optimize(new MaxEval(maxEvaluations),
                           new ObjectiveFunction(func),
                           goal,
                           new InitialGuess(startPoint),
                           new SimpleBounds(boundaries[0],
                                            boundaries[1]));
//        System.out.println(func.getClass().getName() + " = "
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            this.factors = new BlockRealMatrix(factors);
            this.target  = target;
        }

        public ObjectiveFunction getObjectiveFunction() {
            return new ObjectiveFunction(new MultivariateFunction() {
                    public double value(double[] point) {
                        double[] y = factors.operate(point);
                        double sum = 0;
                        for (int i = 0; i < y.length; ++i) {
                            double ri = y[i] - target[i];
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    public void testBoundsUnsupported() {
        SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
        final FourExtrema fourExtrema = new FourExtrema();

        optimizer.optimize(new MaxEval(100),
                           new ObjectiveFunction(fourExtrema),
                           GoalType.MINIMIZE,
                           new InitialGuess(new double[] { -3, 0 }),
                           new NelderMeadSimplex(new double[] { 0.2, 0.2 }),
                           new SimpleBounds(new double[] { -5, -1 },
                                            new double[] { 5, 1 }));
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        SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(new MaxEval(200),
                                 new ObjectiveFunction(fourExtrema),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { -3, 0 }),
                                 new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
        Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 4e-6);
        Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
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        SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(new MaxEval(200),
                                 new ObjectiveFunction(fourExtrema),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { 1, 0 }),
                                 new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
        Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
        Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
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        SimplexOptimizer optimizer = new SimplexOptimizer(1e-11, 1e-30);
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(new MaxEval(200),
                                 new ObjectiveFunction(fourExtrema),
                                 GoalType.MAXIMIZE,
                                 new InitialGuess(new double[] { -3.0, 0.0 }),
                                 new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
        Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 7e-7);
        Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-7);
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        SimplexOptimizer optimizer = new SimplexOptimizer(new SimpleValueChecker(1e-15, 1e-30));
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(new MaxEval(200),
                                 new ObjectiveFunction(fourExtrema),
                                 GoalType.MAXIMIZE,
                                 new InitialGuess(new double[] { 1, 0 }),
                                 new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
        Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 2e-8);
        Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 3e-6);
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        count = 0;
        SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
        PointValuePair optimum
           = optimizer.optimize(new MaxEval(100),
                                new ObjectiveFunction(rosenbrock),
                                GoalType.MINIMIZE,
                                new InitialGuess(new double[] { -1.2, 1 }),
                                new MultiDirectionalSimplex(new double[][] {
                                        { -1.2,  1.0 },
                                        { 0.9, 1.2 },
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        count = 0;
        SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(1000),
                                 new ObjectiveFunction(powell),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { 3, -1, 0, 1 }),
                                 new MultiDirectionalSimplex(4));
        Assert.assertEquals(count, optimizer.getEvaluations());
        Assert.assertTrue(optimizer.getEvaluations() > 800);
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        // fails because MultiDirectional.iterateSimplex is looping forever
        // the while(true) should be replaced with a convergence check
        SimplexOptimizer optimizer = new SimplexOptimizer(1e-14, 1e-14);
        final Gaussian2D function = new Gaussian2D(0, 0, 1);
        PointValuePair estimate = optimizer.optimize(new MaxEval(1000),
                                                     new ObjectiveFunction(function),
                                                     GoalType.MAXIMIZE,
                                                     new InitialGuess(function.getMaximumPosition()),
                                                     new MultiDirectionalSimplex(2));
        final double EPSILON = 1e-5;
        final double expectedMaximum = function.getMaximum();
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Related Classes of org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
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