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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        final MultivariateFunction func = new SumSincFunction(-1);
        final PowellOptimizer optim = new PowellOptimizer(1e-8, 1e-5,
                                                          1e-4, 1e-4);

        optim.optimize(new MaxEval(100),
                       new ObjectiveFunction(func),
                       GoalType.MINIMIZE,
                       new InitialGuess(new double[] { -3, 0 }),
                       new SimpleBounds(new double[] { -5, -1 },
                                        new double[] { 5, 1 }));
    }
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        // Very small absolute tolerance to rely solely on the relative
        // tolerance as a stopping criterion
        final PowellOptimizer optim = new PowellOptimizer(relTol, 1e-100);

        final PointValuePair funcResult = optim.optimize(new MaxEval(maxEval),
                                                         new ObjectiveFunction(func),
                                                         GoalType.MINIMIZE,
                                                         new InitialGuess(init));
        final double funcValue = func.value(funcResult.getPoint());
        final int funcEvaluations = optim.getEvaluations();

        final double scale = 1e10;
        final MultivariateFunction funcScaled = new MultivariateFunction() {
                public double value(double[] x) {
                    return scale * func.value(x);
                }
            };

        final PointValuePair funcScaledResult = optim.optimize(new MaxEval(maxEval),
                                                               new ObjectiveFunction(funcScaled),
                                                               GoalType.MINIMIZE,
                                                               new InitialGuess(init));
        final double funcScaledValue = funcScaled.value(funcScaledResult.getPoint());
        final int funcScaledEvaluations = optim.getEvaluations();

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                        double fTol,
                        double pointTol) {
        final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d));

        final PointValuePair result = optim.optimize(new MaxEval(1000),
                                                     new ObjectiveFunction(func),
                                                     goal,
                                                     new InitialGuess(init));
        final double[] point = result.getPoint();

        for (int i = 0, dim = optimum.length; i < dim; i++) {
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                        double pointTol) {
        final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d),
                                                          fLineTol, Math.ulp(1d));

        final PointValuePair result = optim.optimize(new MaxEval(1000),
                                                     new ObjectiveFunction(func),
                                                     goal,
                                                     new InitialGuess(init));
        final double[] point = result.getPoint();

        for (int i = 0, dim = optimum.length; i < dim; 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-10, 1e-30);
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 new ObjectiveFunction(fourExtrema),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { -3, 0 }),
                                 new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
        Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 2e-7);
        Assert.assertEquals(fourExtrema.yP, optimum.getPoint()[1], 2e-5);
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        SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 new ObjectiveFunction(fourExtrema),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { 1, 0 }),
                                 new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
        Assert.assertEquals(fourExtrema.xP, optimum.getPoint()[0], 5e-6);
        Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 6e-6);
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        SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30);
        final FourExtrema fourExtrema = new FourExtrema();

        final PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 new ObjectiveFunction(fourExtrema),
                                 GoalType.MAXIMIZE,
                                 new InitialGuess(new double[] { -3, 0 }),
                                 new NelderMeadSimplex(new double[] { 0.2, 0.2 }));
        Assert.assertEquals(fourExtrema.xM, optimum.getPoint()[0], 1e-5);
        Assert.assertEquals(fourExtrema.yM, optimum.getPoint()[1], 3e-6);
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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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