Examples of org.apache.commons.math3.optimization.PointValuePair

Package org.apache.commons.math3.optimization

Examples of org.apache.commons.math3.optimization.PointValuePair

org.apache.commons.math3.optimization.PointValuePair
This class holds a point and the value of an objective function at that point. @see PointVectorValuePair @see org.apache.commons.math3.analysis.MultivariateFunction @deprecated As of 3.1 (to be removed in 4.0). @since 3.0

    @Test(expected = DimensionMismatchException.class)
    public void testInputSigmaDimensionMismatch() {
        double[] startPoint = point(DIM,0.5);
        double[] insigma = point(DIM + 1, 0.5);
        double[][] boundaries = null;
        PointValuePair expected =
            new PointValuePair(point(DIM,1.0),0.0);
        doTest(new Rosen(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, true, 0, 1e-13,
                1e-13, 1e-6, 100000, expected);
    }

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    @Retry(3)
    public void testRosen() {
        double[] startPoint = point(DIM,0.1);
        double[] insigma = point(DIM,0.1);
        double[][] boundaries = null;
        PointValuePair expected =
            new PointValuePair(point(DIM,1.0),0.0);
        doTest(new Rosen(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, true, 0, 1e-13,
                1e-13, 1e-6, 100000, expected);
        doTest(new Rosen(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, false, 0, 1e-13,

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    @Retry(3)
    public void testMaximize() {
        double[] startPoint = point(DIM,1.0);
        double[] insigma = point(DIM,0.1);
        double[][] boundaries = null;
        PointValuePair expected =
            new PointValuePair(point(DIM,0.0),1.0);
        doTest(new MinusElli(), startPoint, insigma, boundaries,
                GoalType.MAXIMIZE, LAMBDA, true, 0, 1.0-1e-13,
                2e-10, 5e-6, 100000, expected);
        doTest(new MinusElli(), startPoint, insigma, boundaries,
                GoalType.MAXIMIZE, LAMBDA, false, 0, 1.0-1e-13,

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    @Test
    public void testEllipse() {
        double[] startPoint = point(DIM,1.0);
        double[] insigma = point(DIM,0.1);
        double[][] boundaries = null;
        PointValuePair expected =
            new PointValuePair(point(DIM,0.0),0.0);
        doTest(new Elli(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, true, 0, 1e-13,
                1e-13, 1e-6, 100000, expected);
        doTest(new Elli(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, false, 0, 1e-13,

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    @Test
    public void testElliRotated() {
        double[] startPoint = point(DIM,1.0);
        double[] insigma = point(DIM,0.1);
        double[][] boundaries = null;
        PointValuePair expected =
            new PointValuePair(point(DIM,0.0),0.0);
        doTest(new ElliRotated(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, true, 0, 1e-13,
                1e-13, 1e-6, 100000, expected);
        doTest(new ElliRotated(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, false, 0, 1e-13,

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    @Test
    public void testCigar() {
        double[] startPoint = point(DIM,1.0);
        double[] insigma = point(DIM,0.1);
        double[][] boundaries = null;
        PointValuePair expected =
            new PointValuePair(point(DIM,0.0),0.0);
        doTest(new Cigar(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, true, 0, 1e-13,
                1e-13, 1e-6, 200000, expected);
        doTest(new Cigar(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, false, 0, 1e-13,

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    @Test
    public void testCigarWithBoundaries() {
        double[] startPoint = point(DIM,1.0);
        double[] insigma = point(DIM,0.1);
        double[][] boundaries = boundaries(DIM, -1e100, Double.POSITIVE_INFINITY);
        PointValuePair expected =
            new PointValuePair(point(DIM,0.0),0.0);
        doTest(new Cigar(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, true, 0, 1e-13,
                1e-13, 1e-6, 200000, expected);
        doTest(new Cigar(), startPoint, insigma, boundaries,
                GoalType.MINIMIZE, LAMBDA, false, 0, 1e-13,

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        final int maxEval = 1000;
        // Very small absolute tolerance to rely solely on the relative
        // tolerance as a stopping criterion
        final MultivariateOptimizer optim = new PowellOptimizer(relTol, 1e-100);


        final PointValuePair funcResult = optim.optimize(maxEval, func, GoalType.MINIMIZE, 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(maxEval, funcScaled, GoalType.MINIMIZE, init);
        final double funcScaledValue = funcScaled.value(funcScaledResult.getPoint());
        final int funcScaledEvaluations = optim.getEvaluations();


        // Check that both minima provide the same objective funciton values,
        // within the relative function tolerance.
        Assert.assertEquals(1, funcScaledValue / (scale * funcValue), relTol);

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


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


        for (int i = 0, dim = optimum.length; i < dim; i++) {
            Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
                                optimum[i], point[i], pointTol);
        }
    }

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


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


        for (int i = 0, dim = optimum.length; i < dim; i++) {
            Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
                                optimum[i], point[i], pointTol);
        }
    }

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Related Classes of org.apache.commons.math3.optimization.PointValuePair

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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