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

Package org.apache.commons.math3.optimization

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

org.apache.commons.math3.util.Incrementor
Simple implementation of the {@link ConvergenceChecker} interface usingonly objective function values. Convergence is considered to have been reached if either the relative difference between the objective function values is smaller than a threshold or if either the absolute difference between the objective function values is smaller than another threshold for all vectors elements.
The {@link #converged(int,PointVectorValuePair,PointVectorValuePair) converged}method will also return {@code true} if the number of iterations has been set(see {@link #SimpleVectorValueChecker(double,double,int) this constructor}). @deprecated As of 3.1 (to be removed in 4.0). @since 3.0

        circle.addPoint(110.0, -20.0);
        circle.addPoint( 35.0,  15.0);
        circle.addPoint( 45.0,  97.0);


        GaussNewtonOptimizer optimizer
            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-13, 1.0e-13));


        PointVectorValuePair optimum =
            optimizer.optimize(100, circle, new double[] { 0, 0, 0, 0, 0 },
                               new double[] { 1, 1, 1, 1, 1 },
                               new double[] { 98.680, 47.345 });

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        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }


        GaussNewtonOptimizer optimizer
            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));


        optimizer.optimize(100, circle, target, weights, new double[] { -12, -12 });
    }

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        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }


        GaussNewtonOptimizer optimizer
            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));


        PointVectorValuePair optimum =
            optimizer.optimize(100, circle, target, weights, new double[] { 0, 0 });
        Assert.assertEquals(-0.1517383071957963, optimum.getPointRef()[0], 1.0e-6);
        Assert.assertEquals(0.2074999736353867,  optimum.getPointRef()[1], 1.0e-6);

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        Arrays.fill(weights, 2.0);
        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }
        LevenbergMarquardtOptimizer optimizer
            = new LevenbergMarquardtOptimizer(new SimpleVectorValueChecker(1.0e-8, 1.0e-8));
        PointVectorValuePair optimum =
            optimizer.optimize(100, circle, target, weights, new double[] { -12, -12 });
        Point2D.Double center = new Point2D.Double(optimum.getPointRef()[0], optimum.getPointRef()[1]);
        Assert.assertTrue(optimizer.getEvaluations() < 25);
        Assert.assertTrue(optimizer.getJacobianEvaluations() < 20);

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     * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
     * @throws NotStrictlyPositiveException if {@code mean <= 0}.
     * @since 2.1
     */
    public ExponentialDistribution(double mean, double inverseCumAccuracy) {
        this(new Well19937c(), mean, inverseCumAccuracy);
    }

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     * @param upper Upper bound (inclusive) of this distribution.
     * @throws NumberIsTooLargeException if {@code lower >= upper}.
     */
    public UniformIntegerDistribution(int lower, int upper)
        throws NumberIsTooLargeException {
        this(new Well19937c(), lower, upper);
    }

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     * @throws NumberIsTooLargeException if {@code a >= b} or if {@code c > b}.
     * @throws NumberIsTooSmallException if {@code c < a}.
     */
    public TriangularDistribution(double a, double c, double b)
        throws NumberIsTooLargeException, NumberIsTooSmallException {
        this(new Well19937c(), a, c, b);
    }

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        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }


        covarianceImpl =

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        geoMeanImpl = new StorelessUnivariateStatistic[k];
        meanImpl    = new StorelessUnivariateStatistic[k];


        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();

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     * @param checker Convergence checker.
     */
    protected BaseOptimizer(ConvergenceChecker<PAIR> checker) {
        this.checker = checker;


        evaluations = new Incrementor(0, new MaxEvalCallback());
        iterations = new Incrementor(0, new MaxIterCallback());
    }

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

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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