Examples of SimpleVectorValueChecker

org.apache.commons.math3.optim.SimpleVectorValueChecker
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}). @since 3.0
org.apache.commons.math3.optimization.SimpleVectorValueChecker
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

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

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


        LeastSquaresProblem lsp = builder(circle)
                .checkerPair(new SimpleVectorValueChecker(1e-30, 1e-30))
                .maxIterations(Integer.MAX_VALUE)
                .start(new double[]{98.680, 47.345})
                .build();


        optimizer.optimize(lsp);

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

        }
        this.xDegree = xDegree;
        this.yDegree = yDegree;


        final double safeFactor = 1e2;
        final SimpleVectorValueChecker checker
            = new SimpleVectorValueChecker(safeFactor * Precision.EPSILON,
                                           safeFactor * Precision.SAFE_MIN);
        xFitter = new PolynomialFitter(new GaussNewtonOptimizer(false, checker));
        yFitter = new PolynomialFitter(new GaussNewtonOptimizer(false, checker));
    }

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

public class GaussNewtonOptimizerTest
    extends AbstractLeastSquaresOptimizerAbstractTest {


    @Override
    public AbstractLeastSquaresOptimizer createOptimizer() {
        return new GaussNewtonOptimizer(new SimpleVectorValueChecker(1.0e-6, 1.0e-6));
    }

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

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


        GaussNewtonOptimizer optimizer
            = new GaussNewtonOptimizer(new SimpleVectorValueChecker(1e-30, 1e-30));


        optimizer.optimize(new MaxEval(100),
                           circle.getModelFunction(),
                           circle.getModelFunctionJacobian(),
                           new Target(new double[] { 0, 0, 0, 0, 0 }),

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


    @Test(expected=NullPointerException.class)
    public void testGetOptimaBeforeOptimize() {


        JacobianMultivariateVectorOptimizer underlyingOptimizer
            = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator
            = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultiStartMultivariateVectorOptimizer optimizer

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

    @Test
    public void testTrivial() {
        LinearProblem problem
            = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        JacobianMultivariateVectorOptimizer underlyingOptimizer
            = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator
            = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultiStartMultivariateVectorOptimizer optimizer

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

     * decomposition.
     * @deprecated See {@link SimpleVectorValueChecker#SimpleVectorValueChecker()}
     */
    @Deprecated
    public GaussNewtonOptimizer(final boolean useLU) {
        this(useLU, new SimpleVectorValueChecker());
    }

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

     * The convergence check is set to a {@link SimpleVectorValueChecker}.
     * @deprecated See {@link SimpleVectorValueChecker#SimpleVectorValueChecker()}
     */
    @Deprecated
    protected BaseAbstractMultivariateVectorOptimizer() {
        this(new SimpleVectorValueChecker());
    }

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

    }


    @Test
    public void testMath798() {
        final double tol = 1e-14;
        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol);
        final double[] init = new double[] { 0, 0 };
        final int maxEval = 3;


        final double[] lm = doMath798(new LevenbergMarquardtOptimizer(checker), maxEval, init);
        final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);

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

     * stringent.
     */
    @Test(expected=TooManyEvaluationsException.class)
    public void testMath798WithToleranceTooLow() {
        final double tol = 1e-100;
        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol);
        final double[] init = new double[] { 0, 0 };
        final int maxEval = 10000; // Trying hard to fit.


        doMath798(new GaussNewtonOptimizer(checker), maxEval, init);
    }

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