Package org.apache.commons.math3.optim

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

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        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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    }

    @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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     * 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.

        @SuppressWarnings("unused")
        final double[] gn = doMath798(new GaussNewtonOptimizer(checker), maxEval, init);
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    @Test
    public void testMath798WithToleranceTooLowButNoException() {
        final double tol = 1e-100;
        final double[] init = new double[] { 0, 0 };
        final int maxEval = 10000; // Trying hard to fit.
        final SimpleVectorValueChecker checker = new SimpleVectorValueChecker(tol, tol, maxEval);

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

        for (int i = 0; i <= 1; i++) {
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    }

    @Test
    public void testRedundantUnsolvable() {
        // Gauss-Newton should not be able to solve redundant information
        checkUnsolvableProblem(new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-15, 1e-15)), false);
    }
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    @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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    @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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    @Test
    public void testIssue914() {
        LinearProblem problem = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        JacobianMultivariateVectorOptimizer underlyingOptimizer =
                new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6)) {
            @Override
            public PointVectorValuePair optimize(OptimizationData... optData) {
                // filter out simple bounds, as they are not supported
                // by the underlying optimizer, and we don't really care for this test
                OptimizationData[] filtered = optData.clone();
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     * of the runs succeed.
     */
    @Test(expected=TestException.class)
    public void testNoOptimum() {
        JacobianMultivariateVectorOptimizer underlyingOptimizer
            = new GaussNewtonOptimizer(true, new SimpleVectorValueChecker(1e-6, 1e-6));
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(12373523445l);
        RandomVectorGenerator generator
            = new UncorrelatedRandomVectorGenerator(1, new GaussianRandomGenerator(g));
        MultiStartMultivariateVectorOptimizer optimizer
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    /** default absolute tolerance of comparisons */
    public static final double TOl = 1e-10;

    public LeastSquaresBuilder base() {
        return new LeastSquaresBuilder()
                .checkerPair(new SimpleVectorValueChecker(1e-6, 1e-6))
                .maxEvaluations(100)
                .maxIterations(getMaxIterations());
    }
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Related Classes of org.apache.commons.math3.optim.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.interpolation.SmoothingPolynomialBicubicSplineInterpolator
  • 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
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