Examples of SimpleVectorialValueChecker

org.apache.commons.math.optimization.SimpleVectorialValueChecker
Simple implementation of the {@link VectorialConvergenceChecker} 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.
@version $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 août 2010) $ @since 2.0

Examples of org.apache.commons.math.optimization.SimpleVectorialValueChecker

                { 1.0,  3.0 }
        }, new double[] { 3.0, 1.0, 4.0 });


        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
        optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 1, 1 });
        assertTrue(optimizer.getRMS() > 0.1);


    }

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Examples of org.apache.commons.math.optimization.SimpleVectorialValueChecker

    public void testInconsistentSizes() throws FunctionEvaluationException, OptimizationException {
        LinearProblem problem =
            new LinearProblem(new double[][] { { 1, 0 }, { 0, 1 } }, new double[] { -1, 1 });
        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));


        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1, 1 }, new double[] { 0, 0 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(-1, optimum.getPoint()[0], 1.0e-10);

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Examples of org.apache.commons.math.optimization.SimpleVectorialValueChecker

        circle.addPoint(110.0, -20.0);
        circle.addPoint( 35.0,  15.0);
        circle.addPoint( 45.0,  97.0);
        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-13, 1.0e-13));
        VectorialPointValuePair optimum =
            optimizer.optimize(circle, new double[] { 0, 0, 0, 0, 0 },
                               new double[] { 1, 1, 1, 1, 1 },
                               new double[] { 98.680, 47.345 });
        assertEquals(1.768262623567235,  Math.sqrt(circle.getN()) * optimizer.getRMS(),  1.0e-10);

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Examples of org.apache.commons.math.optimization.SimpleVectorialValueChecker

        for (int i = 0; i < points.length; ++i) {
            circle.addPoint(points[i][0], points[i][1]);
        }
        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
        try {
            optimizer.optimize(circle, target, weights, new double[] { -12, -12 });
            fail("an exception should have been caught");
        } catch (OptimizationException ee) {
            // expected behavior

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Examples of org.apache.commons.math.optimization.SimpleVectorialValueChecker

        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();
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-10, 1.0e-10));
        VectorialPointValuePair optimum =
            optimizer.optimize(circle, target, weights, new double[] { -12, -12 });
        Point2D.Double center = new Point2D.Double(optimum.getPointRef()[0], optimum.getPointRef()[1]);
        assertTrue(optimizer.getEvaluations() < 25);
        assertTrue(optimizer.getJacobianEvaluations() < 20);

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Examples of org.apache.commons.math.optimization.SimpleVectorialValueChecker

    /** Simple constructor with default settings.
     * <p>The convergence check is set to a {@link SimpleVectorialValueChecker}
     * and the maximal number of evaluation is set to its default value.</p>
     */
    protected AbstractLeastSquaresOptimizer() {
        setConvergenceChecker(new SimpleVectorialValueChecker());
        setMaxIterations(DEFAULT_MAX_ITERATIONS);
        setMaxEvaluations(Integer.MAX_VALUE);
    }

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Examples of org.apache.commons.math.optimization.SimpleVectorialValueChecker

    public void testTrivial() throws FunctionEvaluationException, OptimizationException {
        LinearProblem problem =
            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1 }, new double[] { 0 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
        assertEquals(3.0, optimum.getValue()[0], 1.0e-10);

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Examples of org.apache.commons.math.optimization.SimpleVectorialValueChecker

            new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
                              new double[] { 4.0, 6.0, 1.0 });


        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
        assertEquals(3.0, optimum.getPoint()[1], 1.0e-10);

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Examples of org.apache.commons.math.optimization.SimpleVectorialValueChecker

                { 0, 0, 0, 0, 2, 0 },
                { 0, 0, 0, 0, 0, 2 }
        }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1, 1, 1, 1 },
                               new double[] { 0, 0, 0, 0, 0, 0 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        for (int i = 0; i < problem.target.length; ++i) {

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Examples of org.apache.commons.math.optimization.SimpleVectorialValueChecker

                { -1,  1, 0 },
                {  0, -1, 1 }
        }, new double[] { 1, 1, 1});
        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
        VectorialPointValuePair optimum =
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
        assertEquals(2.0, optimum.getPoint()[1], 1.0e-10);

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