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

                {  0,  0,   0,  0,       1, 1 }
        }, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});


        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);
        assertEquals( 3.0, optimum.getPoint()[0], 1.0e-10);

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

                {  2, 1,  3 },
                { -3, 0, -9 }
        }, new double[] { 1, 1, 1 });
        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
        try {
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 }, new double[] { 0, 0, 0 });
            fail("an exception should have been caught");
        } catch (OptimizationException ee) {
            // expected behavior

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

                {  8.0, 6.0, 10.0,  9.0 },
                {  7.0, 5.0,  9.0, 10.0 }
        }, new double[] { 32, 23, 33, 31 });
        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
        VectorialPointValuePair optimum1 =
            optimizer.optimize(problem1, problem1.target, new double[] { 1, 1, 1, 1 },
                               new double[] { 0, 1, 2, 3 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(1.0, optimum1.getPoint()[0], 1.0e-10);

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

                { 2.0, 0.0,  1.0, 0.0 }
        }, new double[] { 7.0, 3.0, 5.0 });


        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
        try {
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1 },
                               new double[] { 7, 6, 5, 4 });
            fail("an exception should have been caught");
        } catch (OptimizationException ee) {

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

                 { 0.0, 0.0, -1.0,  1.0, 0.0,  1.0 },
                 { 0.0, 0.0,  0.0, -1.0, 1.0,  0.0 }
        }, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
        GaussNewtonOptimizer optimizer = new GaussNewtonOptimizer(true);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleVectorialValueChecker(1.0e-6, 1.0e-6));
        try {
            optimizer.optimize(problem, problem.target, new double[] { 1, 1, 1, 1, 1 },
                               new double[] { 2, 2, 2, 2, 2, 2 });
            fail("an exception should have been caught");
        } catch (OptimizationException ee) {

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

                { 1.0,  3.0 }
        }, new double[] { 3.0, 1.0, 5.0 });


        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[] { 1, 1 });
        assertEquals(0, optimizer.getRMS(), 1.0e-10);
        assertEquals(2.0, optimum.getPoint()[0], 1.0e-8);

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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

    /** 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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