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

Package org.apache.commons.math.optimization

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

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

                 { 0.0, 0.0,  0.0, -1.0, 1.0,  0.0 }
        }, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
        RealPointValuePair optimum =
            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 2, 2, 2, 2, 2, 2 });
        assertEquals(0, optimum.getValue(), 1.0e-10);
    }

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


        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
        RealPointValuePair optimum =
            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 1, 1 });
        assertEquals(2.0, optimum.getPoint()[0], 1.0e-8);
        assertEquals(1.0, optimum.getPoint()[1], 1.0e-8);

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


        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
        RealPointValuePair optimum =
            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 1, 1 });
        assertTrue(optimum.getValue() > 0.1);


    }

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        circle.addPoint( 35.0,  15.0);
        circle.addPoint( 45.0,  97.0);
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-30, 1.0e-30));
        BrentSolver solver = new BrentSolver();
        solver.setAbsoluteAccuracy(1.0e-13);
        solver.setRelativeAccuracy(1.0e-15);
        optimizer.setLineSearchSolver(solver);
        RealPointValuePair optimum =

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    private double[][] startConfiguration;


    /** Simple constructor.
     */
    protected DirectSearchOptimizer() {
        setConvergenceChecker(new SimpleScalarValueChecker());
        setMaxIterations(Integer.MAX_VALUE);
        setMaxEvaluations(Integer.MAX_VALUE);
    }

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

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                        double xTol,
                        double fTol,
                        double pointTol)
        throws MathException {
        final PowellOptimizer optim = new PowellOptimizer(xTol);
        optim.setConvergenceChecker(new SimpleScalarValueChecker(fTol, -1));


        final RealPointValuePair result = optim.optimize(func, goal, init);
        final double[] found = result.getPoint();


        for (int i = 0, dim = optimum.length; i < dim; i++) {

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        LinearProblem problem =
            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
        RealPointValuePair optimum =
            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 0 });
        assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
        assertEquals(0.0, optimum.getValue(), 1.0e-10);
    }

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                              new double[] { 4.0, 6.0, 1.0 });


        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
        RealPointValuePair optimum =
            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 0, 0 });
        assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
        assertEquals(3.0, optimum.getPoint()[1], 1.0e-10);
        assertEquals(0.0, optimum.getValue(), 1.0e-10);

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                { 0, 0, 0, 0, 0, 2 }
        }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
        RealPointValuePair optimum =
            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 0, 0, 0, 0, 0, 0 });
        for (int i = 0; i < problem.target.length; ++i) {
            assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10);
        }

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Related Classes of org.apache.commons.math.optimization.SimpleScalarValueChecker

org.apache.commons.math.optimization.direct.DirectSearchOptimizer

org.apache.commons.math.optimization.direct.MultiDirectionalTest

org.apache.commons.math.optimization.direct.NelderMeadTest

org.apache.commons.math.optimization.direct.PowellOptimizerTest

org.apache.commons.math.optimization.general.AbstractScalarDifferentiableOptimizer

org.apache.commons.math.optimization.general.NonLinearConjugateGradientOptimizerTest

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