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

        }, 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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        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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                {  0, -1, 1 }
        }, new double[] { 1, 1, 1});
        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 });
        assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
        assertEquals(2.0, optimum.getPoint()[1], 1.0e-10);
        assertEquals(3.0, optimum.getPoint()[2], 1.0e-10);

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                d[4] /= 2 * (1 + epsilon * epsilon);
                d[5] /= 4.0;
                return d;
            }
        });
        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-13, 1.0e-13));


        RealPointValuePair optimum =
            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 0, 0, 0, 0, 0, 0 });
        assertEquals( 3.0, optimum.getPoint()[0], 1.0e-10);
        assertEquals( 4.0, optimum.getPoint()[1], 1.0e-10);

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                { -3, 0, -9 }
        }, new double[] { 1, 1, 1 });
        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 });
        assertTrue(optimum.getValue() > 0.5);
    }

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                {  7.0, 5.0,  9.0, 10.0 }
        }, new double[] { 32, 23, 33, 31 });
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
        optimizer.setMaxIterations(100);
        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-13, 1.0e-13));
        BrentSolver solver = new BrentSolver();
        solver.setAbsoluteAccuracy(1.0e-15);
        solver.setRelativeAccuracy(1.0e-15);
        optimizer.setLineSearchSolver(solver);
        RealPointValuePair optimum1 =

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        }, new double[] { 7.0, 3.0, 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[] { 7, 6, 5, 4 });
        assertEquals(0, optimum.getValue(), 1.0e-10);


    }

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

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

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