Examples of org.apache.commons.math3.optimization.SimpleValueChecker

Package org.apache.commons.math3.optimization

Examples of org.apache.commons.math3.optimization.SimpleValueChecker

org.apache.commons.math3.optimization.SimpleValueChecker
Simple implementation of the {@link ConvergenceChecker} 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.
The {@link #converged(int,PointValuePair,PointValuePair) converged}method will also return {@code true} if the number of iterations has been set(see {@link #SimpleValueChecker(double,double,int) this constructor}). @deprecated As of 3.1 (to be removed in 4.0). @since 3.0

    /**
     * @param rel Relative threshold.
     * @param abs Absolute threshold.
     */
    public SimplexOptimizer(double rel, double abs) {
        this(new SimpleValueChecker(rel, abs));
    }

View Full Code Here

        Assert.assertTrue(optimizer.getEvaluations() < 150);
    }


    @Test
    public void testMaximize2() {
        SimplexOptimizer optimizer = new SimplexOptimizer(new SimpleValueChecker(1e-15, 1e-30));
        optimizer.setSimplex(new MultiDirectionalSimplex(new double[] { 0.2, 0.2 }));
        final FourExtrema fourExtrema = new FourExtrema();


        final PointValuePair optimum
            = optimizer.optimize(200, fourExtrema, GoalType.MAXIMIZE, new double[] { 1, 0 });

View Full Code Here

    public void testTrivial() {
        LinearProblem problem =
            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-6, 1e-6));
        PointValuePair optimum =
            optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0 });
        Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
        Assert.assertEquals(0.0, optimum.getValue(), 1.0e-10);
    }

View Full Code Here

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


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

View Full Code Here

                { 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 });
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-6, 1e-6));
        PointValuePair optimum =
            optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0, 0, 0, 0 });
        for (int i = 0; i < problem.target.length; ++i) {
            Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10);
        }

View Full Code Here

                { -1,  1, 0 },
                {  0, -1, 1 }
        }, new double[] { 1, 1, 1});
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-6, 1e-6));
        PointValuePair optimum =
            optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0 });
        Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
        Assert.assertEquals(2.0, optimum.getPoint()[1], 1.0e-10);
        Assert.assertEquals(3.0, optimum.getPoint()[2], 1.0e-10);

View Full Code Here

                    }
                };


        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-13, 1e-13),
                                                    new BrentSolver(),
                                                    preconditioner);
                                                    
        PointValuePair optimum =
            optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0, 0, 0, 0 });

View Full Code Here

                {  2, 1,  3 },
                { -3, 0, -9 }
        }, new double[] { 1, 1, 1 });
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-6, 1e-6));
        PointValuePair optimum =
                optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0 });
        Assert.assertTrue(optimum.getValue() > 0.5);
    }

View Full Code Here

                {  8.0, 6.0, 10.0,  9.0 },
                {  7.0, 5.0,  9.0, 10.0 }
        }, new double[] { 32, 23, 33, 31 });
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-13, 1e-13),
                                                    new BrentSolver(1e-15, 1e-15));
        PointValuePair optimum1 =
            optimizer.optimize(200, problem1, GoalType.MINIMIZE, new double[] { 0, 1, 2, 3 });
        Assert.assertEquals(1.0, optimum1.getPoint()[0], 1.0e-4);
        Assert.assertEquals(1.0, optimum1.getPoint()[1], 1.0e-4);

View Full Code Here

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


        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-6, 1e-6));
        PointValuePair optimum =
            optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 7, 6, 5, 4 });
        Assert.assertEquals(0, optimum.getValue(), 1.0e-10);


    }

View Full Code Here

0 1 2 3 4 5 6 7 8 9

TOP

Related Classes of org.apache.commons.math3.optimization.SimpleValueChecker

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.MultivariateFunction

org.apache.commons.math3.analysis.QuinticFunction

org.apache.commons.math3.analysis.solvers.BrentSolver

org.apache.commons.math3.analysis.UnivariateFunction

org.apache.commons.math3.complex.Complex

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.