Examples of org.apache.commons.math3.analysis.UnivariateFunction

Package org.apache.commons.math3.analysis

Examples of org.apache.commons.math3.analysis.UnivariateFunction

org.apache.commons.math3.analysis.UnivariateFunction
x value that caused the problem. private final double x; public LocalException(double x) { this.x = x; } public double getX() { return x; } } private static class MyFunction implements UnivariateFunction { public double value(double x) { double y = hugeFormula(x); if (somethingBadHappens) { throw new LocalException(x); } return y; } } public void compute() { try { solver.solve(maxEval, new MyFunction(a, b, c), min, max); } catch (LocalException le) { // Retrieve the x value. } } As shown, the exception is local to the user's code and it is guaranteed that Apache Commons Math will not catch it.

@Deprecated
public final class BrentOptimizerTest {


    @Test
    public void testSinMin() {
        UnivariateFunction f = new Sin();
        UnivariateOptimizer optimizer = new BrentOptimizer(1e-10, 1e-14);
        Assert.assertEquals(3 * Math.PI / 2, optimizer.optimize(200, f, GoalType.MINIMIZE, 4, 5).getPoint(), 1e-8);
        Assert.assertTrue(optimizer.getEvaluations() <= 50);
        Assert.assertEquals(200, optimizer.getMaxEvaluations());
        Assert.assertEquals(3 * Math.PI / 2, optimizer.optimize(200, f, GoalType.MINIMIZE, 1, 5).getPoint(), 1e-8);

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        }
    }


    @Test
    public void testSinMinWithValueChecker() {
        final UnivariateFunction f = new Sin();
        final ConvergenceChecker<UnivariatePointValuePair> checker = new SimpleUnivariateValueChecker(1e-5, 1e-14);
        // The default stopping criterion of Brent's algorithm should not
        // pass, but the search will stop at the given relative tolerance
        // for the function value.
        final UnivariateOptimizer optimizer = new BrentOptimizer(1e-10, 1e-14, checker);

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    @Test
    public void testBoundaries() {
        final double lower = -1.0;
        final double upper = +1.0;
        UnivariateFunction f = new UnivariateFunction() {            
            public double value(double x) {
                if (x < lower) {
                    throw new NumberIsTooSmallException(x, lower, true);
                } else if (x > upper) {
                    throw new NumberIsTooLargeException(x, upper, true);

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    }


    @Test
    public void testQuinticMin() {
        // The function has local minima at -0.27195613 and 0.82221643.
        UnivariateFunction f = new QuinticFunction();
        UnivariateOptimizer optimizer = new BrentOptimizer(1e-10, 1e-14);
        Assert.assertEquals(-0.27195613, optimizer.optimize(200, f, GoalType.MINIMIZE, -0.3, -0.2).getPoint(), 1.0e-8);
        Assert.assertEquals( 0.82221643, optimizer.optimize(200, f, GoalType.MINIMIZE,  0.3,  0.9).getPoint(), 1.0e-8);
        Assert.assertTrue(optimizer.getEvaluations() <= 50);

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    }


    @Test
    public void testQuinticMinStatistics() {
        // The function has local minima at -0.27195613 and 0.82221643.
        UnivariateFunction f = new QuinticFunction();
        UnivariateOptimizer optimizer = new BrentOptimizer(1e-11, 1e-14);


        final DescriptiveStatistics[] stat = new DescriptiveStatistics[2];
        for (int i = 0; i < stat.length; i++) {
            stat[i] = new DescriptiveStatistics();

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    @Test
    public void testQuinticMax() {
        // The quintic function has zeros at 0, +-0.5 and +-1.
        // The function has a local maximum at 0.27195613.
        UnivariateFunction f = new QuinticFunction();
        UnivariateOptimizer optimizer = new BrentOptimizer(1e-12, 1e-14);
        Assert.assertEquals(0.27195613, optimizer.optimize(100, f, GoalType.MAXIMIZE, 0.2, 0.3).getPoint(), 1e-8);
        try {
            optimizer.optimize(5, f, GoalType.MAXIMIZE, 0.2, 0.3);
            Assert.fail("an exception should have been thrown");

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        }
    }


    @Test
    public void testMinEndpoints() {
        UnivariateFunction f = new Sin();
        UnivariateOptimizer optimizer = new BrentOptimizer(1e-8, 1e-14);


        // endpoint is minimum
        double result = optimizer.optimize(50, f, GoalType.MINIMIZE, 3 * Math.PI / 2, 5).getPoint();
        Assert.assertEquals(3 * Math.PI / 2, result, 1e-6);

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        Assert.assertEquals(3 * Math.PI / 2, result, 1e-6);
    }


    @Test
    public void testMath832() {
        final UnivariateFunction f = new UnivariateFunction() {
                public double value(double x) {
                    final double sqrtX = FastMath.sqrt(x);
                    final double a = 1e2 * sqrtX;
                    final double b = 1e6 / x;
                    final double c = 1e4 / sqrtX;

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    @Test
    public void testKeepInitIfBest() {
        final double minSin = 3 * Math.PI / 2;
        final double offset = 1e-8;
        final double delta = 1e-7;
        final UnivariateFunction f1 = new Sin();
        final UnivariateFunction f2 = new StepFunction(new double[] { minSin, minSin + offset, minSin + 2 * offset},
                                                       new double[] { 0, -1, 0 });
        final UnivariateFunction f = FunctionUtils.add(f1, f2);
        // A slightly less stringent tolerance would make the test pass
        // even with the previous implementation.
        final double relTol = 1e-8;
        final UnivariateOptimizer optimizer = new BrentOptimizer(relTol, 1e-100);
        final double init = minSin + 1.5 * offset;
        final UnivariatePointValuePair result
            = optimizer.optimize(200, f, GoalType.MINIMIZE,
                                 minSin - 6.789 * delta,
                                 minSin + 9.876 * delta,
                                 init);


        final double sol = result.getPoint();
        final double expected = init;


//         System.out.println("numEval=" + numEval);
//         System.out.println("min=" + init + " f=" + f.value(init));
//         System.out.println("sol=" + sol + " f=" + f.value(sol));
//         System.out.println("exp=" + expected + " f=" + f.value(expected));


        Assert.assertTrue("Best point not reported", f.value(sol) <= f.value(expected));
    }

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    @Test
    public void testMath855() {
        final double minSin = 3 * Math.PI / 2;
        final double offset = 1e-8;
        final double delta = 1e-7;
        final UnivariateFunction f1 = new Sin();
        final UnivariateFunction f2 = new StepFunction(new double[] { minSin, minSin + offset, minSin + 5 * offset },
                                                       new double[] { 0, -1, 0 });
        final UnivariateFunction f = FunctionUtils.add(f1, f2);
        final UnivariateOptimizer optimizer = new BrentOptimizer(1e-8, 1e-100);
        final UnivariatePointValuePair result
            = optimizer.optimize(200, f, GoalType.MINIMIZE,
                                 minSin - 6.789 * delta,
                                 minSin + 9.876 * delta);


        final double sol = result.getPoint();
        final double expected = 4.712389027602411;


        // System.out.println("min=" + (minSin + offset) + " f=" + f.value(minSin + offset));
        // System.out.println("sol=" + sol + " f=" + f.value(sol));
        // System.out.println("exp=" + expected + " f=" + f.value(expected));


        Assert.assertTrue("Best point not reported", f.value(sol) <= f.value(expected));
    }

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Related Classes of org.apache.commons.math3.analysis.UnivariateFunction

org.apache.commons.math3.analysis.differentiation.FiniteDifferencesDifferentiatorTest

org.apache.commons.math3.analysis.function.GaussianTest

org.apache.commons.math3.analysis.function.HarmonicOscillatorTest

org.apache.commons.math3.analysis.function.LogisticTest

org.apache.commons.math3.analysis.function.LogitTest

org.apache.commons.math3.analysis.function.SigmoidTest

org.apache.commons.math3.analysis.function.Sin

org.apache.commons.math3.analysis.function.Sinc

org.apache.commons.math3.analysis.function.SincTest

org.apache.commons.math3.analysis.function.SqrtTest

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