Package org.apache.commons.math.optimization

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

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      multiDirectional.setMaxIterations(100);
      multiDirectional.setMaxEvaluations(1000);

      final Gaussian2D function = new Gaussian2D(0.0, 0.0, 1.0);

      RealPointValuePair estimate = multiDirectional.optimize(function,
                                    GoalType.MAXIMIZE, function.getMaximumPosition());

      final double EPSILON = 1e-5;

      final double expectedMaximum = function.getMaximum();
      final double actualMaximum = estimate.getValue();
      Assert.assertEquals(expectedMaximum, actualMaximum, EPSILON);

      final double[] expectedPosition = function.getMaximumPosition();
      final double[] actualPosition = estimate.getPoint();
      Assert.assertEquals(expectedPosition[0], actualPosition[0], EPSILON );
      Assert.assertEquals(expectedPosition[1], actualPosition[1], EPSILON );

  }
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            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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        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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        }, 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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        }, 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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                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);
        assertEquals(-1.0, optimum.getPoint()[2], 1.0e-10);
        assertEquals(-2.0, optimum.getPoint()[3], 1.0e-10);
        assertEquals( 1.0 + epsilon, optimum.getPoint()[4], 1.0e-10);
        assertEquals( 1.0 - epsilon, optimum.getPoint()[5], 1.0e-10);

    }
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        }, 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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        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 =
            optimizer.optimize(problem1, GoalType.MINIMIZE, new double[] { 0, 1, 2, 3 });
        assertEquals(1.0, optimum1.getPoint()[0], 1.0e-5);
        assertEquals(1.0, optimum1.getPoint()[1], 1.0e-5);
        assertEquals(1.0, optimum1.getPoint()[2], 1.0e-5);
        assertEquals(1.0, optimum1.getPoint()[3], 1.0e-5);

        LinearProblem problem2 = new LinearProblem(new double[][] {
                { 10.00, 7.00, 8.10, 7.20 },
                {  7.08, 5.04, 6.00, 5.00 },
                {  8.00, 5.98, 9.89, 9.00 },
                {  6.99, 4.99, 9.00, 9.98 }
        }, new double[] { 32, 23, 33, 31 });
        RealPointValuePair optimum2 =
            optimizer.optimize(problem2, GoalType.MINIMIZE, new double[] { 0, 1, 2, 3 });
        assertEquals(-81.0, optimum2.getPoint()[0], 1.0e-1);
        assertEquals(137.0, optimum2.getPoint()[1], 1.0e-1);
        assertEquals(-34.0, optimum2.getPoint()[2], 1.0e-1);
        assertEquals( 22.0, optimum2.getPoint()[3], 1.0e-1);

    }
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        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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        }, 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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Related Classes of org.apache.commons.math.optimization.RealPointValuePair

  • com.opengamma.analytics.math.util.wrapper.CommonsMathWrapperTest
  • org.apache.commons.math.optimization.direct.DirectSearchOptimizer
  • org.apache.commons.math.optimization.direct.MultiDirectional
  • org.apache.commons.math.optimization.direct.MultiDirectionalTest
  • org.apache.commons.math.optimization.direct.NelderMead
  • org.apache.commons.math.optimization.direct.NelderMeadTest
  • org.apache.commons.math.optimization.direct.PowellOptimizer
  • org.apache.commons.math.optimization.direct.PowellOptimizerTest
  • org.apache.commons.math.optimization.general.NonLinearConjugateGradientOptimizer
  • org.apache.commons.math.optimization.general.NonLinearConjugateGradientOptimizerTest
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