Package org.apache.commons.math3.optimization

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

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                 { 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,
                                                    new SimpleValueChecker(1e-6, 1e-6));
        PointValuePair optimum =
            optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 2, 2, 2, 2, 2, 2 });
        Assert.assertEquals(0, optimum.getValue(), 1.0e-10);
    }
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        count = 0;
        SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
        optimizer.setSimplex(new MultiDirectionalSimplex(new double[][] {
                    { -1.2,  1.0 }, { 0.9, 1.2 } , {  3.5, -2.3 }
                }));
        PointValuePair optimum =
            optimizer.optimize(100, rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1 });

        Assert.assertEquals(count, optimizer.getEvaluations());
        Assert.assertTrue(optimizer.getEvaluations() > 50);
        Assert.assertTrue(optimizer.getEvaluations() < 100);
        Assert.assertTrue(optimum.getValue() > 1e-2);
    }
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            };

        count = 0;
        SimplexOptimizer optimizer = new SimplexOptimizer(-1, 1e-3);
        optimizer.setSimplex(new MultiDirectionalSimplex(4));
        PointValuePair optimum =
            optimizer.optimize(1000, powell, GoalType.MINIMIZE, new double[] { 3, -1, 0, 1 });
        Assert.assertEquals(count, optimizer.getEvaluations());
        Assert.assertTrue(optimizer.getEvaluations() > 800);
        Assert.assertTrue(optimizer.getEvaluations() < 900);
        Assert.assertTrue(optimum.getValue() > 1e-2);
    }
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        }, new double[] { 3.0, 1.0, 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[] { 1, 1 });
        Assert.assertEquals(2.0, optimum.getPoint()[0], 1.0e-8);
        Assert.assertEquals(1.0, optimum.getPoint()[1], 1.0e-8);

    }
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        // fails because MultiDirectional.iterateSimplex is looping forever
        // the while(true) should be replaced with a convergence check
        SimplexOptimizer optimizer = new SimplexOptimizer(1e-14, 1e-14);
        optimizer.setSimplex(new MultiDirectionalSimplex(2));
        final Gaussian2D function = new Gaussian2D(0, 0, 1);
        PointValuePair estimate = optimizer.optimize(1000, 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 double[] { 3.0, 1.0, 4.0 });

        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-6, 1e-6));
        PointValuePair optimum =
            optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 1, 1 });
        Assert.assertTrue(optimum.getValue() > 0.1);

    }
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        circle.addPoint( 45.0,  97.0);
        NonLinearConjugateGradientOptimizer optimizer =
            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE,
                                                    new SimpleValueChecker(1e-30, 1e-30),
                                                    new BrentSolver(1e-15, 1e-13));
        PointValuePair optimum =
            optimizer.optimize(100, circle, GoalType.MINIMIZE, new double[] { 98.680, 47.345 });
        Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]);
        Assert.assertEquals(69.960161753, circle.getRadius(center), 1.0e-8);
        Assert.assertEquals(96.075902096, center.getX(), 1.0e-8);
        Assert.assertEquals(48.135167894, center.getY(), 1.0e-8);
    }
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            wrapped.boundedToUnbounded(new double[] { 1.5, 2.75 }),
            wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
            wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
        }));

        final PointValuePair optimum
            = optimizer.optimize(300, wrapped, GoalType.MINIMIZE,
                                 wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 }));
        final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());

        Assert.assertEquals(biQuadratic.getBoundedXOptimum(), bounded[0], 2e-7);
        Assert.assertEquals(biQuadratic.getBoundedYOptimum(), bounded[1], 2e-7);

    }
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            wrapped.boundedToUnbounded(new double[] { 1.5, 2.75 }),
            wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
            wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
        }));

        final PointValuePair optimum
            = optimizer.optimize(100, wrapped, GoalType.MINIMIZE,
                                 wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 }));
        final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());

        Assert.assertEquals(biQuadratic.getBoundedXOptimum(), bounded[0], 2e-7);
        Assert.assertEquals(biQuadratic.getBoundedYOptimum(), bounded[1], 2e-7);

    }
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            wrapped.boundedToUnbounded(new double[] { 1.5, 2.75 }),
            wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
            wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
        }));

        final PointValuePair optimum
            = optimizer.optimize(300, wrapped, GoalType.MINIMIZE,
                                 wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 }));
        final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());

        Assert.assertEquals(biQuadratic.getBoundedXOptimum(), bounded[0], 2e-7);
        Assert.assertEquals(biQuadratic.getBoundedYOptimum(), bounded[1], 2e-7);

    }
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Related Classes of org.apache.commons.math3.optimization.PointValuePair

  • 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
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