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

  public void testRosenbrock()
    throws FunctionEvaluationException, ConvergenceException {


    Rosenbrock rosenbrock = new Rosenbrock();
    NelderMead optimizer = new NelderMead();
    optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
    optimizer.setMaxIterations(100);
    optimizer.setStartConfiguration(new double[][] {
            { -1.2,  1.0 }, { 0.9, 1.2 } , {  3.5, -2.3 }
    });
    RealPointValuePair optimum =

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  public void testPowell()
    throws FunctionEvaluationException, ConvergenceException {


    Powell powell = new Powell();
    NelderMead optimizer = new NelderMead();
    optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-3));
    optimizer.setMaxIterations(200);
    RealPointValuePair optimum =
      optimizer.optimize(powell, GoalType.MINIMIZE, new double[] { 3.0, -1.0, 0.0, 1.0 });
    assertEquals(powell.getCount(), optimizer.getEvaluations());
    assertTrue(optimizer.getEvaluations() > 110);

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          public double[] value(double[] variables) {
              return factors.operate(variables);
          }
      }, new double[] { 2.0, -3.0 });
      NelderMead optimizer = new NelderMead();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
      optimizer.setMaxIterations(200);
      RealPointValuePair optimum =
          optimizer.optimize(ls, GoalType.MINIMIZE, new double[] { 10.0, 10.0 });
      assertEquals( 2.0, optimum.getPointRef()[0], 3.0e-5);
      assertEquals(-3.0, optimum.getPointRef()[1], 4.0e-4);

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          public double[] value(double[] variables) {
              return factors.operate(variables);
          }
      }, new double[] { 2.0, -3.0 }, new double[] { 10.0, 0.1 });
      NelderMead optimizer = new NelderMead();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
      optimizer.setMaxIterations(200);
      RealPointValuePair optimum =
          optimizer.optimize(ls, GoalType.MINIMIZE, new double[] { 10.0, 10.0 });
      assertEquals( 2.0, optimum.getPointRef()[0], 5.0e-5);
      assertEquals(-3.0, optimum.getPointRef()[1], 8.0e-4);

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          }
      }, new double[] { 2.0, -3.0 }, new Array2DRowRealMatrix(new double [][] {
          { 1.0, 1.2 }, { 1.2, 2.0 }
      }));
      NelderMead optimizer = new NelderMead();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-6));
      optimizer.setMaxIterations(200);
      RealPointValuePair optimum =
          optimizer.optimize(ls, GoalType.MINIMIZE, new double[] { 10.0, 10.0 });
      assertEquals( 2.0, optimum.getPointRef()[0], 2.0e-3);
      assertEquals(-3.0, optimum.getPointRef()[1], 8.0e-4);

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  @Test(expected = MaxIterationsExceededException.class)
  public void testMaxIterations() throws MathException {
      try {
          Powell powell = new Powell();
          NelderMead optimizer = new NelderMead();
          optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-3));
          optimizer.setMaxIterations(20);
          optimizer.optimize(powell, GoalType.MINIMIZE, new double[] { 3.0, -1.0, 0.0, 1.0 });
      } catch (OptimizationException oe) {
          if (oe.getCause() instanceof ConvergenceException) {
              throw (ConvergenceException) oe.getCause();

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              return ((x == 0) || (y == 0)) ? 0 : (Math.atan(x) * Math.atan(x + 2) * Math.atan(y) * Math.atan(y) / (x * y));
          }
      };


      MultiDirectional optimizer = new MultiDirectional();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-10, 1.0e-30));
      optimizer.setMaxIterations(200);
      optimizer.setStartConfiguration(new double[] { 0.2, 0.2 });
      RealPointValuePair optimum;


      // minimization

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        }
      };


    count = 0;
    MultiDirectional optimizer = new MultiDirectional();
    optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
    optimizer.setMaxIterations(100);
    optimizer.setStartConfiguration(new double[][] {
            { -1.2,  1.0 }, { 0.9, 1.2 } , {  3.5, -2.3 }
    });
    RealPointValuePair optimum =

View Full Code Here

        }
      };


    count = 0;
    MultiDirectional optimizer = new MultiDirectional();
    optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1.0, 1.0e-3));
    optimizer.setMaxIterations(1000);
    RealPointValuePair optimum =
      optimizer.optimize(powell, GoalType.MINIMIZE, new double[] { 3.0, -1.0, 0.0, 1.0 });
    assertEquals(count, optimizer.getEvaluations());
    assertTrue(optimizer.getEvaluations() > 800);

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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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Related Classes of org.apache.commons.math.optimization.SimpleScalarValueChecker

org.apache.commons.math.optimization.direct.DirectSearchOptimizer

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

org.apache.commons.math.optimization.direct.NelderMeadTest

org.apache.commons.math.optimization.direct.PowellOptimizerTest

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

org.apache.commons.math.optimization.general.NonLinearConjugateGradientOptimizerTest

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