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 : (FastMath.atan(x) * FastMath.atan(x + 2) * FastMath.atan(y) * FastMath.atan(y) / (x * y));
          }
      };


      MultiDirectional optimizer = new MultiDirectional();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-11, 1.0e-30));
      optimizer.setMaxIterations(200);
      optimizer.setStartConfiguration(new double[] { 0.2, 0.2 });
      RealPointValuePair optimum;


      // minimization
      optimum = optimizer.optimize(fourExtrema, GoalType.MINIMIZE, new double[] { -3.0, 0 });
      Assert.assertEquals(xM,        optimum.getPoint()[0], 4.0e-6);
      Assert.assertEquals(yP,        optimum.getPoint()[1], 3.0e-6);
      Assert.assertEquals(valueXmYp, optimum.getValue(),    8.0e-13);
      Assert.assertTrue(optimizer.getEvaluations() > 120);
      Assert.assertTrue(optimizer.getEvaluations() < 150);


      optimum = optimizer.optimize(fourExtrema, GoalType.MINIMIZE, new double[] { +1, 0 });
      Assert.assertEquals(xP,        optimum.getPoint()[0], 2.0e-8);
      Assert.assertEquals(yM,        optimum.getPoint()[1], 3.0e-6);
      Assert.assertEquals(valueXpYm, optimum.getValue(),    2.0e-12);
      Assert.assertTrue(optimizer.getEvaluations() > 120);
      Assert.assertTrue(optimizer.getEvaluations() < 150);


      // maximization
      optimum = optimizer.optimize(fourExtrema, GoalType.MAXIMIZE, new double[] { -3.0, 0.0 });
      Assert.assertEquals(xM,        optimum.getPoint()[0], 7.0e-7);
      Assert.assertEquals(yM,        optimum.getPoint()[1], 3.0e-7);
      Assert.assertEquals(valueXmYm, optimum.getValue(),    2.0e-14);
      Assert.assertTrue(optimizer.getEvaluations() > 120);
      Assert.assertTrue(optimizer.getEvaluations() < 150);


      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-15, 1.0e-30));
      optimum = optimizer.optimize(fourExtrema, GoalType.MAXIMIZE, new double[] { +1, 0 });
      Assert.assertEquals(xP,        optimum.getPoint()[0], 2.0e-8);
      Assert.assertEquals(yP,        optimum.getPoint()[1], 3.0e-6);
      Assert.assertEquals(valueXpYp, optimum.getValue(),    2.0e-12);
      Assert.assertTrue(optimizer.getEvaluations() > 180);

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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 =

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        }
      };


    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 });
    Assert.assertEquals(count, optimizer.getEvaluations());
    Assert.assertTrue(optimizer.getEvaluations() > 800);

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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));
          }
      };


      NelderMead optimizer = new NelderMead();
      optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-10, 1.0e-30));
      optimizer.setMaxIterations(100);
      optimizer.setStartConfiguration(new double[] { 0.2, 0.2 });
      RealPointValuePair optimum;


      // minimization

View Full Code Here

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

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