Package org.apache.commons.math.optimization

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

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    GoalType goal = minimize.getValue() ?
            GoalType.MINIMIZE : GoalType.MAXIMIZE;
    IValueFactory vf = values;
    boolean nonNegativeJ =  nonNegative.getValue();
    try {
      RealPointValuePair res =
          solver.optimize(fJ, constraintsJ, goal,nonNegativeJ);
      return vf.constructor(Maybe.Maybe_just,
          vf.constructor(
              LLSolution_llSolution, convertToRealList(res.getPoint(), vf),
              vf.real(res.getValue()) )
          );
    } catch (Exception e) {
      return  vf.constructor(Maybe.Maybe_nothing);
    }
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      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
      optimum = optimizer.optimize(fourExtrema, GoalType.MINIMIZE, new double[] { -3.0, 0 });
      assertEquals(xM,        optimum.getPoint()[0], 2.0e-7);
      assertEquals(yP,        optimum.getPoint()[1], 2.0e-5);
      assertEquals(valueXmYp, optimum.getValue(),    6.0e-12);
      assertTrue(optimizer.getEvaluations() > 60);
      assertTrue(optimizer.getEvaluations() < 90);

      optimum = optimizer.optimize(fourExtrema, GoalType.MINIMIZE, new double[] { +1, 0 });
      assertEquals(xP,        optimum.getPoint()[0], 5.0e-6);
      assertEquals(yM,        optimum.getPoint()[1], 6.0e-6);
      assertEquals(valueXpYm, optimum.getValue(),    1.0e-11);
      assertTrue(optimizer.getEvaluations() > 60);
      assertTrue(optimizer.getEvaluations() < 90);

      // maximization
      optimum = optimizer.optimize(fourExtrema, GoalType.MAXIMIZE, new double[] { -3.0, 0.0 });
      assertEquals(xM,        optimum.getPoint()[0], 1.0e-5);
      assertEquals(yM,        optimum.getPoint()[1], 3.0e-6);
      assertEquals(valueXmYm, optimum.getValue(),    3.0e-12);
      assertTrue(optimizer.getEvaluations() > 60);
      assertTrue(optimizer.getEvaluations() < 90);

      optimum = optimizer.optimize(fourExtrema, GoalType.MAXIMIZE, new double[] { +1, 0 });
      assertEquals(xP,        optimum.getPoint()[0], 4.0e-6);
      assertEquals(yP,        optimum.getPoint()[1], 5.0e-6);
      assertEquals(valueXpYp, optimum.getValue(),    7.0e-12);
      assertTrue(optimizer.getEvaluations() > 60);
      assertTrue(optimizer.getEvaluations() < 90);

  }
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    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 =
        optimizer.optimize(rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1.0 });

    assertEquals(rosenbrock.getCount(), optimizer.getEvaluations());
    assertTrue(optimizer.getEvaluations() > 40);
    assertTrue(optimizer.getEvaluations() < 50);
    assertTrue(optimum.getValue() < 8.0e-4);

  }
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    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);
    assertTrue(optimizer.getEvaluations() < 130);
    assertTrue(optimum.getValue() < 2.0e-3);

  }
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          }
      }, 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);
      assertTrue(optimizer.getEvaluations() > 60);
      assertTrue(optimizer.getEvaluations() < 80);
      assertTrue(optimum.getValue() < 1.0e-6);
  }
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          }
      }, 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);
      assertTrue(optimizer.getEvaluations() > 60);
      assertTrue(optimizer.getEvaluations() < 80);
      assertTrue(optimum.getValue() < 1.0e-6);
  }
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          { 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);
      assertTrue(optimizer.getEvaluations() > 60);
      assertTrue(optimizer.getEvaluations() < 80);
      assertTrue(optimum.getValue() < 1.0e-6);
  }
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      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);
      Assert.assertTrue(optimizer.getEvaluations() < 220);

  }
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    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 =
        optimizer.optimize(rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1.0 });

    Assert.assertEquals(count, optimizer.getEvaluations());
    Assert.assertTrue(optimizer.getEvaluations() > 50);
    Assert.assertTrue(optimizer.getEvaluations() < 100);
    Assert.assertTrue(optimum.getValue() > 1.0e-2);

  }
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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);
    Assert.assertTrue(optimizer.getEvaluations() < 900);
    Assert.assertTrue(optimum.getValue() > 1.0e-2);

  }
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  • 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
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