Package org.apache.commons.math.optimization

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

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        constraints.add(equationFromString(objective.length, "x202 - x190 = 0"));
        constraints.add(equationFromString(objective.length, "x203 - x191 = 0"));
        constraints.add(equationFromString(objective.length, "x204 - x192 = 0"));

        SimplexSolver solver = new SimplexSolver();
        RealPointValuePair solution = solver.optimize(f, constraints, GoalType.MINIMIZE, true);
        assertEquals(7518.0, solution.getValue(), .0000001);
    }
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        constraints.add(new LinearConstraint(new double[] { 1, 1, 0 }, Relationship.GEQ,  1));
        constraints.add(new LinearConstraint(new double[] { 1, 0, 1 }, Relationship.GEQ,  1));
        constraints.add(new LinearConstraint(new double[] { 0, 1, 0 }, Relationship.GEQ,  1));

        SimplexSolver solver = new SimplexSolver();
        RealPointValuePair solution = solver.optimize(f, constraints, GoalType.MINIMIZE, true);
       
        assertEquals(0.0, solution.getPoint()[0], .0000001);
        assertEquals(1.0, solution.getPoint()[1], .0000001);
        assertEquals(1.0, solution.getPoint()[2], .0000001);
        assertEquals(3.0, solution.getValue(), .0000001);
      }
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        constraints.add(new LinearConstraint(new double[] { 1, 0 }, Relationship.LEQ, 2));
        constraints.add(new LinearConstraint(new double[] { 0, 1 }, Relationship.LEQ, 3));
        constraints.add(new LinearConstraint(new double[] { 1, 1 }, Relationship.EQ, 4));

        SimplexSolver solver = new SimplexSolver();
        RealPointValuePair solution = solver.optimize(f, constraints, GoalType.MAXIMIZE, false);
        assertEquals(2.0, solution.getPoint()[0], 0.0);
        assertEquals(2.0, solution.getPoint()[1], 0.0);
        assertEquals(57.0, solution.getValue(), 0.0);
    }
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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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            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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  • org.apache.commons.math.optimization.direct.MultiDirectional
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  • org.apache.commons.math.optimization.direct.NelderMead
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  • org.apache.commons.math.optimization.direct.PowellOptimizer
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  • org.apache.commons.math.optimization.general.NonLinearConjugateGradientOptimizerTest
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