Package org.apache.commons.math3.optim

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

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            // Default convergence check.
            boolean stop = 2 * (fX - fVal) <=
                (relativeThreshold * (FastMath.abs(fX) + FastMath.abs(fVal)) +
                 absoluteThreshold);

            final PointValuePair previous = new PointValuePair(x1, fX);
            final PointValuePair current = new PointValuePair(x, fVal);
            if (!stop && checker != null) { // User-defined stopping criteria.
                stop = checker.converged(getIterations(), previous, current);
            }
            if (stop) {
                if (goal == GoalType.MINIMIZE) {
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        }, new double[] { 1, 1, 1 });
        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-6, 1e-6),
                                                      1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { 0, 0, 0 }));
        Assert.assertTrue(optimum.getValue() > 0.5);
    }
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        }, new double[] { 32, 23, 33, 31 });
        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-13, 1e-13),
                                                      1e-15, 1e-15, 1);
        PointValuePair optimum1
            = optimizer.optimize(new MaxEval(200),
                                 problem1.getObjectiveFunction(),
                                 problem1.getObjectiveFunctionGradient(),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { 0, 1, 2, 3 }));
        Assert.assertEquals(1.0, optimum1.getPoint()[0], 1.0e-4);
        Assert.assertEquals(1.0, optimum1.getPoint()[1], 1.0e-3);
        Assert.assertEquals(1.0, optimum1.getPoint()[2], 1.0e-4);
        Assert.assertEquals(1.0, optimum1.getPoint()[3], 1.0e-4);

        LinearProblem problem2 = new LinearProblem(new double[][] {
                { 10.00, 7.00, 8.10, 7.20 },
                {  7.08, 5.04, 6.00, 5.00 },
                {  8.00, 5.98, 9.89, 9.00 },
                {  6.99, 4.99, 9.00, 9.98 }
        }, new double[] { 32, 23, 33, 31 });
        PointValuePair optimum2
            = optimizer.optimize(new MaxEval(200),
                                 problem2.getObjectiveFunction(),
                                 problem2.getObjectiveFunctionGradient(),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { 0, 1, 2, 3 }));

        final double[] result2 = optimum2.getPoint();
        final double[] expected2 = {-81, 137, -34, 22};

        Assert.assertEquals(expected2[0], result2[0], 2);
        Assert.assertEquals(expected2[1], result2[1], 4);
        Assert.assertEquals(expected2[2], result2[2], 1);
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      constraints.add(new LinearConstraint(new double[] {  9, 8, 0 }, Relationship.EQ,  17));
      constraints.add(new LinearConstraint(new double[] {  0, 7, 8 }, Relationship.LEQ,  7));
      constraints.add(new LinearConstraint(new double[] { 10, 0, 2 }, Relationship.LEQ, 10));

      SimplexSolver solver = new SimplexSolver();
      PointValuePair solution = solver.optimize(DEFAULT_MAX_ITER, f, new LinearConstraintSet(constraints),
                                                GoalType.MAXIMIZE, new NonNegativeConstraint(false));
      Assert.assertEquals(1.0, solution.getPoint()[0], 0.0);
      Assert.assertEquals(1.0, solution.getPoint()[1], 0.0);
      Assert.assertEquals(0.0, solution.getPoint()[2], 0.0);
      Assert.assertEquals(15.0, solution.getValue(), 0.0);
  }
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        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-6, 1e-6),
                                                      1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { 7, 6, 5, 4 }));
        Assert.assertEquals(0, optimum.getValue(), 1.0e-10);

    }
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        }, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
        NonLinearConjugateGradientOptimizer optimizer
           = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                     new SimpleValueChecker(1e-6, 1e-6),
                                                     1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { 2, 2, 2, 2, 2, 2 }));
        Assert.assertEquals(0, optimum.getValue(), 1.0e-10);
    }
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        LinearObjectiveFunction f = new LinearObjectiveFunction(new double[] { 1, 1 }, 0);
        Collection<LinearConstraint> constraints = new ArrayList<LinearConstraint>();
        constraints.add(new LinearConstraint(new double[] { 1, 1 }, Relationship.EQ,  0));

        SimplexSolver solver = new SimplexSolver();
        PointValuePair solution = solver.optimize(DEFAULT_MAX_ITER, f, new LinearConstraintSet(constraints),
                                                  GoalType.MAXIMIZE, new NonNegativeConstraint(true));
        Assert.assertEquals(0, solution.getValue(), .0000001);
    }
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        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-6, 1e-6),
                                                      1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(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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        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();
        PointValuePair solution = solver.optimize(DEFAULT_MAX_ITER, f, new LinearConstraintSet(constraints),
                                                  GoalType.MINIMIZE, new NonNegativeConstraint(true));
        Assert.assertEquals(7518.0, solution.getValue(), .0000001);
    }
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        NonLinearConjugateGradientOptimizer optimizer
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-6, 1e-6),
                                                      1e-3, 1e-3, 1);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 problem.getObjectiveFunction(),
                                 problem.getObjectiveFunctionGradient(),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { 1, 1 }));
        Assert.assertTrue(optimum.getValue() > 0.1);

    }
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Related Classes of org.apache.commons.math3.optim.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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