Package org.apache.commons.math3.optim

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

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        // Input to the optimizer: the model and its Jacobian.
        final TheoreticalValuesFunction model = new TheoreticalValuesFunction(f);

        // Perform the fit.
        final PointVectorValuePair optimum
            = optimizer.optimize(new MaxEval(maxEval),
                                 model.getModelFunction(),
                                 model.getModelFunctionJacobian(),
                                 new Target(target),
                                 new Weight(weights),
                                 new InitialGuess(initialGuess));
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        final double[] start = { 0 };
        final double[] lower = { -1e6 };
        final double[] upper = { 1.5 };
        final double[] sigma = { 1e-1 };
        final double[] result = optimizer.optimize(new MaxEval(10000),
                                                   new ObjectiveFunction(fitnessFunction),
                                                   GoalType.MINIMIZE,
                                                   new CMAESOptimizer.PopulationSize(5),
                                                   new CMAESOptimizer.Sigma(sigma),
                                                   new InitialGuess(start),
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            };

        final double[] start = { 1 };
        // No bounds.
        PointValuePair result = optimizer.optimize(new MaxEval(100000),
                                                   new ObjectiveFunction(fitnessFunction),
                                                   GoalType.MINIMIZE,
                                                   SimpleBounds.unbounded(1),
                                                   new CMAESOptimizer.PopulationSize(5),
                                                   new CMAESOptimizer.Sigma(new double[] { 1e-1 }),
                                                   new InitialGuess(start));
        final double resNoBound = result.getPoint()[0];

        // Optimum is near the lower bound.
        final double[] lower = { -20 };
        final double[] upper = { 5e16 };
        final double[] sigma = { 10 };
        result = optimizer.optimize(new MaxEval(100000),
                                    new ObjectiveFunction(fitnessFunction),
                                    GoalType.MINIMIZE,
                                    new CMAESOptimizer.PopulationSize(5),
                                    new CMAESOptimizer.Sigma(sigma),
                                    new InitialGuess(start),
                                    new SimpleBounds(lower, upper));
        final double resNearLo = result.getPoint()[0];

        // Optimum is near the upper bound.
        lower[0] = -5e16;
        upper[0] = 20;
        result = optimizer.optimize(new MaxEval(100000),
                                    new ObjectiveFunction(fitnessFunction),
                                    GoalType.MINIMIZE,
                                    new CMAESOptimizer.PopulationSize(5),
                                    new CMAESOptimizer.Sigma(sigma),
                                    new InitialGuess(start),
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        int dim = startPoint.length;
        // test diagonalOnly = 0 - slow but normally fewer feval#
        CMAESOptimizer optim = new CMAESOptimizer(30000, stopValue, isActive, diagonalOnly,
                                                  0, new MersenneTwister(), false, null);
        PointValuePair result = boundaries == null ?
            optim.optimize(new MaxEval(maxEvaluations),
                           new ObjectiveFunction(func),
                           goal,
                           new InitialGuess(startPoint),
                           SimpleBounds.unbounded(dim),
                           new CMAESOptimizer.Sigma(inSigma),
                           new CMAESOptimizer.PopulationSize(lambda)) :
            optim.optimize(new MaxEval(maxEvaluations),
                           new ObjectiveFunction(func),
                           goal,
                           new SimpleBounds(boundaries[0],
                                            boundaries[1]),
                           new InitialGuess(startPoint),
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        int dim = startPoint.length;
        final int numIterpolationPoints = 2 * dim + 1 + additionalInterpolationPoints;
        BOBYQAOptimizer optim = new BOBYQAOptimizer(numIterpolationPoints);
        PointValuePair result = boundaries == null ?
            optim.optimize(new MaxEval(maxEvaluations),
                           new ObjectiveFunction(func),
                           goal,
                           SimpleBounds.unbounded(dim),
                           new InitialGuess(startPoint)) :
            optim.optimize(new MaxEval(maxEvaluations),
                           new ObjectiveFunction(func),
                           goal,
                           new InitialGuess(startPoint),
                           new SimpleBounds(boundaries[0],
                                            boundaries[1]));
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        final GoalType goal = mainOptimizer.getGoalType();
        bracket.search(f, goal, 0, initialBracketingRange);
        // Passing "MAX_VALUE" as a dummy value because it is the enclosing
        // class that counts the number of evaluations (and will eventually
        // generate the exception).
        return lineOptimizer.optimize(new MaxEval(Integer.MAX_VALUE),
                                      new UnivariateObjectiveFunction(f),
                                      goal,
                                      new SearchInterval(bracket.getLo(),
                                                         bracket.getHi(),
                                                         bracket.getMid()));
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    public abstract AbstractLeastSquaresOptimizer createOptimizer();

    @Test
    public void testGetIterations() {
        AbstractLeastSquaresOptimizer optim = createOptimizer();
        optim.optimize(new MaxEval(100), new Target(new double[] { 1 }),
                       new Weight(new double[] { 1 }),
                       new InitialGuess(new double[] { 3 }),
                       new ModelFunction(new MultivariateVectorFunction() {
                               public double[] value(double[] point) {
                                   return new double[] {
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    public void testTrivial() {
        LinearProblem problem
            = new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1 }),
                               new InitialGuess(new double[] { 0 }));
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            = new LinearProblem(new double[][] { { 1, -1 }, { 0, 2 }, { 1, -2 } },
                                new double[] { 4, 6, 1 });

        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 0 }));
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                { 0, 0, 0, 0, 2, 0 },
                { 0, 0, 0, 0, 0, 2 }
        }, new double[] { 0, 1.1, 2.2, 3.3, 4.4, 5.5 });
        AbstractLeastSquaresOptimizer optimizer = createOptimizer();
        PointVectorValuePair optimum =
            optimizer.optimize(new MaxEval(100),
                               problem.getModelFunction(),
                               problem.getModelFunctionJacobian(),
                               problem.getTarget(),
                               new Weight(new double[] { 1, 1, 1, 1, 1, 1 }),
                               new InitialGuess(new double[] { 0, 0, 0, 0, 0, 0 }));
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Related Classes of org.apache.commons.math3.optim.MaxEval

  • 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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