Package org.apache.commons.math3.optim.nonlinear.scalar

Examples of org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction

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        // Very small absolute tolerance to rely solely on the relative
        // tolerance as a stopping criterion
        final PowellOptimizer optim = new PowellOptimizer(relTol, 1e-100);

        final PointValuePair funcResult = optim.optimize(new MaxEval(maxEval),
                                                         new ObjectiveFunction(func),
                                                         GoalType.MINIMIZE,
                                                         new InitialGuess(init));
        final double funcValue = func.value(funcResult.getPoint());
        final int funcEvaluations = optim.getEvaluations();

        final double scale = 1e10;
        final MultivariateFunction funcScaled = new MultivariateFunction() {
                public double value(double[] x) {
                    return scale * func.value(x);
                }
            };

        final PointValuePair funcScaledResult = optim.optimize(new MaxEval(maxEval),
                                                               new ObjectiveFunction(funcScaled),
                                                               GoalType.MINIMIZE,
                                                               new InitialGuess(init));
        final double funcScaledValue = funcScaled.value(funcScaledResult.getPoint());
        final int funcScaledEvaluations = optim.getEvaluations();

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                        double fTol,
                        double pointTol) {
        final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d));

        final PointValuePair result = optim.optimize(new MaxEval(1000),
                                                     new ObjectiveFunction(func),
                                                     goal,
                                                     new InitialGuess(init));
        final double[] point = result.getPoint();

        for (int i = 0, dim = optimum.length; i < dim; i++) {
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                        double pointTol) {
        final PowellOptimizer optim = new PowellOptimizer(fTol, Math.ulp(1d),
                                                          fLineTol, Math.ulp(1d));

        final PointValuePair result = optim.optimize(new MaxEval(1000),
                                                     new ObjectiveFunction(func),
                                                     goal,
                                                     new InitialGuess(init));
        final double[] point = result.getPoint();

        for (int i = 0, dim = optimum.length; i < dim; i++) {
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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),
                                                   new SimpleBounds(lower, upper)).getPoint();
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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),
                                    new SimpleBounds(lower, upper));
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        // 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),
                           new CMAESOptimizer.Sigma(inSigma),
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     * {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}).
     * @throws NotStrictlyPositiveException if {@code mean <= 0}.
     * @since 2.1
     */
    public ExponentialDistribution(double mean, double inverseCumAccuracy) {
        this(new Well19937c(), mean, inverseCumAccuracy);
    }
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        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }

        covarianceImpl =
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        geoMeanImpl = new StorelessUnivariateStatistic[k];
        meanImpl    = new StorelessUnivariateStatistic[k];

        for (int i = 0; i < k; ++i) {
            sumImpl[i]     = new Sum();
            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
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     * @param checker Convergence checker.
     */
    protected BaseOptimizer(ConvergenceChecker<PAIR> checker) {
        this.checker = checker;

        evaluations = new Incrementor(0, new MaxEvalCallback());
        iterations = new Incrementor(0, new MaxIterCallback());
    }
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Related Classes of org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction

  • 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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