Examples of org.apache.commons.math3.optimization.general.NonLinearConjugateGradientOptimizer.optimize()

org.apache.commons.math3.optimization.general.NonLinearConjugateGradientOptimizer.optimize()
{@inheritDoc} @param optData Optimization data. The following data will be looked for:
- {@link org.apache.commons.math3.optim.MaxEval}
- {@link org.apache.commons.math3.optim.InitialGuess}
- {@link org.apache.commons.math3.optim.SimpleBounds}
- {@link org.apache.commons.math3.optim.nonlinear.vector.Target}
- {@link org.apache.commons.math3.optim.nonlinear.vector.Weight}
- {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunction}
- {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunctionJacobian}
@return {@inheritDoc} @throws TooManyEvaluationsException if the maximal number ofevaluations is exceeded. @throws DimensionMismatchException if the initial guess, target, and weightarguments have inconsistent dimensions.

                wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
                wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
            });


        final PointValuePair optimum
            = optimizer.optimize(new MaxEval(100),
                                 new ObjectiveFunction(wrapped),
                                 simplex,
                                 GoalType.MINIMIZE,
                                 new InitialGuess(wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 })));
        final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());

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                wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
                wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
            });


        final PointValuePair optimum
            = optimizer.optimize(new MaxEval(300),
                                 new ObjectiveFunction(wrapped),
                                 simplex,
                                 GoalType.MINIMIZE,
                                 new InitialGuess(wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 })));
        final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());

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                wrapped.boundedToUnbounded(new double[] { 1.5, 2.95 }),
                wrapped.boundedToUnbounded(new double[] { 1.7, 2.90 })
            });


        final PointValuePair optimum
            = optimizer.optimize(new MaxEval(200),
                                 new ObjectiveFunction(wrapped),
                                 simplex,
                                 GoalType.MINIMIZE,
                                 new InitialGuess(wrapped.boundedToUnbounded(new double[] { 1.5, 2.25 })));
        final double[] bounded = wrapped.unboundedToBounded(optimum.getPoint());

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        final int maxEval = 1000;
        // Very small absolute tolerance to rely solely on the relative
        // tolerance as a stopping criterion
        final MultivariateOptimizer optim = new PowellOptimizer(relTol, 1e-100);


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


        final double scale = 1e10;
        final MultivariateFunction funcScaled = new MultivariateFunction() {

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                public double value(double[] x) {
                    return scale * func.value(x);
                }
            };


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


        // Check that both minima provide the same objective funciton values,
        // within the relative function tolerance.

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


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


        for (int i = 0, dim = optimum.length; i < dim; i++) {
            Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
                                optimum[i], point[i], pointTol);

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


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


        for (int i = 0, dim = optimum.length; i < dim; i++) {
            Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
                                optimum[i], point[i], pointTol);

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        final int maxEval = 1000;
        // Very small absolute tolerance to rely solely on the relative
        // tolerance as a stopping criterion
        final MultivariateOptimizer optim = new PowellOptimizer(relTol, 1e-100);


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


        final double scale = 1e10;
        final MultivariateFunction funcScaled = new MultivariateFunction() {

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                public double value(double[] x) {
                    return scale * func.value(x);
                }
            };


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


        // Check that both minima provide the same objective funciton values,
        // within the relative function tolerance.

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


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


        for (int i = 0, dim = optimum.length; i < dim; i++) {
            Assert.assertEquals("found[" + i + "]=" + point[i] + " value=" + result.getValue(),
                                optimum[i], point[i], pointTol);

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