Example usage for org.apache.commons.math.optimization.direct MultiDirectional MultiDirectional

Introduction

In this page you can find the example usage for org.apache.commons.math.optimization.direct MultiDirectional MultiDirectional.

Prototype

public MultiDirectional() 

Source Link

Document

Build a multi-directional optimizer with default coefficients.

Usage

From source file:com.opengamma.analytics.math.minimization.MultiDirectionalSimplexMinimizer.java

/**
 * {@inheritDoc}//from  w w w  . j a  va  2s.c om
 */
@Override
public DoubleMatrix1D minimize(final Function1D<DoubleMatrix1D, Double> f, final DoubleMatrix1D initialPoint) {
    Validate.notNull(f, "function");
    Validate.notNull(initialPoint, "initial point");
    final MultivariateRealOptimizer optimizer = new MultiDirectional();
    final MultivariateRealFunction commons = CommonsMathWrapper.wrapMultivariate(f);
    try {
        return new DoubleMatrix1D(
                CommonsMathWrapper.unwrap(optimizer.optimize(commons, MINIMIZER, initialPoint.getData())));
    } catch (final ConvergenceException e) {
        throw new MathException(e);
    } catch (final FunctionEvaluationException e) {
        throw new MathException(e);
    }
}

---

From source file:ch.algotrader.simulation.SimulationExecutorImpl.java

/**
 * {@inheritDoc}/*from  w w w .ja v a2  s. co m*/
 */
@Override
public void optimizeMultiParam(final StrategyGroup strategyGroup, final String[] parameters,
        final double[] starts) {

    Validate.notNull(parameters, "Parameter is null");
    Validate.notNull(starts, "Starts is null");

    RealPointValuePair result;
    try {
        MultivariateRealFunction function = new MultivariateFunction(this, strategyGroup, parameters);
        MultivariateRealOptimizer optimizer = new MultiDirectional();
        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(0.0, 0.01));
        result = optimizer.optimize(function, GoalType.MAXIMIZE, starts);
        if (RESULT_LOGGER.isInfoEnabled()) {
            for (int i = 0; i < result.getPoint().length; i++) {
                RESULT_LOGGER.info("optimal value for {}={}", parameters[i],
                        format.format(result.getPoint()[i]));
            }
            RESULT_LOGGER.info("functionValue: {} needed iterations: {})", format.format(result.getValue()),
                    optimizer.getEvaluations());
        }
    } catch (MathException ex) {
        throw new SimulationExecutorException(ex);
    }
}

---

From source file:uk.ac.diamond.scisoft.analysis.optimize.ApacheMultiDirectional.java

@Override
public void optimize(IDataset[] coords, IDataset data, final IFunction function) throws Exception {

    // Pull out the data which is required from the inputs
    final int numCoords = coords.length;
    final DoubleDataset[] newCoords = new DoubleDataset[numCoords];
    for (int i = 0; i < numCoords; i++) {
        newCoords[i] = (DoubleDataset) DatasetUtils.convertToAbstractDataset(coords[i])
                .cast(AbstractDataset.FLOAT64);
    }//w  w  w. j a  va2  s  . co m

    final DoubleDataset values = (DoubleDataset) DatasetUtils.convertToAbstractDataset(data)
            .cast(AbstractDataset.FLOAT64);

    // create an instance of the fitter
    MultiDirectional md = new MultiDirectional();

    // provide the fitting function which wrappers all the normal fitting functionality
    MultivariateRealFunction f1 = new MultivariateRealFunction() {

        @Override
        public double value(double[] arg0) throws FunctionEvaluationException, IllegalArgumentException {
            function.setParameterValues(arg0);
            return function.residual(true, values, newCoords);
        }
    };

    double[] start = function.getParameterValues();

    // preform the optimisation
    RealPointValuePair result = md.optimize(f1, GoalType.MINIMIZE, start);

    // set the input functions parameters to be the result before finishing.
    function.setParameterValues(result.getPoint());

}

---