Example usage for org.apache.commons.math3.optim InitialGuess InitialGuess

Introduction

In this page you can find the example usage for org.apache.commons.math3.optim InitialGuess InitialGuess.

Prototype

public InitialGuess(double[] startPoint) 

Source Link

Usage

From source file:de.tuberlin.uebb.jdae.solvers.OptimalitySolver.java

@Override
public double[] solve(int maxEval, MultivariateDifferentiableVectorFunction f, double[] startValue) {
    final double[] zeros = startValue.clone();
    final double[] ones = startValue.clone();
    Arrays.fill(zeros, 0.0);/*from   w  ww  .j av  a2 s  .c  o m*/
    Arrays.fill(ones, 1.0);

    return optim.optimize(new MaxEval(maxEval), new InitialGuess(startValue), new Target(zeros),
            new Weight(ones), new ModelFunction(f), new ModelFunctionJacobian(new JacobianFunction(f)))
            .getPoint();
}

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From source file:edu.ucsf.valelab.saim.calculations.SaimErrorFunctionFitter.java

public double[] fit(Collection<WeightedObservedPoint> observedPoints) {
    SaimErrorFunction ser = new SaimErrorFunction(data_, observedPoints);
    MultivariateOptimizer optimizer = new BOBYQAOptimizer(6, 10, 1.0E-8);
    double[] lb = { 0.0, 0.0, 0.0 };
    double[] ub = { 64000, 64000, 1000 };
    SimpleBounds sb = new SimpleBounds(lb, ub);
    PointValuePair results = optimizer.optimize(new MaxEval(20000), GoalType.MINIMIZE, new InitialGuess(guess_),
            new ObjectiveFunction(ser), sb);
    System.out.println("Value: " + results.getValue());
    return results.getPoint();

}

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From source file:edu.utexas.cs.tactex.tariffoptimization.OptimizerWrapperApachePowell.java

@Override
public TreeMap<Double, TariffSpecification> findOptimum(TariffUtilityEstimate tariffUtilityEstimate,
        int NUM_RATES, int numEval) {

    double[] startingVertex = new double[NUM_RATES]; // start from the fixed-rate tariff's offset
    Arrays.fill(startingVertex, 0.0);

    PowellOptimizer optimizer = new PowellOptimizer(1e-3, 5);

    // needed since one optimization found positive 
    // charges (paying customer to consume...)
    double[][] boundaries = createBoundaries(NUM_RATES);

    final PointValuePair optimum = optimizer.optimize(new MaxEval(numEval),
            //new ObjectiveFunction(new MultivariateFunctionMappingAdapter(tariffUtilityEstimate, boundaries[0], boundaries[1])),
            new ObjectiveFunction(new OptimizerWrapperApacheObjective(tariffUtilityEstimate)),
            GoalType.MAXIMIZE, new InitialGuess(startingVertex));

    TreeMap<Double, TariffSpecification> eval2TOUTariff = new TreeMap<Double, TariffSpecification>();
    eval2TOUTariff.put(optimum.getValue(), tariffUtilityEstimate.getCorrespondingSpec(optimum.getKey()));
    return eval2TOUTariff;
}

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From source file:eu.crisis_economics.abm.markets.clearing.heterogeneous.BoundedQuadraticEstimationClearingAlgorithm.java

@Override
public double applyToNetwork(final MixedClearingNetwork network) {
    Preconditions.checkNotNull(network);
    final int dimension = network.getNumberOfEdges();

    final ResidualCostFunction aggregateCostFunction = super.getResidualScalarCostFunction(network);
    final RealVector start = new ArrayRealVector(network.getNumberOfEdges());
    start.set(1.0); // Initial rate guess.

    final BOBYQAOptimizer optimizer = new BOBYQAOptimizer(2 * dimension + 1, 1.2, 1.e-8);
    final PointValuePair result = optimizer.optimize(new MaxEval(maximumEvaluations),
            new ObjectiveFunction(aggregateCostFunction), GoalType.MINIMIZE,
            new SimpleBounds(new double[dimension], ArrayUtil.ones(dimension)),
            new InitialGuess(start.toArray()));

    final double residualCost = result.getValue();
    System.out.println("Network cleared: residual cost: " + residualCost + ".");

    return residualCost;
}

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From source file:com.itemanalysis.psychometrics.factoranalysis.MINRESmethod.java

public double estimateParameters() {
    MINRESObjectiveFunction objectiveFunction = new MINRESObjectiveFunction();

    optimizer = new NonLinearConjugateGradientOptimizer(
            NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE, new SimpleValueChecker(1e-10, 1e-10),
            1e-4, 1e-4, 1);/*from  w  w w  .  j a  v  a  2 s  . c  o  m*/

    solution = optimizer.optimize(new MaxEval(1000), objectiveFunction.getObjectiveFunction(),
            objectiveFunction.getObjectiveFunctionGradient(), GoalType.MINIMIZE,
            new InitialGuess(getStartValues()));

    computeFactorLoadings(solution.getPoint());
    return solution.getValue();
}

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From source file:de.tuberlin.uebb.jdae.solvers.OptimalitySolver.java

public double[] solve(int maxEval, MultivariateVectorFunction residual, MultivariateMatrixFunction jacobian,
        double[] startValue) {
    final double[] zeros = startValue.clone();
    final double[] ones = startValue.clone();
    Arrays.fill(zeros, 0.0);//from www . j  a  v a 2 s  .com
    Arrays.fill(ones, 1.0);

    return optim.optimize(new MaxEval(maxEval), new InitialGuess(startValue), new Target(zeros),
            new Weight(ones), new ModelFunction(residual), new ModelFunctionJacobian(jacobian)).getPoint();
}

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From source file:com.itemanalysis.psychometrics.factoranalysis.WeightedLeastSquaresMethod.java

public double estimateParameters() {

    Sinv = new LUDecomposition(R).getSolver().getInverse();

    WLSObjectiveFunction objectiveFunction = new WLSObjectiveFunction();

    optimizer = new NonLinearConjugateGradientOptimizer(
            NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE, new SimpleValueChecker(1e-8, 1e-8));

    solution = optimizer.optimize(new MaxEval(1000), objectiveFunction.getObjectiveFunction(),
            objectiveFunction.getObjectiveFunctionGradient(), GoalType.MINIMIZE,
            new InitialGuess(getStartValues()));

    computeFactorLoadings(solution.getPoint());
    return solution.getValue();

}

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From source file:com.itemanalysis.psychometrics.factoranalysis.GeneralizedLeastSquaresMethod.java

public double estimateParameters() {

    Sinv = new LUDecomposition(R).getSolver().getInverse();

    GLSObjectiveFunction objectiveFunction = new GLSObjectiveFunction();

    optimizer = new NonLinearConjugateGradientOptimizer(
            NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE, new SimpleValueChecker(1e-8, 1e-8));

    solution = optimizer.optimize(new MaxEval(1000), objectiveFunction.getObjectiveFunction(),
            objectiveFunction.getObjectiveFunctionGradient(), GoalType.MINIMIZE,
            new InitialGuess(getStartValues()));

    computeFactorLoadings(solution.getPoint());
    return solution.getValue();

}

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From source file:edu.utexas.cs.tactex.tariffoptimization.OptimizerWrapperApacheAmoeba.java

@Override
public TreeMap<Double, TariffSpecification> findOptimum(TariffUtilityEstimate tariffUtilityEstimate,
        int NUM_RATES, int numEval) {
    double[] startingVertex = new double[NUM_RATES]; // start from the fixed-rate tariff's offset
    //Arrays.fill(startingVertex, -0.5 * SIMPLEX_EDGE);
    //Arrays.fill(startingVertex, -1 * SIMPLEX_EDGE);

    // TODO if there are convergence issues, change these guessed thresholds
    //SimplexOptimizer optimizer = new SimplexOptimizer(1e-10, 1e-30); 
    //SimplexOptimizer optimizer = new SimplexOptimizer(1e-4, 1e-4); 
    //SimplexOptimizer optimizer = new SimplexOptimizer(1e-2, 10); 
    SimplexOptimizer optimizer = new SimplexOptimizer(1e-3, 5);
    //SimplexOptimizer optimizer = new SimplexOptimizer(1e-2, 5); 

    final PointValuePair optimum = optimizer.optimize(new MaxEval(numEval),
            new ObjectiveFunction(new OptimizerWrapperApacheObjective(tariffUtilityEstimate)),
            GoalType.MAXIMIZE, new InitialGuess(startingVertex),
            //new NelderMeadSimplex(NUM_RATES, -1 * SIMPLEX_EDGE)); 
            new NelderMeadSimplex(NUM_RATES, SIMPLEX_EDGE));// should be positive since this reflects decrease in (negative) charges

    TreeMap<Double, TariffSpecification> eval2TOUTariff = new TreeMap<Double, TariffSpecification>();
    eval2TOUTariff.put(optimum.getValue(), tariffUtilityEstimate.getCorrespondingSpec(optimum.getKey()));
    return eval2TOUTariff;
}

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From source file:eu.crisis_economics.abm.markets.clearing.heterogeneous.NelderMeadClearingAlgorithm.java

@Override
public double applyToNetwork(final MixedClearingNetwork network) {
    Preconditions.checkNotNull(network);
    final SimplexOptimizer optimizer = new SimplexOptimizer(relErrorTarget, absErrorTarget);

    final ResidualCostFunction aggregateCostFunction = super.getResidualScalarCostFunction(network);
    final RealVector start = new ArrayRealVector(network.getNumberOfEdges());
    for (int i = 0; i < network.getNumberOfEdges(); ++i)
        start.setEntry(i, network.getEdges().get(i).getMaximumRateAdmissibleByBothParties());
    start.set(1.);//from ww w  .ja  v  a 2  s.  co m

    final PointValuePair result = optimizer.optimize(new MaxEval(maximumEvaluations),
            new ObjectiveFunction(aggregateCostFunction), GoalType.MINIMIZE, new InitialGuess(start.toArray()),
            new NelderMeadSimplex(network.getNumberOfEdges()));

    final double residualCost = result.getValue();
    System.out.println("Network cleared: residual cost: " + residualCost + ".");

    return residualCost;
}

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