Example usage for org.apache.commons.math3.optim.nonlinear.scalar GoalType MINIMIZE

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In this page you can find the example usage for org.apache.commons.math3.optim.nonlinear.scalar GoalType MINIMIZE.

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GoalType MINIMIZE

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Minimization.

Usage

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: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  ava2  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: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: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   w  w w  . j  a  va  2s .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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From source file:com.itemanalysis.psychometrics.factoranalysis.MaximumLikelihoodMethod.java

public double estimateParameters() {

    MLObjectiveFunction objectiveFunction = new MLObjectiveFunction();

    //        System.out.println("START VALUES: " + Arrays.toString(getStartValues()));

    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.uchc.octane.GaussianFitAstigmatism.java

@Override
public double[] doFit() {
    double[] initParameters;

    if (bPreprocessBg_) {
        initParameters = new double[] { 0, 0, pixelValue(0, 0) - bg_, sigma2_, sigma2_ };
    } else {/* w w w  .ja v a 2  s.  c  o  m*/
        initParameters = new double[] { 0, 0, pixelValue(0, 0) - bg_, sigma2_, sigma2_, bg_ };
    }

    PowellOptimizer optimizer = new PowellOptimizer(1e-4, 1e-1);

    MultivariateFunction func = new MultivariateFunction() {
        @Override
        public double value(double[] point) {

            double bg = bPreprocessBg_ ? 0 : point[3];

            double v = 0;

            for (int xi = -windowSize_; xi < windowSize_; xi++) {
                for (int yi = -windowSize_; yi < windowSize_; yi++) {
                    double delta = getValueExcludingBackground(xi, yi, point) + bg - pixelValue(xi, yi);
                    v += delta * delta;
                }
            }
            return v;
        }
    };

    pvp_ = optimizer.optimize(new ObjectiveFunction(func), new InitialGuess(initParameters), new MaxEval(10000),
            GoalType.MINIMIZE);

    //      double sigmax = pvp.getPoint()[3];
    //      double sigmay = pvp.getPoint()[4];
    //      if (calibration_ != null) {
    //         double [] pn = new double[4];
    //         pn[0] = p1_;
    //         pn[1] = p2_;
    //         pn[2] = p3_ + 2 * calibration_[2] * (calibration_[0] - sigmax) + 2 * calibration_[5] * (calibration_[3] - sigmay);
    //         pn[3] = calibration_[1] * (calibration_[0] - sigmax) + calibration_[4] * (calibration_[3] - sigmay);
    //         
    //         Complex [] roots = new LaguerreSolver().solveAllComplex(pn, 0);
    //         
    //         int minIndex = -1;
    //         double minV = Double.MAX_VALUE;
    //         for (int i = 0; i < 3; i ++) {
    //            double v;
    //            if (FastMath.abs(roots[i].getImaginary()) < 1e-7) {
    //               double z = roots[i].getReal(); 
    //               double vx = calibration_[0] + calibration_[1] * z + calibration_[2] * z * z - sigmax;
    //               double vy = calibration_[3] + calibration_[4] * z + calibration_[5] * z * z - sigmay;
    //               v = vx * vx + vy * vy;
    //               if (v < minV) {
    //                  minV = v;
    //                  minIndex = i;
    //               }
    //            }
    //         }
    //         
    //         if (minV > errTol_) {
    //            return null;
    //         }
    //
    //         z_ = roots[minIndex].getReal();
    //      }

    if (calibration_ != null) {
        calculateZ();
    }

    return pvp_.getPoint();
}

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From source file:edu.uchc.octane.GaussianFit2D.java

@Override
public double[] doFit() {

    double[] initParameters;

    if (floatingSigma_) {
        if (bPreprocessBg_) {
            initParameters = new double[] { 0, 0, pixelValue(0, 0) - bg_, sigma2_ };
        } else {/*from ww  w. j  a  va2  s. co m*/
            initParameters = new double[] { 0, 0, pixelValue(0, 0) - bg_, bg_, sigma2_ };
        }
    } else {
        if (bPreprocessBg_) {
            initParameters = new double[] { 0, 0, pixelValue(0, 0) - bg_ };
        } else {
            initParameters = new double[] { 0, 0, pixelValue(0, 0) - bg_, bg_ };
        }
    }

    PowellOptimizer optimizer = new PowellOptimizer(1e-4, 1e-1);

    MultivariateFunction func = new MultivariateFunction() {
        @Override
        public double value(double[] point) {

            //initParameters = point;
            double bg = bPreprocessBg_ ? 0 : point[3];

            double v = 0;

            for (int xi = -windowSize_; xi < windowSize_; xi++) {
                for (int yi = -windowSize_; yi < windowSize_; yi++) {
                    double delta = getValueExcludingBackground(xi, yi, point) + bg - pixelValue(xi, yi);
                    v += delta * delta;
                }
            }
            return v;
        }
    };

    pvp_ = optimizer.optimize(new ObjectiveFunction(func), new InitialGuess(initParameters), new MaxEval(10000),
            GoalType.MINIMIZE);

    return pvp_.getPoint();
}

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From source file:edu.cmu.tetrad.sem.SemOptimizerPowell.java

public void optimize(SemIm semIm) {
    double min = Double.POSITIVE_INFINITY;
    double[] point = null;

    for (int count = 0; count < numRestarts + 1; count++) {
        System.out.println("Trial " + (count + 1));
        SemIm _sem2 = new SemIm(semIm);

        List<Parameter> freeParameters = _sem2.getFreeParameters();

        double[] p = new double[freeParameters.size()];

        for (int i = 0; i < freeParameters.size(); i++) {
            if (freeParameters.get(i).getType() == ParamType.VAR) {
                p[i] = RandomUtil.getInstance().nextUniform(0, 1);
            } else {
                p[i] = RandomUtil.getInstance().nextUniform(-1, 1);
            }//from  ww  w .  j  a  v a  2 s.co m
        }

        _sem2.setFreeParamValues(p);

        MultivariateOptimizer search = new PowellOptimizer(1e-7, 1e-7);
        PointValuePair pair = search.optimize(new InitialGuess(_sem2.getFreeParamValues()),
                new ObjectiveFunction(fittingFunction(semIm)), GoalType.MINIMIZE, new MaxEval(100000));

        double chisq = _sem2.getChiSquare();
        System.out.println("chisq = " + chisq);

        if (chisq < min) {
            min = chisq;
            point = pair.getPoint();
        }
    }

    System.arraycopy(point, 0, semIm.getFreeParamValues(), 0, point.length);
}

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