Example usage for org.apache.commons.math3.optim.nonlinear.scalar.gradient NonLinearConjugateGradientOptimizer NonLinearConjugateGradientOptimizer

Introduction

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

Prototype

public NonLinearConjugateGradientOptimizer(final Formula updateFormula,
        ConvergenceChecker<PointValuePair> checker) 

Source Link

Document

Constructor with default BrentSolver line search solver and IdentityPreconditioner preconditioner .

Usage

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: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:net.sf.tweety.math.opt.solver.ApacheCommonsNonLinearConjugateGradientOptimizer.java

@Override
public Map<Variable, Term> solve(ConstraintSatisfactionProblem problem) throws GeneralMathException {
    // only optimization problems
    if (!(problem instanceof OptimizationProblem))
        throw new IllegalArgumentException("Only optimization problems allowed for this solver.");
    OptimizationProblem p = (OptimizationProblem) problem;
    // no constraints allowed
    if (!p.isEmpty())
        throw new IllegalArgumentException(
                "Only optimization problems without constraints allowed for this solver.");
    final Term target = p.getTargetFunction();
    final List<Variable> vars = new ArrayList<Variable>(target.getVariables());
    MultivariateFunction acTarget = new MultivariateFunction() {
        @Override/*from  w  w  w . j  a  v a  2  s  . c  o  m*/
        public double value(double[] arg0) {
            return target.replaceAllTerms(arg0, vars).doubleValue();
        }
    };
    final Term[] targetGradient = new Term[vars.size()];
    for (int i = 0; i < vars.size(); i++)
        targetGradient[i] = target.derive(vars.get(i));
    MultivariateVectorFunction acTargetGradient = new MultivariateVectorFunction() {
        @Override
        public double[] value(double[] arg0) throws IllegalArgumentException {
            double[] result = new double[arg0.length];
            for (int i = 0; i < arg0.length; i++)
                result[i] = targetGradient[i].replaceAllTerms(arg0, vars).doubleValue();
            return result;
        }
    };
    // create solver
    NonLinearConjugateGradientOptimizer optimizer = new NonLinearConjugateGradientOptimizer(
            NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
            new SimplePointChecker<PointValuePair>(this.precision, this.precision));
    double[] s = new double[vars.size()];
    for (int i = 0; i < vars.size(); i++)
        s[i] = 0.5;
    PointValuePair val = optimizer.optimize(new ObjectiveFunction(acTarget),
            new ObjectiveFunctionGradient(acTargetGradient), new InitialGuess(s),
            p.getType() == OptimizationProblem.MAXIMIZE ? GoalType.MAXIMIZE : GoalType.MINIMIZE,
            new MaxEval(this.maxEval));
    Map<Variable, Term> result = new HashMap<Variable, Term>();
    for (int i = 0; i < vars.size(); i++)
        result.put(vars.get(i), new FloatConstant(val.getPoint()[i]));
    return result;
}

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

private MultivariateOptimizer createOptimizer() {
    SimplePointChecker<PointValuePair> checker = new SimplePointChecker<PointValuePair>(REL_TOL, ABS_TOL);
    switch (optimizer) {
    case CONJUGATE_GRADIENT:
        return new NonLinearConjugateGradientOptimizer(Formula.POLAK_RIBIERE, checker);
    case BOBYQA://  w  w  w. ja  v a  2  s . c o m
        return new BOBYQAOptimizer(n + 2);
    case CMAES:
        return new CMAESOptimizer(MAX_ITER, 0., true, 0, 10,
                seed == null ? new Well19937c() : new Well19937c(seed), false,
                new SimplePointChecker<PointValuePair>(REL_TOL, ABS_TOL));
    case POWELL:
        return new PowellOptimizer(REL_TOL, ABS_TOL, checker);
    case SIMPLEX_MD:
    case SIMPLEX_NM:
        return new SimplexOptimizer(checker);
    default:
        throw new IllegalStateException("Should not be called");
    }
}

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