Examples with MaxEval org.apache.commons.math3.optim.MaxEval used on opensource projects

Example 36 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project aic-praise by aic-sri-international.

the class RegularParameterEstimation method optimizeModel.

/**
 * Call the optimizer of Java Commons Math.
 */
public PointValuePair optimizeModel() {
    NonLinearConjugateGradientOptimizer optimizer = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE, new SimpleValueChecker(1e-13, 1e-13));
    final LoglikelihoodToOptimize f = new LoglikelihoodToOptimize(expressionBasedModel, queries);
    final GradientLoglikelihoodToOptimize gradientToOptimize = new GradientLoglikelihoodToOptimize(expressionBasedModel, queries);
    ObjectiveFunction objectiveFunction = new ObjectiveFunction(f);
    MultivariateVectorFunction gradientMultivariateFunction = gradientToOptimize;
    ObjectiveFunctionGradient objectiveFunctionGradient = new ObjectiveFunctionGradient(gradientMultivariateFunction);
    double[] startPoint = new double[] { 0.5, 0.5 };
    final PointValuePair optimum = optimizer.optimize(new MaxEval(10000), objectiveFunction, objectiveFunctionGradient, GoalType.MAXIMIZE, new InitialGuess(startPoint));
    return optimum;
}

Example 37 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project aic-praise by aic-sri-international.

the class RegularSymbolicParameterEstimation method optimize.

/**
 * Call the optimizer of Java Commons Math with the objectiveFunction and the gradient given in the arguments.
 */
public PointValuePair optimize(Expression expressionToOptimize, Vector<Expression> gradient) {
    NonLinearConjugateGradientOptimizer optimizer = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE, new SimpleValueChecker(1e-13, 1e-13));
    final FunctionToOptimize f = new FunctionToOptimize(expressionToOptimize);
    final GradientToOptimize gradientToOptimize = new GradientToOptimize(expressionToOptimize, gradient);
    ObjectiveFunction objectiveFunction = new ObjectiveFunction(f);
    MultivariateVectorFunction gradientMultivariateFunction = gradientToOptimize;
    ObjectiveFunctionGradient objectiveFunctionGradient = new ObjectiveFunctionGradient(gradientMultivariateFunction);
    double[] startPoint = new double[] { 0.5, 0.5 };
    final PointValuePair optimum = optimizer.optimize(new MaxEval(10000), objectiveFunction, objectiveFunctionGradient, GoalType.MAXIMIZE, new InitialGuess(startPoint));
    return optimum;
}

Example 38 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project GDSC-SMLM by aherbert.

the class PCPALMFitting method runBoundedOptimiser.

private PointValuePair runBoundedOptimiser(double[][] gr, double[] initialSolution, double[] lB, double[] uB, SumOfSquaresModelFunction function) {
    // Create the functions to optimise
    ObjectiveFunction objective = new ObjectiveFunction(new SumOfSquaresMultivariateFunction(function));
    ObjectiveFunctionGradient gradient = new ObjectiveFunctionGradient(new SumOfSquaresMultivariateVectorFunction(function));
    final boolean debug = false;
    // Try a BFGS optimiser since this will produce a deterministic solution and can respect bounds.
    PointValuePair optimum = null;
    boundedEvaluations = 0;
    final MaxEval maxEvaluations = new MaxEval(2000);
    MultivariateOptimizer opt = null;
    for (int iteration = 0; iteration <= fitRestarts; iteration++) {
        try {
            opt = new BFGSOptimizer();
            final double relativeThreshold = 1e-6;
            // Configure maximum step length for each dimension using the bounds
            double[] stepLength = new double[lB.length];
            for (int i = 0; i < stepLength.length; i++) stepLength[i] = (uB[i] - lB[i]) * 0.3333333;
            // The GoalType is always minimise so no need to pass this in
            optimum = opt.optimize(maxEvaluations, gradient, objective, new InitialGuess((optimum == null) ? initialSolution : optimum.getPointRef()), new SimpleBounds(lB, uB), new BFGSOptimizer.GradientTolerance(relativeThreshold), new BFGSOptimizer.StepLength(stepLength));
            if (debug)
                System.out.printf("BFGS Iter %d = %g (%d)\n", iteration, optimum.getValue(), opt.getEvaluations());
        } catch (TooManyEvaluationsException e) {
            // No need to restart
            break;
        } catch (RuntimeException e) {
            // No need to restart
            break;
        } finally {
            boundedEvaluations += opt.getEvaluations();
        }
    }
    // Try a CMAES optimiser which is non-deterministic. To overcome this we perform restarts.
    // CMAESOptimiser based on Matlab code:
    // https://www.lri.fr/~hansen/cmaes.m
    // Take the defaults from the Matlab documentation
    //Double.NEGATIVE_INFINITY;
    double stopFitness = 0;
    boolean isActiveCMA = true;
    int diagonalOnly = 0;
    int checkFeasableCount = 1;
    //Well19937c();
    RandomGenerator random = new Well44497b();
    boolean generateStatistics = false;
    ConvergenceChecker<PointValuePair> checker = new SimpleValueChecker(1e-6, 1e-10);
    // The sigma determines the search range for the variables. It should be 1/3 of the initial search region.
    double[] range = new double[lB.length];
    for (int i = 0; i < lB.length; i++) range[i] = (uB[i] - lB[i]) / 3;
    OptimizationData sigma = new CMAESOptimizer.Sigma(range);
    OptimizationData popSize = new CMAESOptimizer.PopulationSize((int) (4 + Math.floor(3 * Math.log(initialSolution.length))));
    SimpleBounds bounds = new SimpleBounds(lB, uB);
    opt = new CMAESOptimizer(maxEvaluations.getMaxEval(), stopFitness, isActiveCMA, diagonalOnly, checkFeasableCount, random, generateStatistics, checker);
    // Restart the optimiser several times and take the best answer.
    for (int iteration = 0; iteration <= fitRestarts; iteration++) {
        try {
            // Start from the initial solution
            PointValuePair constrainedSolution = opt.optimize(new InitialGuess(initialSolution), objective, GoalType.MINIMIZE, bounds, sigma, popSize, maxEvaluations);
            if (debug)
                System.out.printf("CMAES Iter %d initial = %g (%d)\n", iteration, constrainedSolution.getValue(), opt.getEvaluations());
            boundedEvaluations += opt.getEvaluations();
            if (optimum == null || constrainedSolution.getValue() < optimum.getValue()) {
                optimum = constrainedSolution;
            }
        } catch (TooManyEvaluationsException e) {
        } catch (TooManyIterationsException e) {
        } finally {
            boundedEvaluations += maxEvaluations.getMaxEval();
        }
        if (optimum == null)
            continue;
        try {
            // Also restart from the current optimum
            PointValuePair constrainedSolution = opt.optimize(new InitialGuess(optimum.getPointRef()), objective, GoalType.MINIMIZE, bounds, sigma, popSize, maxEvaluations);
            if (debug)
                System.out.printf("CMAES Iter %d restart = %g (%d)\n", iteration, constrainedSolution.getValue(), opt.getEvaluations());
            if (constrainedSolution.getValue() < optimum.getValue()) {
                optimum = constrainedSolution;
            }
        } catch (TooManyEvaluationsException e) {
        } catch (TooManyIterationsException e) {
        } finally {
            boundedEvaluations += maxEvaluations.getMaxEval();
        }
    }
    return optimum;
}

Example 39 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project GDSC-SMLM by aherbert.

the class PCPALMMolecules method optimiseSimplex.

private double[] optimiseSimplex(float[] x, float[] y, double[] initialSolution) {
    // Simplex optimisation
    SkewNormalMultivariateFunction sn2 = new SkewNormalMultivariateFunction(initialSolution);
    sn2.addData(x, y);
    NelderMeadSimplex simplex = new NelderMeadSimplex(4);
    SimplexOptimizer opt = new SimplexOptimizer(1e-6, 1e-10);
    PointValuePair solution = opt.optimize(new MaxEval(1000), new InitialGuess(initialSolution), simplex, new ObjectiveFunction(sn2), GoalType.MINIMIZE);
    double[] skewParameters2 = solution.getPointRef();
    return skewParameters2;
}

Example 40 with MaxEval

use of org.apache.commons.math3.optim.MaxEval in project gatk by broadinstitute.

the class OptimizationUtils method argmax.

public static double argmax(final Function<Double, Double> function, final double min, final double max, final double guess, final double relativeTolerance, final double absoluteTolerance, final int maxEvaluations) {
    final BrentOptimizer optimizer = new BrentOptimizer(relativeTolerance, absoluteTolerance);
    final SearchInterval interval = new SearchInterval(min, max, guess);
    return optimizer.optimize(new UnivariateObjectiveFunction(function::apply), GoalType.MAXIMIZE, interval, new MaxEval(maxEvaluations)).getPoint();
}