Examples with MultivariateOptimizer org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer used on opensource projects

Example 6 with MultivariateOptimizer

use of org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer in project tetrad by cmu-phil.

the class MimbuildTrek method optimizeNonMeasureVariancesConditionally.

private void optimizeNonMeasureVariancesConditionally(Node[][] indicators, TetradMatrix measurescov, TetradMatrix latentscov, double[][] loadings, int[][] indicatorIndices, double[] delta) {
    int count = 0;
    for (int i = 0; i < indicators.length; i++) {
        for (int j = i; j < indicators.length; j++) {
            count++;
        }
    }
    for (int i = 0; i < indicators.length; i++) {
        for (int j = 0; j < indicators[i].length; j++) {
            count++;
        }
    }
    double[] values3 = new double[count];
    count = 0;
    for (int i = 0; i < indicators.length; i++) {
        for (int j = i; j < indicators.length; j++) {
            values3[count] = latentscov.get(i, j);
            count++;
        }
    }
    for (int i = 0; i < indicators.length; i++) {
        for (int j = 0; j < indicators[i].length; j++) {
            values3[count] = loadings[i][j];
            count++;
        }
    }
    Function2 function2 = new Function2(indicatorIndices, measurescov, loadings, latentscov, delta, count);
    MultivariateOptimizer search = new PowellOptimizer(1e-7, 1e-7);
    PointValuePair pair = search.optimize(new InitialGuess(values3), new ObjectiveFunction(function2), GoalType.MINIMIZE, new MaxEval(100000));
    minimum = pair.getValue();
}

Example 7 with MultivariateOptimizer

use of org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer in project tetrad by cmu-phil.

the class MimbuildTrek method optimizeMeasureVariancesConditionally.

private void optimizeMeasureVariancesConditionally(TetradMatrix measurescov, TetradMatrix latentscov, double[][] loadings, int[][] indicatorIndices, double[] delta) {
    double[] values2 = new double[delta.length];
    int count = 0;
    for (int i = 0; i < delta.length; i++) {
        values2[count++] = delta[i];
    }
    Function2 function2 = new Function2(indicatorIndices, measurescov, loadings, latentscov, delta, count);
    MultivariateOptimizer search = new PowellOptimizer(1e-7, 1e-7);
    PointValuePair pair = search.optimize(new InitialGuess(values2), new ObjectiveFunction(function2), GoalType.MINIMIZE, new MaxEval(100000));
    minimum = pair.getValue();
}

Example 8 with MultivariateOptimizer

use of org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer in project tetrad by cmu-phil.

the class MimbuildTrek method optimizeNonMeasureVariancesQuick.

private void optimizeNonMeasureVariancesQuick(Node[][] indicators, TetradMatrix measurescov, TetradMatrix latentscov, double[][] loadings, int[][] indicatorIndices) {
    int count = 0;
    for (int i = 0; i < indicators.length; i++) {
        for (int j = i; j < indicators.length; j++) {
            count++;
        }
    }
    for (int i = 0; i < indicators.length; i++) {
        for (int j = 0; j < indicators[i].length; j++) {
            count++;
        }
    }
    double[] values = new double[count];
    count = 0;
    for (int i = 0; i < indicators.length; i++) {
        for (int j = i; j < indicators.length; j++) {
            values[count++] = latentscov.get(i, j);
        }
    }
    for (int i = 0; i < indicators.length; i++) {
        for (int j = 0; j < indicators[i].length; j++) {
            values[count++] = loadings[i][j];
        }
    }
    Function1 function1 = new Function1(indicatorIndices, measurescov, loadings, latentscov, count);
    MultivariateOptimizer search = new PowellOptimizer(1e-7, 1e-7);
    PointValuePair pair = search.optimize(new InitialGuess(values), new ObjectiveFunction(function1), GoalType.MINIMIZE, new MaxEval(100000));
    minimum = pair.getValue();
}

Example 9 with MultivariateOptimizer

use of org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer in project GDSC-SMLM by aherbert.

the class PcPalmFitting method runBoundedOptimiser.

private PointValuePair runBoundedOptimiser(double[] initialSolution, double[] lowerB, double[] upperB, SumOfSquaresModelFunction function) {
    // Create the functions to optimise
    final ObjectiveFunction objective = new ObjectiveFunction(new SumOfSquaresMultivariateFunction(function));
    final ObjectiveFunctionGradient gradient = new ObjectiveFunctionGradient(new SumOfSquaresMultivariateVectorFunction(function));
    final boolean debug = false;
    // Try a gradient optimiser since this will produce a deterministic solution
    PointValuePair optimum = null;
    boundedEvaluations = 0;
    final MaxEval maxEvaluations = new MaxEval(2000);
    MultivariateOptimizer opt = null;
    for (int iteration = 0; iteration <= settings.fitRestarts; iteration++) {
        try {
            final double relativeThreshold = 1e-6;
            opt = new BoundedNonLinearConjugateGradientOptimizer(BoundedNonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES, new SimpleValueChecker(relativeThreshold, -1));
            optimum = opt.optimize(maxEvaluations, gradient, objective, GoalType.MINIMIZE, new InitialGuess((optimum == null) ? initialSolution : optimum.getPointRef()), new SimpleBounds(lowerB, upperB));
            if (debug) {
                System.out.printf("Bounded Iter %d = %g (%d)\n", iteration, optimum.getValue(), opt.getEvaluations());
            }
        } catch (final RuntimeException ex) {
            // No need to restart
            break;
        } finally {
            if (opt != null) {
                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
    final double stopFitness = 0;
    final boolean isActiveCma = true;
    final int diagonalOnly = 0;
    final int checkFeasableCount = 1;
    final RandomGenerator random = new RandomGeneratorAdapter(UniformRandomProviders.create());
    final boolean generateStatistics = false;
    final 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.
    final double[] range = new double[lowerB.length];
    for (int i = 0; i < lowerB.length; i++) {
        range[i] = (upperB[i] - lowerB[i]) / 3;
    }
    final OptimizationData sigma = new CMAESOptimizer.Sigma(range);
    final OptimizationData popSize = new CMAESOptimizer.PopulationSize((int) (4 + Math.floor(3 * Math.log(initialSolution.length))));
    final SimpleBounds bounds = new SimpleBounds(lowerB, upperB);
    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 <= settings.fitRestarts; iteration++) {
        try {
            // Start from the initial solution
            final 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 (final TooManyEvaluationsException | TooManyIterationsException ex) {
        // Ignore
        } finally {
            boundedEvaluations += maxEvaluations.getMaxEval();
        }
        if (optimum == null) {
            continue;
        }
        try {
            // Also restart from the current optimum
            final 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 (final TooManyEvaluationsException | TooManyIterationsException ex) {
        // Ignore
        } finally {
            boundedEvaluations += maxEvaluations.getMaxEval();
        }
    }
    return optimum;
}

Example 10 with MultivariateOptimizer

use of org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer 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;
}