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

Example 6 with SimpleBounds

use of org.apache.commons.math3.optim.SimpleBounds in project narchy by automenta.

the class MyCMAESOptimizerTest method testFitAccuracyDependsOnBoundary.

/**
 * Cf. MATH-867
 */
@Test
public void testFitAccuracyDependsOnBoundary() {
    double[] sigma1 = { 1e-1 };
    MyCMAESOptimizer optimizer = new MyCMAESOptimizer(30000, 0, true, 10, 0, rng(), false, null, 5, sigma1);
    final MultivariateFunction fitnessFunction = new MultivariateFunction() {

        @Override
        public double value(double[] parameters) {
            final double target = 11.1;
            final double error = target - parameters[0];
            return error * error;
        }
    };
    final double[] start = { 1 };
    // No bounds.
    PointValuePair result = optimizer.optimize(new MaxEval(100000), new ObjectiveFunction(fitnessFunction), GoalType.MINIMIZE, SimpleBounds.unbounded(1), new InitialGuess(start));
    final double resNoBound = result.getPoint()[0];
    // Optimum is near the lower bound.
    final double[] lower = { -20 };
    final double[] upper = { 5e16 };
    final double[] sigma2 = { 10 };
    optimizer = new MyCMAESOptimizer(30000, 0, true, 10, 0, rng(), false, null, 5, sigma2);
    result = optimizer.optimize(new MaxEval(100000), new ObjectiveFunction(fitnessFunction), GoalType.MINIMIZE, new InitialGuess(start), new SimpleBounds(lower, upper));
    final double resNearLo = result.getPoint()[0];
    // Optimum is near the upper bound.
    lower[0] = -5e16;
    upper[0] = 20;
    result = optimizer.optimize(new MaxEval(100000), new ObjectiveFunction(fitnessFunction), GoalType.MINIMIZE, new InitialGuess(start), new SimpleBounds(lower, upper));
    final double resNearHi = result.getPoint()[0];
    // System.out.println("resNoBound=" + resNoBound +
    // " resNearLo=" + resNearLo +
    // " resNearHi=" + resNearHi);
    // The two values currently differ by a substantial amount, indicating that
    // the bounds definition can prevent reaching the optimum.
    assertEquals(resNoBound, resNearLo, 1e-3);
    assertEquals(resNoBound, resNearHi, 1e-3);
}

Example 7 with SimpleBounds

use of org.apache.commons.math3.optim.SimpleBounds in project narchy by automenta.

the class MyCMAESOptimizerTest method doTest.

/**
 * @param func Function to optimize.
 * @param startPoint Starting point.
 * @param inSigma Individual input sigma.
 * @param boundaries Upper / lower point limit.
 * @param goal Minimization or maximization.
 * @param lambda Population size used for offspring.
 * @param isActive Covariance update mechanism.
 * @param diagonalOnly Simplified covariance update.
 * @param stopValue Termination criteria for optimization.
 * @param fTol Tolerance relative error on the objective function.
 * @param pointTol Tolerance for checking that the optimum is correct.
 * @param maxEvaluations Maximum number of evaluations.
 * @param expected Expected point / value.
 */
private void doTest(MultivariateFunction func, double[] startPoint, double[] inSigma, double[][] boundaries, GoalType goal, int lambda, boolean isActive, int diagonalOnly, double stopValue, double fTol, double pointTol, int maxEvaluations, PointValuePair expected) {
    int dim = startPoint.length;
    // test diagonalOnly = 0 - slow but normally fewer feval#
    MyCMAESOptimizer optim = new MyCMAESOptimizer(30000, stopValue, isActive, diagonalOnly, 0, rng(), false, null, lambda, inSigma);
    PointValuePair result = boundaries == null ? optim.optimize(new MaxEval(maxEvaluations), new ObjectiveFunction(func), goal, new InitialGuess(startPoint), SimpleBounds.unbounded(dim)) : optim.optimize(new MaxEval(maxEvaluations), new ObjectiveFunction(func), goal, new SimpleBounds(boundaries[0], boundaries[1]), new InitialGuess(startPoint));
    // System.out.println("sol=" + Arrays.toString(result.getPoint()));
    assertEquals(expected.getValue(), result.getValue(), fTol);
    for (int i = 0; i < dim; i++) {
        assertEquals(expected.getPoint()[i], result.getPoint()[i], pointTol);
    }
    assertTrue(optim.getIterations() > 0);
}

Example 8 with SimpleBounds

use of org.apache.commons.math3.optim.SimpleBounds in project narchy by automenta.

the class MyCMAESOptimizerTest method testMath864.

@Test
public void testMath864() {
    final double[] sigma = { 1e-1 };
    final MyCMAESOptimizer optimizer = new MyCMAESOptimizer(30000, 0, true, 10, 0, rng(), false, null, 5, sigma);
    final MultivariateFunction fitnessFunction = new MultivariateFunction() {

        @Override
        public double value(double[] parameters) {
            final double target = 1;
            final double error = target - parameters[0];
            return error * error;
        }
    };
    final double[] start = { 0 };
    final double[] lower = { -1e6 };
    final double[] upper = { 1.5 };
    final double[] result = optimizer.optimize(new MaxEval(10000), new ObjectiveFunction(fitnessFunction), GoalType.MINIMIZE, new InitialGuess(start), new SimpleBounds(lower, upper)).getPoint();
    assertTrue(result[0] <= upper[0], () -> "Out of bounds (" + result[0] + " > " + upper[0] + ")");
}

Example 9 with SimpleBounds

use of org.apache.commons.math3.optim.SimpleBounds in project aic-expresso by aic-sri-international.

the class OptimizationWithBOBYQA method optimize.

/**
 * Main method. It computes the optimum of the function and returns a PointValuePair which contains the argopt and the optimum.
 * Both can be computed directly with findOptimum and findArgopt.
 */
private PointValuePair optimize(BOBYQAOptimizer optimizer) {
    final FunctionToOptimize f = new FunctionToOptimize(this.expressionToOptimize);
    int dimension = numberOfVariablesInExpression();
    double[] ones = new double[dimension];
    for (int i = 0; i < dimension; i++) {
        ones[i] = 1;
    }
    final PointValuePair optimum = optimizer.optimize(this.maxEval, new ObjectiveFunction(f), this.goalType, new SimpleBounds(new double[dimension], ones), this.initialGuess);
    return optimum;
}

Example 10 with SimpleBounds

use of org.apache.commons.math3.optim.SimpleBounds 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;
}