Examples with GaussNewtonOptimizer org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer used on opensource projects

Example 1 with GaussNewtonOptimizer

use of org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer in project Orekit by CS-SI.

the class OrbitDetermination method createEstimator.

/**
 * Set up estimator.
 * @param parser input file parser
 * @param propagatorBuilder propagator builder
 * @return estimator
 * @throws NoSuchElementException if input parameters are missing
 * @throws OrekitException if some propagator parameters cannot be retrieved
 */
private BatchLSEstimator createEstimator(final KeyValueFileParser<ParameterKey> parser, final NumericalPropagatorBuilder propagatorBuilder) throws NoSuchElementException, OrekitException {
    final boolean optimizerIsLevenbergMarquardt;
    if (!parser.containsKey(ParameterKey.ESTIMATOR_OPTIMIZATION_ENGINE)) {
        optimizerIsLevenbergMarquardt = true;
    } else {
        final String engine = parser.getString(ParameterKey.ESTIMATOR_OPTIMIZATION_ENGINE);
        optimizerIsLevenbergMarquardt = engine.toLowerCase().contains("levenberg");
    }
    final LeastSquaresOptimizer optimizer;
    if (optimizerIsLevenbergMarquardt) {
        // we want to use a Levenberg-Marquardt optimization engine
        final double initialStepBoundFactor;
        if (!parser.containsKey(ParameterKey.ESTIMATOR_LEVENBERG_MARQUARDT_INITIAL_STEP_BOUND_FACTOR)) {
            initialStepBoundFactor = 100.0;
        } else {
            initialStepBoundFactor = parser.getDouble(ParameterKey.ESTIMATOR_LEVENBERG_MARQUARDT_INITIAL_STEP_BOUND_FACTOR);
        }
        optimizer = new LevenbergMarquardtOptimizer().withInitialStepBoundFactor(initialStepBoundFactor);
    } else {
        // we want to use a Gauss-Newton optimization engine
        optimizer = new GaussNewtonOptimizer(Decomposition.QR);
    }
    final double convergenceThreshold;
    if (!parser.containsKey(ParameterKey.ESTIMATOR_NORMALIZED_PARAMETERS_CONVERGENCE_THRESHOLD)) {
        convergenceThreshold = 1.0e-3;
    } else {
        convergenceThreshold = parser.getDouble(ParameterKey.ESTIMATOR_NORMALIZED_PARAMETERS_CONVERGENCE_THRESHOLD);
    }
    final int maxIterations;
    if (!parser.containsKey(ParameterKey.ESTIMATOR_MAX_ITERATIONS)) {
        maxIterations = 10;
    } else {
        maxIterations = parser.getInt(ParameterKey.ESTIMATOR_MAX_ITERATIONS);
    }
    final int maxEvaluations;
    if (!parser.containsKey(ParameterKey.ESTIMATOR_MAX_EVALUATIONS)) {
        maxEvaluations = 20;
    } else {
        maxEvaluations = parser.getInt(ParameterKey.ESTIMATOR_MAX_EVALUATIONS);
    }
    final BatchLSEstimator estimator = new BatchLSEstimator(optimizer, propagatorBuilder);
    estimator.setParametersConvergenceThreshold(convergenceThreshold);
    estimator.setMaxIterations(maxIterations);
    estimator.setMaxEvaluations(maxEvaluations);
    return estimator;
}

Example 2 with GaussNewtonOptimizer

use of org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer in project Orekit by CS-SI.

the class OrbitDeterminationTest method createEstimator.

/**
 * Set up estimator.
 * @param parser input file parser
 * @param propagatorBuilder propagator builder
 * @return estimator
 * @throws NoSuchElementException if input parameters are missing
 * @throws OrekitException if some propagator parameters cannot be retrieved
 */
private BatchLSEstimator createEstimator(final KeyValueFileParser<ParameterKey> parser, final NumericalPropagatorBuilder propagatorBuilder) throws NoSuchElementException, OrekitException {
    final boolean optimizerIsLevenbergMarquardt;
    if (!parser.containsKey(ParameterKey.ESTIMATOR_OPTIMIZATION_ENGINE)) {
        optimizerIsLevenbergMarquardt = true;
    } else {
        final String engine = parser.getString(ParameterKey.ESTIMATOR_OPTIMIZATION_ENGINE);
        optimizerIsLevenbergMarquardt = engine.toLowerCase().contains("levenberg");
    }
    final LeastSquaresOptimizer optimizer;
    if (optimizerIsLevenbergMarquardt) {
        // we want to use a Levenberg-Marquardt optimization engine
        final double initialStepBoundFactor;
        if (!parser.containsKey(ParameterKey.ESTIMATOR_LEVENBERG_MARQUARDT_INITIAL_STEP_BOUND_FACTOR)) {
            initialStepBoundFactor = 100.0;
        } else {
            initialStepBoundFactor = parser.getDouble(ParameterKey.ESTIMATOR_LEVENBERG_MARQUARDT_INITIAL_STEP_BOUND_FACTOR);
        }
        optimizer = new LevenbergMarquardtOptimizer().withInitialStepBoundFactor(initialStepBoundFactor);
    } else {
        // we want to use a Gauss-Newton optimization engine
        optimizer = new GaussNewtonOptimizer(Decomposition.QR);
    }
    final double convergenceThreshold;
    if (!parser.containsKey(ParameterKey.ESTIMATOR_NORMALIZED_PARAMETERS_CONVERGENCE_THRESHOLD)) {
        convergenceThreshold = 1.0e-3;
    } else {
        convergenceThreshold = parser.getDouble(ParameterKey.ESTIMATOR_NORMALIZED_PARAMETERS_CONVERGENCE_THRESHOLD);
    }
    final int maxIterations;
    if (!parser.containsKey(ParameterKey.ESTIMATOR_MAX_ITERATIONS)) {
        maxIterations = 10;
    } else {
        maxIterations = parser.getInt(ParameterKey.ESTIMATOR_MAX_ITERATIONS);
    }
    final int maxEvaluations;
    if (!parser.containsKey(ParameterKey.ESTIMATOR_MAX_EVALUATIONS)) {
        maxEvaluations = 20;
    } else {
        maxEvaluations = parser.getInt(ParameterKey.ESTIMATOR_MAX_EVALUATIONS);
    }
    final BatchLSEstimator estimator = new BatchLSEstimator(optimizer, propagatorBuilder);
    estimator.setParametersConvergenceThreshold(convergenceThreshold);
    estimator.setMaxIterations(maxIterations);
    estimator.setMaxEvaluations(maxEvaluations);
    return estimator;
}