Commons Math example source code file (GaussNewtonOptimizer.java)

This example Commons Math source code file (GaussNewtonOptimizer.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" ^TM.

Java - Commons Math tags/keywords

abstractleastsquaresoptimizer, blockrealmatrix, decompositionsolver, decompositionsolver, functionevaluationexception, gaussnewtonoptimizer, gaussnewtonoptimizer, illegalargumentexception, optimizationexception, optimizationexception, override, realmatrix, vectorialpointvaluepair, vectorialpointvaluepair

The Commons Math GaussNewtonOptimizer.java source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.commons.math.optimization.general;

import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.linear.BlockRealMatrix;
import org.apache.commons.math.linear.DecompositionSolver;
import org.apache.commons.math.linear.InvalidMatrixException;
import org.apache.commons.math.linear.LUDecompositionImpl;
import org.apache.commons.math.linear.QRDecompositionImpl;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.optimization.OptimizationException;
import org.apache.commons.math.optimization.VectorialPointValuePair;

/**
 * Gauss-Newton least-squares solver.
 * <p>
 * This class solve a least-square problem by solving the normal equations
 * of the linearized problem at each iteration. Either LU decomposition or
 * QR decomposition can be used to solve the normal equations. LU decomposition
 * is faster but QR decomposition is more robust for difficult problems.
 * </p>
 *
 * @version $Revision: 811685 $ $Date: 2009-09-05 13:36:48 -0400 (Sat, 05 Sep 2009) $
 * @since 2.0
 *
 */

public class GaussNewtonOptimizer extends AbstractLeastSquaresOptimizer {

    /** Indicator for using LU decomposition. */
    private final boolean useLU;

    /** Simple constructor with default settings.
     * <p>The convergence check is set to a {@link
     * org.apache.commons.math.optimization.SimpleVectorialValueChecker}
     * and the maximal number of evaluation is set to
     * {@link AbstractLeastSquaresOptimizer#DEFAULT_MAX_ITERATIONS}.
     * @param useLU if true, the normal equations will be solved using LU
     * decomposition, otherwise they will be solved using QR decomposition
     */
    public GaussNewtonOptimizer(final boolean useLU) {
        this.useLU = useLU;
    }

    /** {@inheritDoc} */
    @Override
    public VectorialPointValuePair doOptimize()
        throws FunctionEvaluationException, OptimizationException, IllegalArgumentException {

        // iterate until convergence is reached
        VectorialPointValuePair current = null;
        for (boolean converged = false; !converged;) {

            incrementIterationsCounter();

            // evaluate the objective function and its jacobian
            VectorialPointValuePair previous = current;
            updateResidualsAndCost();
            updateJacobian();
            current = new VectorialPointValuePair(point, objective);

            // build the linear problem
            final double[]   b = new double[cols];
            final double[][] a = new double[cols][cols];
            for (int i = 0; i < rows; ++i) {

                final double[] grad   = jacobian[i];
                final double weight   = residualsWeights[i];
                final double residual = objective[i] - targetValues[i];

                // compute the normal equation
                final double wr = weight * residual;
                for (int j = 0; j < cols; ++j) {
                    b[j] += wr * grad[j];
                }

                // build the contribution matrix for measurement i
                for (int k = 0; k < cols; ++k) {
                    double[] ak = a[k];
                    double wgk = weight * grad[k];
                    for (int l = 0; l < cols; ++l) {
                        ak[l] += wgk * grad[l];
                    }
                }

            }

            try {

                // solve the linearized least squares problem
                RealMatrix mA = new BlockRealMatrix(a);
                DecompositionSolver solver = useLU ?
                        new LUDecompositionImpl(mA).getSolver() :
                        new QRDecompositionImpl(mA).getSolver();
                final double[] dX = solver.solve(b);

                // update the estimated parameters
                for (int i = 0; i < cols; ++i) {
                    point[i] += dX[i];
                }

            } catch(InvalidMatrixException e) {
                throw new OptimizationException("unable to solve: singular problem");
            }

            // check convergence
            if (previous != null) {
                converged = checker.converged(getIterations(), previous, current);
            }

        }

        // we have converged
        return current;

    }

}