| /* |
| * 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.math3.optimization.general; |
| |
| import org.apache.commons.math3.exception.ConvergenceException; |
| import org.apache.commons.math3.exception.NullArgumentException; |
| import org.apache.commons.math3.exception.MathInternalError; |
| import org.apache.commons.math3.exception.util.LocalizedFormats; |
| import org.apache.commons.math3.linear.ArrayRealVector; |
| import org.apache.commons.math3.linear.BlockRealMatrix; |
| import org.apache.commons.math3.linear.DecompositionSolver; |
| import org.apache.commons.math3.linear.LUDecomposition; |
| import org.apache.commons.math3.linear.QRDecomposition; |
| import org.apache.commons.math3.linear.RealMatrix; |
| import org.apache.commons.math3.linear.SingularMatrixException; |
| import org.apache.commons.math3.optimization.ConvergenceChecker; |
| import org.apache.commons.math3.optimization.SimpleVectorValueChecker; |
| import org.apache.commons.math3.optimization.PointVectorValuePair; |
| |
| /** |
| * 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> |
| * |
| * @deprecated As of 3.1 (to be removed in 4.0). |
| * @since 2.0 |
| * |
| */ |
| @Deprecated |
| public class GaussNewtonOptimizer extends AbstractLeastSquaresOptimizer { |
| /** Indicator for using LU decomposition. */ |
| private final boolean useLU; |
| |
| /** |
| * Simple constructor with default settings. |
| * The normal equations will be solved using LU decomposition and the |
| * convergence check is set to a {@link SimpleVectorValueChecker} |
| * with default tolerances. |
| * @deprecated See {@link SimpleVectorValueChecker#SimpleVectorValueChecker()} |
| */ |
| @Deprecated |
| public GaussNewtonOptimizer() { |
| this(true); |
| } |
| |
| /** |
| * Simple constructor with default settings. |
| * The normal equations will be solved using LU decomposition. |
| * |
| * @param checker Convergence checker. |
| */ |
| public GaussNewtonOptimizer(ConvergenceChecker<PointVectorValuePair> checker) { |
| this(true, checker); |
| } |
| |
| /** |
| * Simple constructor with default settings. |
| * The convergence check is set to a {@link SimpleVectorValueChecker} |
| * with default tolerances. |
| * |
| * @param useLU If {@code true}, the normal equations will be solved |
| * using LU decomposition, otherwise they will be solved using QR |
| * decomposition. |
| * @deprecated See {@link SimpleVectorValueChecker#SimpleVectorValueChecker()} |
| */ |
| @Deprecated |
| public GaussNewtonOptimizer(final boolean useLU) { |
| this(useLU, new SimpleVectorValueChecker()); |
| } |
| |
| /** |
| * @param useLU If {@code true}, the normal equations will be solved |
| * using LU decomposition, otherwise they will be solved using QR |
| * decomposition. |
| * @param checker Convergence checker. |
| */ |
| public GaussNewtonOptimizer(final boolean useLU, |
| ConvergenceChecker<PointVectorValuePair> checker) { |
| super(checker); |
| this.useLU = useLU; |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public PointVectorValuePair doOptimize() { |
| final ConvergenceChecker<PointVectorValuePair> checker |
| = getConvergenceChecker(); |
| |
| // Computation will be useless without a checker (see "for-loop"). |
| if (checker == null) { |
| throw new NullArgumentException(); |
| } |
| |
| final double[] targetValues = getTarget(); |
| final int nR = targetValues.length; // Number of observed data. |
| |
| final RealMatrix weightMatrix = getWeight(); |
| // Diagonal of the weight matrix. |
| final double[] residualsWeights = new double[nR]; |
| for (int i = 0; i < nR; i++) { |
| residualsWeights[i] = weightMatrix.getEntry(i, i); |
| } |
| |
| final double[] currentPoint = getStartPoint(); |
| final int nC = currentPoint.length; |
| |
| // iterate until convergence is reached |
| PointVectorValuePair current = null; |
| int iter = 0; |
| for (boolean converged = false; !converged;) { |
| ++iter; |
| |
| // evaluate the objective function and its jacobian |
| PointVectorValuePair previous = current; |
| // Value of the objective function at "currentPoint". |
| final double[] currentObjective = computeObjectiveValue(currentPoint); |
| final double[] currentResiduals = computeResiduals(currentObjective); |
| final RealMatrix weightedJacobian = computeWeightedJacobian(currentPoint); |
| current = new PointVectorValuePair(currentPoint, currentObjective); |
| |
| // build the linear problem |
| final double[] b = new double[nC]; |
| final double[][] a = new double[nC][nC]; |
| for (int i = 0; i < nR; ++i) { |
| |
| final double[] grad = weightedJacobian.getRow(i); |
| final double weight = residualsWeights[i]; |
| final double residual = currentResiduals[i]; |
| |
| // compute the normal equation |
| final double wr = weight * residual; |
| for (int j = 0; j < nC; ++j) { |
| b[j] += wr * grad[j]; |
| } |
| |
| // build the contribution matrix for measurement i |
| for (int k = 0; k < nC; ++k) { |
| double[] ak = a[k]; |
| double wgk = weight * grad[k]; |
| for (int l = 0; l < nC; ++l) { |
| ak[l] += wgk * grad[l]; |
| } |
| } |
| } |
| |
| try { |
| // solve the linearized least squares problem |
| RealMatrix mA = new BlockRealMatrix(a); |
| DecompositionSolver solver = useLU ? |
| new LUDecomposition(mA).getSolver() : |
| new QRDecomposition(mA).getSolver(); |
| final double[] dX = solver.solve(new ArrayRealVector(b, false)).toArray(); |
| // update the estimated parameters |
| for (int i = 0; i < nC; ++i) { |
| currentPoint[i] += dX[i]; |
| } |
| } catch (SingularMatrixException e) { |
| throw new ConvergenceException(LocalizedFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM); |
| } |
| |
| // Check convergence. |
| if (previous != null) { |
| converged = checker.converged(iter, previous, current); |
| if (converged) { |
| cost = computeCost(currentResiduals); |
| // Update (deprecated) "point" field. |
| point = current.getPoint(); |
| return current; |
| } |
| } |
| } |
| // Must never happen. |
| throw new MathInternalError(); |
| } |
| } |