/*
    * 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
    *
    *      https://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.
   */
  
  /*
   * This is not the original file distributed by the Apache Software Foundation
   * It has been modified by the Hipparchus project
   */
  package org.hipparchus.optim.nonlinear.vector.leastsquares;
  
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.exception.MathIllegalStateException;
  import org.hipparchus.exception.NullArgumentException;
  import org.hipparchus.linear.ArrayRealVector;
  import org.hipparchus.linear.MatrixDecomposer;
  import org.hipparchus.linear.MatrixUtils;
  import org.hipparchus.linear.QRDecomposer;
  import org.hipparchus.linear.RealMatrix;
  import org.hipparchus.linear.RealVector;
  import org.hipparchus.optim.ConvergenceChecker;
  import org.hipparchus.optim.LocalizedOptimFormats;
  import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem.Evaluation;
  import org.hipparchus.util.Incrementor;
  import org.hipparchus.util.Pair;
  
  /**
   * 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
   * Cholesky decomposition can be used to solve the normal equations, or QR
   * decomposition or SVD decomposition can be used to solve the linear system.
   * Cholesky/LU decomposition is faster but QR decomposition is more robust for difficult
   * problems, and SVD can compute a solution for rank-deficient problems.
   */
  public class GaussNewtonOptimizer implements LeastSquaresOptimizer {
  
      /**
       * The singularity threshold for matrix decompositions. Determines when a {@link
       * MathIllegalStateException} is thrown. The current value was the default value for {@link
       * org.hipparchus.linear.LUDecomposition}.
       */
      private static final double SINGULARITY_THRESHOLD = 1e-11;
  
      /** Decomposer */
      private final MatrixDecomposer decomposer;
  
      /** Indicates if normal equations should be formed explicitly. */
      private final boolean formNormalEquations;
  
      /**
       * Creates a Gauss Newton optimizer.
       * <p>
       * The default for the algorithm is to use QR decomposition and not
       * form normal equations.
       * </p>
       */
      public GaussNewtonOptimizer() {
          this(new QRDecomposer(SINGULARITY_THRESHOLD), false);
      }
  
      /**
       * Create a Gauss Newton optimizer that uses the given matrix decomposition algorithm
       * to solve the normal equations.
       *
       * @param decomposer          the decomposition algorithm to use.
       * @param formNormalEquations whether the normal equations should be explicitly
       *                            formed. If {@code true} then {@code decomposer} is used
       *                            to solve J<sup>T</sup>Jx=J<sup>T</sup>r, otherwise
       *                            {@code decomposer} is used to solve Jx=r. If {@code
       *                            decomposer} can only solve square systems then this
       *                            parameter should be {@code true}.
       */
      public GaussNewtonOptimizer(final MatrixDecomposer decomposer,
                                  final boolean formNormalEquations) {
          this.decomposer          = decomposer;
          this.formNormalEquations = formNormalEquations;
      }
  
      /**
       * Get the matrix decomposition algorithm.
       *
       * @return the decomposition algorithm.
       */
      public MatrixDecomposer getDecomposer() {
          return decomposer;
     }
 
     /**
      * Configure the matrix decomposition algorithm.
      *
      * @param newDecomposer the decomposition algorithm to use.
      * @return a new instance.
      */
     public GaussNewtonOptimizer withDecomposer(final MatrixDecomposer newDecomposer) {
         return new GaussNewtonOptimizer(newDecomposer, this.isFormNormalEquations());
     }
 
     /**
      * Get if the normal equations are explicitly formed.
      *
      * @return if the normal equations should be explicitly formed. If {@code true} then
      * {@code decomposer} is used to solve J<sup>T</sup>Jx=J<sup>T</sup>r, otherwise
      * {@code decomposer} is used to solve Jx=r.
      */
     public boolean isFormNormalEquations() {
         return formNormalEquations;
     }
 
     /**
      * Configure if the normal equations should be explicitly formed.
      *
      * @param newFormNormalEquations whether the normal equations should be explicitly
      *                               formed. If {@code true} then {@code decomposer} is used
      *                               to solve J<sup>T</sup>Jx=J<sup>T</sup>r, otherwise
      *                               {@code decomposer} is used to solve Jx=r. If {@code
      *                               decomposer} can only solve square systems then this
      *                               parameter should be {@code true}.
      * @return a new instance.
      */
     public GaussNewtonOptimizer withFormNormalEquations(final boolean newFormNormalEquations) {
         return new GaussNewtonOptimizer(this.getDecomposer(), newFormNormalEquations);
     }
 
     /** {@inheritDoc} */
     @Override
     public Optimum optimize(final LeastSquaresProblem lsp) {
         //create local evaluation and iteration counts
         final Incrementor evaluationCounter = lsp.getEvaluationCounter();
         final Incrementor iterationCounter = lsp.getIterationCounter();
         final ConvergenceChecker<Evaluation> checker
                 = lsp.getConvergenceChecker();
 
         // Computation will be useless without a checker (see "for-loop").
         if (checker == null) {
             throw new NullArgumentException();
         }
 
         RealVector currentPoint = lsp.getStart();
 
         // iterate until convergence is reached
         Evaluation current = null;
         while (true) {
             iterationCounter.increment();
 
             // evaluate the objective function and its jacobian
             Evaluation previous = current;
             // Value of the objective function at "currentPoint".
             evaluationCounter.increment();
             current = lsp.evaluate(currentPoint);
             final RealVector currentResiduals = current.getResiduals();
             final RealMatrix weightedJacobian = current.getJacobian();
             currentPoint = current.getPoint();
 
             // Check convergence.
             if (previous != null &&
                 checker.converged(iterationCounter.getCount(), previous, current)) {
                 return Optimum.of(current,
                                   evaluationCounter.getCount(),
                                   iterationCounter.getCount());
             }
 
             // solve the linearized least squares problem
             final RealMatrix lhs; // left hand side
             final RealVector rhs; // right hand side
             if (this.formNormalEquations) {
                 final Pair<RealMatrix, RealVector> normalEquation =
                         computeNormalMatrix(weightedJacobian, currentResiduals);
                 lhs = normalEquation.getFirst();
                 rhs = normalEquation.getSecond();
             } else {
                 lhs = weightedJacobian;
                 rhs = currentResiduals;
             }
             final RealVector dX;
             try {
                 dX = this.decomposer.decompose(lhs).solve(rhs);
             } catch (MathIllegalArgumentException e) {
                 // change exception message
                 throw new MathIllegalStateException(
                         LocalizedOptimFormats.UNABLE_TO_SOLVE_SINGULAR_PROBLEM, e);
             }
             // update the estimated parameters
             currentPoint = currentPoint.add(dX);
         }
     }
 
     /** {@inheritDoc} */
     @Override
     public String toString() {
         return "GaussNewtonOptimizer{" +
                 "decomposer=" + decomposer +
                 ", formNormalEquations=" + formNormalEquations +
                 '}';
     }
 
     /**
      * Compute the normal matrix, J<sup>T</sup>J.
      *
      * @param jacobian  the m by n jacobian matrix, J. Input.
      * @param residuals the m by 1 residual vector, r. Input.
      * @return  the n by n normal matrix and  the n by 1 J<sup>Tr vector.
      */
     private static Pair<RealMatrix, RealVector> computeNormalMatrix(final RealMatrix jacobian,
                                                                     final RealVector residuals) {
         //since the normal matrix is symmetric, we only need to compute half of it.
         final int nR = jacobian.getRowDimension();
         final int nC = jacobian.getColumnDimension();
         //allocate space for return values
         final RealMatrix normal = MatrixUtils.createRealMatrix(nC, nC);
         final RealVector jTr = new ArrayRealVector(nC);
         //for each measurement
         for (int i = 0; i < nR; ++i) {
             //compute JTr for measurement i
             for (int j = 0; j < nC; j++) {
                 jTr.setEntry(j, jTr.getEntry(j) +
                         residuals.getEntry(i) * jacobian.getEntry(i, j));
             }
 
             // add the the contribution to the normal matrix for measurement i
             for (int k = 0; k < nC; ++k) {
                 //only compute the upper triangular part
                 for (int l = k; l < nC; ++l) {
                     normal.setEntry(k, l, normal.getEntry(k, l) +
                             jacobian.getEntry(i, k) * jacobian.getEntry(i, l));
                 }
             }
         }
         //copy the upper triangular part to the lower triangular part.
         for (int i = 0; i < nC; i++) {
             for (int j = 0; j < i; j++) {
                 normal.setEntry(i, j, normal.getEntry(j, i));
             }
         }
         return new Pair<RealMatrix, RealVector>(normal, jTr);
     }
 
 }