/*
    * 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.math4.legacy.linear;
  
  import org.apache.commons.numbers.complex.Complex;
  import org.apache.commons.numbers.core.Precision;
  import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
  import org.apache.commons.math4.legacy.exception.MathArithmeticException;
  import org.apache.commons.math4.legacy.exception.MathUnsupportedOperationException;
  import org.apache.commons.math4.legacy.exception.MaxCountExceededException;
  import org.apache.commons.math4.legacy.exception.util.LocalizedFormats;
  import org.apache.commons.math4.core.jdkmath.JdkMath;
  
  /**
   * Calculates the eigen decomposition of a real matrix.
   * <p>
   * The eigen decomposition of matrix A is a set of two matrices:
   * V and D such that A = V &times; D &times; V<sup>T</sup>.
   * A, V and D are all m &times; m matrices.
   * <p>
   * This class is similar in spirit to the {@code EigenvalueDecomposition}
   * class from the <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a>
   * library, with the following changes:
   * <ul>
   *   <li>a {@link #getVT() getVt} method has been added,</li>
   *   <li>two {@link #getRealEigenvalue(int) getRealEigenvalue} and
   *       {@link #getImagEigenvalue(int) getImagEigenvalue} methods to pick up a
   *       single eigenvalue have been added,</li>
   *   <li>a {@link #getEigenvector(int) getEigenvector} method to pick up a
   *       single eigenvector has been added,</li>
   *   <li>a {@link #getDeterminant() getDeterminant} method has been added.</li>
   *   <li>a {@link #getSolver() getSolver} method has been added.</li>
   * </ul>
   * <p>
   * As of 3.1, this class supports general real matrices (both symmetric and non-symmetric):
   * <p>
   * If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is diagonal
   * and the eigenvector matrix V is orthogonal, i.e.
   * {@code A = V.multiply(D.multiply(V.transpose()))} and
   * {@code V.multiply(V.transpose())} equals the identity matrix.
   * </p>
   * <p>
   * If A is not symmetric, then the eigenvalue matrix D is block diagonal with the real
   * eigenvalues in 1-by-1 blocks and any complex eigenvalues, lambda + i*mu, in 2-by-2
   * blocks:
   * <pre>
   *    [lambda, mu    ]
   *    [   -mu, lambda]
   * </pre>
   * The columns of V represent the eigenvectors in the sense that {@code A*V = V*D},
   * i.e. A.multiply(V) equals V.multiply(D).
   * The matrix V may be badly conditioned, or even singular, so the validity of the
   * equation {@code A = V*D*inverse(V)} depends upon the condition of V.
   * <p>
   * This implementation is based on the paper by A. Drubrulle, R.S. Martin and
   * J.H. Wilkinson "The Implicit QL Algorithm" in Wilksinson and Reinsch (1971)
   * Handbook for automatic computation, vol. 2, Linear algebra, Springer-Verlag,
   * New-York.
   *
   * @see <a href="http://mathworld.wolfram.com/EigenDecomposition.html">MathWorld</a>
   * @see <a href="http://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix">Wikipedia</a>
   * @since 2.0 (changed to concrete class in 3.0)
   */
  public class EigenDecomposition {
      /** Internally used epsilon criteria. */
      private static final double EPSILON = 1e-12;
      /** Maximum number of iterations accepted in the implicit QL transformation. */
      private static final byte MAX_ITER = 30;
      /** Main diagonal of the tridiagonal matrix. */
      private double[] main;
      /** Secondary diagonal of the tridiagonal matrix. */
      private double[] secondary;
      /**
       * Transformer to tridiagonal (may be null if matrix is already
       * tridiagonal).
       */
      private TriDiagonalTransformer transformer;
      /** Real part of the realEigenvalues. */
      private double[] realEigenvalues;
      /** Imaginary part of the realEigenvalues. */
      private double[] imagEigenvalues;
      /** Eigenvectors. */
      private ArrayRealVector[] eigenvectors;
      /** Cached value of V. */
     private RealMatrix cachedV;
     /** Cached value of D. */
     private RealMatrix cachedD;
     /** Cached value of Vt. */
     private RealMatrix cachedVt;
     /** Whether the matrix is symmetric. */
     private final boolean isSymmetric;
 
     /**
      * Calculates the eigen decomposition of the given real matrix.
      * <p>
      * Supports decomposition of a general matrix since 3.1.
      *
      * @param matrix Matrix to decompose.
      * @throws MaxCountExceededException if the algorithm fails to converge.
      * @throws MathArithmeticException if the decomposition of a general matrix
      * results in a matrix with zero norm
      * @since 3.1
      */
     public EigenDecomposition(final RealMatrix matrix)
         throws MathArithmeticException {
         final double symTol = 10 * matrix.getRowDimension() * matrix.getColumnDimension() * Precision.EPSILON;
         isSymmetric = MatrixUtils.isSymmetric(matrix, symTol);
         if (isSymmetric) {
             transformToTridiagonal(matrix);
             findEigenVectors(transformer.getQ().getData());
         } else {
             final SchurTransformer t = transformToSchur(matrix);
             findEigenVectorsFromSchur(t);
         }
     }
 
     /**
      * Calculates the eigen decomposition of the symmetric tridiagonal
      * matrix.  The Householder matrix is assumed to be the identity matrix.
      *
      * @param main Main diagonal of the symmetric tridiagonal form.
      * @param secondary Secondary of the tridiagonal form.
      * @throws MaxCountExceededException if the algorithm fails to converge.
      * @since 3.1
      */
     public EigenDecomposition(final double[] main, final double[] secondary) {
         isSymmetric = true;
         this.main      = main.clone();
         this.secondary = secondary.clone();
         transformer    = null;
         final int size = main.length;
         final double[][] z = new double[size][size];
         for (int i = 0; i < size; i++) {
             z[i][i] = 1.0;
         }
         findEigenVectors(z);
     }
 
     /**
      * Gets the matrix V of the decomposition.
      * V is an orthogonal matrix, i.e. its transpose is also its inverse.
      * The columns of V are the eigenvectors of the original matrix.
      * No assumption is made about the orientation of the system axes formed
      * by the columns of V (e.g. in a 3-dimension space, V can form a left-
      * or right-handed system).
      *
      * @return the V matrix.
      */
     public RealMatrix getV() {
 
         if (cachedV == null) {
             final int m = eigenvectors.length;
             cachedV = MatrixUtils.createRealMatrix(m, m);
             for (int k = 0; k < m; ++k) {
                 cachedV.setColumnVector(k, eigenvectors[k]);
             }
         }
         // return the cached matrix
         return cachedV;
     }
 
     /**
      * Gets the block diagonal matrix D of the decomposition.
      * D is a block diagonal matrix.
      * Real eigenvalues are on the diagonal while complex values are on
      * 2x2 blocks { {real +imaginary}, {-imaginary, real} }.
      *
      * @return the D matrix.
      *
      * @see #getRealEigenvalues()
      * @see #getImagEigenvalues()
      */
     public RealMatrix getD() {
 
         if (cachedD == null) {
             // cache the matrix for subsequent calls
             cachedD = MatrixUtils.createRealMatrixWithDiagonal(realEigenvalues);
 
             for (int i = 0; i < imagEigenvalues.length; i++) {
                 if (Precision.compareTo(imagEigenvalues[i], 0.0, EPSILON) > 0) {
                     cachedD.setEntry(i, i+1, imagEigenvalues[i]);
                 } else if (Precision.compareTo(imagEigenvalues[i], 0.0, EPSILON) < 0) {
                     cachedD.setEntry(i, i-1, imagEigenvalues[i]);
                 }
             }
         }
         return cachedD;
     }
 
     /**
      * Gets the transpose of the matrix V of the decomposition.
      * V is an orthogonal matrix, i.e. its transpose is also its inverse.
      * The columns of V are the eigenvectors of the original matrix.
      * No assumption is made about the orientation of the system axes formed
      * by the columns of V (e.g. in a 3-dimension space, V can form a left-
      * or right-handed system).
      *
      * @return the transpose of the V matrix.
      */
     public RealMatrix getVT() {
 
         if (cachedVt == null) {
             final int m = eigenvectors.length;
             cachedVt = MatrixUtils.createRealMatrix(m, m);
             for (int k = 0; k < m; ++k) {
                 cachedVt.setRowVector(k, eigenvectors[k]);
             }
         }
 
         // return the cached matrix
         return cachedVt;
     }
 
     /**
      * Returns whether the calculated eigen values are complex or real.
      * <p>The method performs a zero check for each element of the
      * {@link #getImagEigenvalues()} array and returns {@code true} if any
      * element is not equal to zero.
      *
      * @return {@code true} if the eigen values are complex, {@code false} otherwise
      * @since 3.1
      */
     public boolean hasComplexEigenvalues() {
         for (int i = 0; i < imagEigenvalues.length; i++) {
             if (!Precision.equals(imagEigenvalues[i], 0.0, EPSILON)) {
                 return true;
             }
         }
         return false;
     }
 
     /**
      * Gets a copy of the real parts of the eigenvalues of the original matrix.
      *
      * @return a copy of the real parts of the eigenvalues of the original matrix.
      *
      * @see #getD()
      * @see #getRealEigenvalue(int)
      * @see #getImagEigenvalues()
      */
     public double[] getRealEigenvalues() {
         return realEigenvalues.clone();
     }
 
     /**
      * Returns the real part of the i<sup>th</sup> eigenvalue of the original
      * matrix.
      *
      * @param i index of the eigenvalue (counting from 0)
      * @return real part of the i<sup>th</sup> eigenvalue of the original
      * matrix.
      *
      * @see #getD()
      * @see #getRealEigenvalues()
      * @see #getImagEigenvalue(int)
      */
     public double getRealEigenvalue(final int i) {
         return realEigenvalues[i];
     }
 
     /**
      * Gets a copy of the imaginary parts of the eigenvalues of the original
      * matrix.
      *
      * @return a copy of the imaginary parts of the eigenvalues of the original
      * matrix.
      *
      * @see #getD()
      * @see #getImagEigenvalue(int)
      * @see #getRealEigenvalues()
      */
     public double[] getImagEigenvalues() {
         return imagEigenvalues.clone();
     }
 
     /**
      * Gets the imaginary part of the i<sup>th</sup> eigenvalue of the original
      * matrix.
      *
      * @param i Index of the eigenvalue (counting from 0).
      * @return the imaginary part of the i<sup>th</sup> eigenvalue of the original
      * matrix.
      *
      * @see #getD()
      * @see #getImagEigenvalues()
      * @see #getRealEigenvalue(int)
      */
     public double getImagEigenvalue(final int i) {
         return imagEigenvalues[i];
     }
 
     /**
      * Gets a copy of the i<sup>th</sup> eigenvector of the original matrix.
      *
      * @param i Index of the eigenvector (counting from 0).
      * @return a copy of the i<sup>th</sup> eigenvector of the original matrix.
      * @see #getD()
      */
     public RealVector getEigenvector(final int i) {
         return eigenvectors[i].copy();
     }
 
     /**
      * Computes the determinant of the matrix.
      *
      * @return the determinant of the matrix.
      */
     public double getDeterminant() {
         double determinant = 1;
         for (double lambda : realEigenvalues) {
             determinant *= lambda;
         }
         return determinant;
     }
 
     /**
      * Computes the square-root of the matrix.
      * This implementation assumes that the matrix is symmetric and positive
      * definite.
      *
      * @return the square-root of the matrix.
      * @throws MathUnsupportedOperationException if the matrix is not
      * symmetric or not positive definite.
      * @since 3.1
      */
     public RealMatrix getSquareRoot() {
         if (!isSymmetric) {
             throw new MathUnsupportedOperationException();
         }
 
         final double[] sqrtEigenValues = new double[realEigenvalues.length];
         for (int i = 0; i < realEigenvalues.length; i++) {
             final double eigen = realEigenvalues[i];
             if (eigen <= 0) {
                 throw new MathUnsupportedOperationException();
             }
             sqrtEigenValues[i] = JdkMath.sqrt(eigen);
         }
         final RealMatrix sqrtEigen = MatrixUtils.createRealDiagonalMatrix(sqrtEigenValues);
         final RealMatrix v = getV();
         final RealMatrix vT = getVT();
 
         return v.multiply(sqrtEigen).multiply(vT);
     }
 
     /**
      * Gets a solver for finding the A &times; X = B solution in exact
      * linear sense.
      * <p>
      * Since 3.1, eigen decomposition of a general matrix is supported,
      * but the {@link DecompositionSolver} only supports real eigenvalues.
      *
      * @return a solver
      * @throws MathUnsupportedOperationException if the decomposition resulted in
      * complex eigenvalues
      */
     public DecompositionSolver getSolver() {
         if (hasComplexEigenvalues()) {
             throw new MathUnsupportedOperationException();
         }
         return new Solver(realEigenvalues, imagEigenvalues, eigenvectors);
     }
 
     /** Specialized solver. */
     private static final class Solver implements DecompositionSolver {
         /** Real part of the realEigenvalues. */
         private final double[] realEigenvalues;
         /** Imaginary part of the realEigenvalues. */
         private final double[] imagEigenvalues;
         /** Eigenvectors. */
         private final ArrayRealVector[] eigenvectors;
 
         /**
          * Builds a solver from decomposed matrix.
          *
          * @param realEigenvalues Real parts of the eigenvalues.
          * @param imagEigenvalues Imaginary parts of the eigenvalues.
          * @param eigenvectors Eigenvectors.
          */
         private Solver(final double[] realEigenvalues,
                 final double[] imagEigenvalues,
                 final ArrayRealVector[] eigenvectors) {
             this.realEigenvalues = realEigenvalues;
             this.imagEigenvalues = imagEigenvalues;
             this.eigenvectors = eigenvectors;
         }
 
         /**
          * Solves the linear equation A &times; X = B for symmetric matrices A.
          * <p>
          * This method only finds exact linear solutions, i.e. solutions for
          * which ||A &times; X - B|| is exactly 0.
          * </p>
          *
          * @param b Right-hand side of the equation A &times; X = B.
          * @return a Vector X that minimizes the two norm of A &times; X - B.
          *
          * @throws DimensionMismatchException if the matrices dimensions do not match.
          * @throws SingularMatrixException if the decomposed matrix is singular.
          */
         @Override
         public RealVector solve(final RealVector b) {
             if (!isNonSingular()) {
                 throw new SingularMatrixException();
             }
 
             final int m = realEigenvalues.length;
             if (b.getDimension() != m) {
                 throw new DimensionMismatchException(b.getDimension(), m);
             }
 
             final double[] bp = new double[m];
             for (int i = 0; i < m; ++i) {
                 final ArrayRealVector v = eigenvectors[i];
                 final double[] vData = v.getDataRef();
                 final double s = v.dotProduct(b) / realEigenvalues[i];
                 for (int j = 0; j < m; ++j) {
                     bp[j] += s * vData[j];
                 }
             }
 
             return new ArrayRealVector(bp, false);
         }
 
         /** {@inheritDoc} */
         @Override
         public RealMatrix solve(RealMatrix b) {
 
             if (!isNonSingular()) {
                 throw new SingularMatrixException();
             }
 
             final int m = realEigenvalues.length;
             if (b.getRowDimension() != m) {
                 throw new DimensionMismatchException(b.getRowDimension(), m);
             }
 
             final int nColB = b.getColumnDimension();
             final double[][] bp = new double[m][nColB];
             final double[] tmpCol = new double[m];
             for (int k = 0; k < nColB; ++k) {
                 for (int i = 0; i < m; ++i) {
                     tmpCol[i] = b.getEntry(i, k);
                     bp[i][k]  = 0;
                 }
                 for (int i = 0; i < m; ++i) {
                     final ArrayRealVector v = eigenvectors[i];
                     final double[] vData = v.getDataRef();
                     double s = 0;
                     for (int j = 0; j < m; ++j) {
                         s += v.getEntry(j) * tmpCol[j];
                     }
                     s /= realEigenvalues[i];
                     for (int j = 0; j < m; ++j) {
                         bp[j][k] += s * vData[j];
                     }
                 }
             }
 
             return new Array2DRowRealMatrix(bp, false);
         }
 
         /**
          * Checks whether the decomposed matrix is non-singular.
          *
          * @return true if the decomposed matrix is non-singular.
          */
         @Override
         public boolean isNonSingular() {
             double largestEigenvalueNorm = 0.0;
             // Looping over all values (in case they are not sorted in decreasing
             // order of their norm).
             for (int i = 0; i < realEigenvalues.length; ++i) {
                 largestEigenvalueNorm = JdkMath.max(largestEigenvalueNorm, eigenvalueNorm(i));
             }
             // Corner case: zero matrix, all exactly 0 eigenvalues
             if (largestEigenvalueNorm == 0.0) {
                 return false;
             }
             for (int i = 0; i < realEigenvalues.length; ++i) {
                 // Looking for eigenvalues that are 0, where we consider anything much much smaller
                 // than the largest eigenvalue to be effectively 0.
                 if (Precision.equals(eigenvalueNorm(i) / largestEigenvalueNorm, 0, EPSILON)) {
                     return false;
                 }
             }
             return true;
         }
 
         /**
          * @param i which eigenvalue to find the norm of
          * @return the norm of ith (complex) eigenvalue.
          */
         private double eigenvalueNorm(int i) {
             final double re = realEigenvalues[i];
             final double im = imagEigenvalues[i];
             return JdkMath.sqrt(re * re + im * im);
         }
 
         /**
          * Get the inverse of the decomposed matrix.
          *
          * @return the inverse matrix.
          * @throws SingularMatrixException if the decomposed matrix is singular.
          */
         @Override
         public RealMatrix getInverse() {
             if (!isNonSingular()) {
                 throw new SingularMatrixException();
             }
 
             final int m = realEigenvalues.length;
             final double[][] invData = new double[m][m];
 
             for (int i = 0; i < m; ++i) {
                 final double[] invI = invData[i];
                 for (int j = 0; j < m; ++j) {
                     double invIJ = 0;
                     for (int k = 0; k < m; ++k) {
                         final double[] vK = eigenvectors[k].getDataRef();
                         invIJ += vK[i] * vK[j] / realEigenvalues[k];
                     }
                     invI[j] = invIJ;
                 }
             }
             return MatrixUtils.createRealMatrix(invData);
         }
     }
 
     /**
      * Transforms the matrix to tridiagonal form.
      *
      * @param matrix Matrix to transform.
      */
     private void transformToTridiagonal(final RealMatrix matrix) {
         // transform the matrix to tridiagonal
         transformer = new TriDiagonalTransformer(matrix);
         main = transformer.getMainDiagonalRef();
         secondary = transformer.getSecondaryDiagonalRef();
     }
 
     /**
      * Find eigenvalues and eigenvectors (Dubrulle et al., 1971).
      *
      * @param householderMatrix Householder matrix of the transformation
      * to tridiagonal form.
      */
     private void findEigenVectors(final double[][] householderMatrix) {
         final double[][]z = householderMatrix.clone();
         final int n = main.length;
         realEigenvalues = new double[n];
         imagEigenvalues = new double[n];
         final double[] e = new double[n];
         for (int i = 0; i < n - 1; i++) {
             realEigenvalues[i] = main[i];
             e[i] = secondary[i];
         }
         realEigenvalues[n - 1] = main[n - 1];
         e[n - 1] = 0;
 
         // Determine the largest main and secondary value in absolute term.
         double maxAbsoluteValue = 0;
         for (int i = 0; i < n; i++) {
             if (JdkMath.abs(realEigenvalues[i]) > maxAbsoluteValue) {
                 maxAbsoluteValue = JdkMath.abs(realEigenvalues[i]);
             }
             if (JdkMath.abs(e[i]) > maxAbsoluteValue) {
                 maxAbsoluteValue = JdkMath.abs(e[i]);
             }
         }
         // Make null any main and secondary value too small to be significant
         if (maxAbsoluteValue != 0) {
             for (int i=0; i < n; i++) {
                 if (JdkMath.abs(realEigenvalues[i]) <= Precision.EPSILON * maxAbsoluteValue) {
                     realEigenvalues[i] = 0;
                 }
                 if (JdkMath.abs(e[i]) <= Precision.EPSILON * maxAbsoluteValue) {
                     e[i]=0;
                 }
             }
         }
 
         for (int j = 0; j < n; j++) {
             int its = 0;
             int m;
             do {
                 for (m = j; m < n - 1; m++) {
                     double delta = JdkMath.abs(realEigenvalues[m]) +
                         JdkMath.abs(realEigenvalues[m + 1]);
                     if (JdkMath.abs(e[m]) + delta == delta) {
                         break;
                     }
                 }
                 if (m != j) {
                     if (its == MAX_ITER) {
                         throw new MaxCountExceededException(LocalizedFormats.CONVERGENCE_FAILED,
                                                             MAX_ITER);
                     }
                     its++;
                     double q = (realEigenvalues[j + 1] - realEigenvalues[j]) / (2 * e[j]);
                     double t = JdkMath.sqrt(1 + q * q);
                     if (q < 0.0) {
                         q = realEigenvalues[m] - realEigenvalues[j] + e[j] / (q - t);
                     } else {
                         q = realEigenvalues[m] - realEigenvalues[j] + e[j] / (q + t);
                     }
                     double u = 0.0;
                     double s = 1.0;
                     double c = 1.0;
                     int i;
                     for (i = m - 1; i >= j; i--) {
                         double p = s * e[i];
                         double h = c * e[i];
                         if (JdkMath.abs(p) >= JdkMath.abs(q)) {
                             c = q / p;
                             t = JdkMath.sqrt(c * c + 1.0);
                             e[i + 1] = p * t;
                             s = 1.0 / t;
                             c *= s;
                         } else {
                             s = p / q;
                             t = JdkMath.sqrt(s * s + 1.0);
                             e[i + 1] = q * t;
                             c = 1.0 / t;
                             s *= c;
                         }
                         if (e[i + 1] == 0.0) {
                             realEigenvalues[i + 1] -= u;
                             e[m] = 0.0;
                             break;
                         }
                         q = realEigenvalues[i + 1] - u;
                         t = (realEigenvalues[i] - q) * s + 2.0 * c * h;
                         u = s * t;
                         realEigenvalues[i + 1] = q + u;
                         q = c * t - h;
                         for (int ia = 0; ia < n; ia++) {
                             p = z[ia][i + 1];
                             z[ia][i + 1] = s * z[ia][i] + c * p;
                             z[ia][i] = c * z[ia][i] - s * p;
                         }
                     }
                     if (t == 0.0 && i >= j) {
                         continue;
                     }
                     realEigenvalues[j] -= u;
                     e[j] = q;
                     e[m] = 0.0;
                 }
             } while (m != j);
         }
 
         //Sort the eigen values (and vectors) in increase order
         for (int i = 0; i < n; i++) {
             int k = i;
             double p = realEigenvalues[i];
             for (int j = i + 1; j < n; j++) {
                 if (realEigenvalues[j] > p) {
                     k = j;
                     p = realEigenvalues[j];
                 }
             }
             if (k != i) {
                 realEigenvalues[k] = realEigenvalues[i];
                 realEigenvalues[i] = p;
                 for (int j = 0; j < n; j++) {
                     p = z[j][i];
                     z[j][i] = z[j][k];
                     z[j][k] = p;
                 }
             }
         }
 
         // Determine the largest eigen value in absolute term.
         maxAbsoluteValue = 0;
         for (int i = 0; i < n; i++) {
             if (JdkMath.abs(realEigenvalues[i]) > maxAbsoluteValue) {
                 maxAbsoluteValue=JdkMath.abs(realEigenvalues[i]);
             }
         }
         // Make null any eigen value too small to be significant
         if (maxAbsoluteValue != 0.0) {
             for (int i=0; i < n; i++) {
                 if (JdkMath.abs(realEigenvalues[i]) < Precision.EPSILON * maxAbsoluteValue) {
                     realEigenvalues[i] = 0;
                 }
             }
         }
         eigenvectors = new ArrayRealVector[n];
         final double[] tmp = new double[n];
         for (int i = 0; i < n; i++) {
             for (int j = 0; j < n; j++) {
                 tmp[j] = z[j][i];
             }
             eigenvectors[i] = new ArrayRealVector(tmp);
         }
     }
 
     /**
      * Transforms the matrix to Schur form and calculates the eigenvalues.
      *
      * @param matrix Matrix to transform.
      * @return the {@link SchurTransformer Shur transform} for this matrix
      */
     private SchurTransformer transformToSchur(final RealMatrix matrix) {
         final SchurTransformer schurTransform = new SchurTransformer(matrix);
         final double[][] matT = schurTransform.getT().getData();
 
         realEigenvalues = new double[matT.length];
         imagEigenvalues = new double[matT.length];
 
         for (int i = 0; i < realEigenvalues.length; i++) {
             if (i == (realEigenvalues.length - 1) ||
                 Precision.equals(matT[i + 1][i], 0.0, EPSILON)) {
                 realEigenvalues[i] = matT[i][i];
             } else {
                 final double x = matT[i + 1][i + 1];
                 final double p = 0.5 * (matT[i][i] - x);
                 final double z = JdkMath.sqrt(JdkMath.abs(p * p + matT[i + 1][i] * matT[i][i + 1]));
                 realEigenvalues[i] = x + p;
                 imagEigenvalues[i] = z;
                 realEigenvalues[i + 1] = x + p;
                 imagEigenvalues[i + 1] = -z;
                 i++;
             }
         }
         return schurTransform;
     }
 
     /**
      * Performs a division of two complex numbers.
      *
      * @param xr real part of the first number
      * @param xi imaginary part of the first number
      * @param yr real part of the second number
      * @param yi imaginary part of the second number
      * @return result of the complex division
      */
     private Complex cdiv(final double xr, final double xi,
                          final double yr, final double yi) {
         return Complex.ofCartesian(xr, xi).divide(Complex.ofCartesian(yr, yi));
     }
 
     /**
      * Find eigenvectors from a matrix transformed to Schur form.
      *
      * @param schur the schur transformation of the matrix
      * @throws MathArithmeticException if the Schur form has a norm of zero
      */
     private void findEigenVectorsFromSchur(final SchurTransformer schur)
         throws MathArithmeticException {
         final double[][] matrixT = schur.getT().getData();
         final double[][] matrixP = schur.getP().getData();
 
         final int n = matrixT.length;
 
         // compute matrix norm
         double norm = 0.0;
         for (int i = 0; i < n; i++) {
            for (int j = JdkMath.max(i - 1, 0); j < n; j++) {
                norm += JdkMath.abs(matrixT[i][j]);
            }
         }
 
         // we can not handle a matrix with zero norm
         if (Precision.equals(norm, 0.0, EPSILON)) {
            throw new MathArithmeticException(LocalizedFormats.ZERO_NORM);
         }
 
         // Backsubstitute to find vectors of upper triangular form
 
         double r = 0.0;
         double s = 0.0;
         double z = 0.0;
 
         for (int idx = n - 1; idx >= 0; idx--) {
             double p = realEigenvalues[idx];
             double q = imagEigenvalues[idx];
 
             if (Precision.equals(q, 0.0)) {
                 // Real vector
                 int l = idx;
                 matrixT[idx][idx] = 1.0;
                 for (int i = idx - 1; i >= 0; i--) {
                     double w = matrixT[i][i] - p;
                     r = 0.0;
                     for (int j = l; j <= idx; j++) {
                         r += matrixT[i][j] * matrixT[j][idx];
                     }
                     if (Precision.compareTo(imagEigenvalues[i], 0.0, EPSILON) < 0) {
                         z = w;
                         s = r;
                     } else {
                         l = i;
                         if (Precision.equals(imagEigenvalues[i], 0.0)) {
                             if (w != 0.0) {
                                 matrixT[i][idx] = -r / w;
                             } else {
                                 matrixT[i][idx] = -r / (Precision.EPSILON * norm);
                             }
                         } else {
                             // Solve real equations
                             double x = matrixT[i][i + 1];
                             double y = matrixT[i + 1][i];
                             q = (realEigenvalues[i] - p) * (realEigenvalues[i] - p) +
                                 imagEigenvalues[i] * imagEigenvalues[i];
                             double t = (x * s - z * r) / q;
                             matrixT[i][idx] = t;
                             if (JdkMath.abs(x) > JdkMath.abs(z)) {
                                 matrixT[i + 1][idx] = (-r - w * t) / x;
                             } else {
                                 matrixT[i + 1][idx] = (-s - y * t) / z;
                             }
                         }
 
                         // Overflow control
                         double t = JdkMath.abs(matrixT[i][idx]);
                         if ((Precision.EPSILON * t) * t > 1) {
                             for (int j = i; j <= idx; j++) {
                                 matrixT[j][idx] /= t;
                             }
                         }
                     }
                 }
             } else if (q < 0.0) {
                 // Complex vector
                 int l = idx - 1;
 
                 // Last vector component imaginary so matrix is triangular
                 if (JdkMath.abs(matrixT[idx][idx - 1]) > JdkMath.abs(matrixT[idx - 1][idx])) {
                     matrixT[idx - 1][idx - 1] = q / matrixT[idx][idx - 1];
                     matrixT[idx - 1][idx]     = -(matrixT[idx][idx] - p) / matrixT[idx][idx - 1];
                 } else {
                     final Complex result = cdiv(0.0, -matrixT[idx - 1][idx],
                                                 matrixT[idx - 1][idx - 1] - p, q);
                     matrixT[idx - 1][idx - 1] = result.getReal();
                     matrixT[idx - 1][idx]     = result.getImaginary();
                 }
 
                 matrixT[idx][idx - 1] = 0.0;
                 matrixT[idx][idx]     = 1.0;
 
                 for (int i = idx - 2; i >= 0; i--) {
                     double ra = 0.0;
                     double sa = 0.0;
                     for (int j = l; j <= idx; j++) {
                         ra += matrixT[i][j] * matrixT[j][idx - 1];
                         sa += matrixT[i][j] * matrixT[j][idx];
                     }
                     double w = matrixT[i][i] - p;
 
                     if (Precision.compareTo(imagEigenvalues[i], 0.0, EPSILON) < 0) {
                         z = w;
                         r = ra;
                         s = sa;
                     } else {
                         l = i;
                         if (Precision.equals(imagEigenvalues[i], 0.0)) {
                             final Complex c = cdiv(-ra, -sa, w, q);
                             matrixT[i][idx - 1] = c.getReal();
                             matrixT[i][idx] = c.getImaginary();
                         } else {
                             // Solve complex equations
                             double x = matrixT[i][i + 1];
                             double y = matrixT[i + 1][i];
                             double vr = (realEigenvalues[i] - p) * (realEigenvalues[i] - p) +
                                         imagEigenvalues[i] * imagEigenvalues[i] - q * q;
                             final double vi = (realEigenvalues[i] - p) * 2.0 * q;
                             if (Precision.equals(vr, 0.0) && Precision.equals(vi, 0.0)) {
                                 vr = Precision.EPSILON * norm *
                                      (JdkMath.abs(w) + JdkMath.abs(q) + JdkMath.abs(x) +
                                       JdkMath.abs(y) + JdkMath.abs(z));
                             }
                             final Complex c     = cdiv(x * r - z * ra + q * sa,
                                                        x * s - z * sa - q * ra, vr, vi);
                             matrixT[i][idx - 1] = c.getReal();
                             matrixT[i][idx]     = c.getImaginary();
 
                             if (JdkMath.abs(x) > (JdkMath.abs(z) + JdkMath.abs(q))) {
                                 matrixT[i + 1][idx - 1] = (-ra - w * matrixT[i][idx - 1] +
                                                            q * matrixT[i][idx]) / x;
                                 matrixT[i + 1][idx]     = (-sa - w * matrixT[i][idx] -
                                                            q * matrixT[i][idx - 1]) / x;
                             } else {
                                 final Complex c2        = cdiv(-r - y * matrixT[i][idx - 1],
                                                                -s - y * matrixT[i][idx], z, q);
                                 matrixT[i + 1][idx - 1] = c2.getReal();
                                 matrixT[i + 1][idx]     = c2.getImaginary();
                             }
                         }
 
                         // Overflow control
                         double t = JdkMath.max(JdkMath.abs(matrixT[i][idx - 1]),
                                                 JdkMath.abs(matrixT[i][idx]));
                         if ((Precision.EPSILON * t) * t > 1) {
                             for (int j = i; j <= idx; j++) {
                                 matrixT[j][idx - 1] /= t;
                                 matrixT[j][idx] /= t;
                             }
                         }
                     }
                 }
             }
         }
 
         // Back transformation to get eigenvectors of original matrix
         for (int j = n - 1; j >= 0; j--) {
             for (int i = 0; i <= n - 1; i++) {
                 z = 0.0;
                 for (int k = 0; k <= JdkMath.min(j, n - 1); k++) {
                     z += matrixP[i][k] * matrixT[k][j];
                 }
                 matrixP[i][j] = z;
             }
         }
 
         eigenvectors = new ArrayRealVector[n];
         final double[] tmp = new double[n];
         for (int i = 0; i < n; i++) {
             for (int j = 0; j < n; j++) {
                 tmp[j] = matrixP[j][i];
             }
             eigenvectors[i] = new ArrayRealVector(tmp);
         }
     }
 }