// @(#)root/matrix:$Name:  $:$Id: TMatrixDEigen.cxx,v 1.11 2005/09/02 11:04:45 brun Exp $
// Authors: Fons Rademakers, Eddy Offermann  Dec 2003

/*************************************************************************
 * Copyright (C) 1995-2000, Rene Brun and Fons Rademakers.               *
 * All rights reserved.                                                  *
 *                                                                       *
 * For the licensing terms see $ROOTSYS/LICENSE.                         *
 * For the list of contributors see $ROOTSYS/README/CREDITS.             *
 *************************************************************************/

//////////////////////////////////////////////////////////////////////////
//                                                                      //
// TMatrixDEigen                                                        //
//                                                                      //
// Eigenvalues and eigenvectors of a real matrix.                       //
//                                                                      //
// 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, a + i*b, in 2-by-2 blocks, [a, b; -b, a].  That is, if  //
// the complex eigenvalues look like                                    //
//                                                                      //
//     u + iv     .        .          .      .    .                     //
//       .      u - iv     .          .      .    .                     //
//       .        .      a + ib       .      .    .                     //
//       .        .        .        a - ib   .    .                     //
//       .        .        .          .      x    .                     //
//       .        .        .          .      .    y                     //
//                                                                      //
// then D looks like                                                    //
//                                                                      //
//       u        v        .          .      .    .                     //
//      -v        u        .          .      .    .                     //
//       .        .        a          b      .    .                     //
//       .        .       -b          a      .    .                     //
//       .        .        .          .      x    .                     //
//       .        .        .          .      .    y                     //
//                                                                      //
// This keeps V a real matrix in both symmetric and non-symmetric       //
// cases, and A*V = V*D.                                                //
//                                                                      //
//////////////////////////////////////////////////////////////////////////

#include "TMatrixDEigen.h"

ClassImp(TMatrixDEigen)

//______________________________________________________________________________
 TMatrixDEigen::TMatrixDEigen(const TMatrixD &a)
{
  Assert(a.IsValid());

  const Int_t nRows  = a.GetNrows();
  const Int_t nCols  = a.GetNcols();
  const Int_t rowLwb = a.GetRowLwb();
  const Int_t colLwb = a.GetColLwb();

  if (nRows != nCols || rowLwb != colLwb)
  {
    Error("TMatrixDEigen(TMatrixD &)","matrix should be square");
    return;
  }

  const Int_t rowUpb = rowLwb+nRows-1;
  fEigenVectors.ResizeTo(rowLwb,rowUpb,rowLwb,rowUpb);
  fEigenValuesRe.ResizeTo(rowLwb,rowUpb);
  fEigenValuesIm.ResizeTo(rowLwb,rowUpb);

  TVectorD ortho;
  Double_t work[kWorkMax];
  if (nRows > kWorkMax) ortho.ResizeTo(nRows);
  else                  ortho.Use(nRows,work);

  TMatrixD mH = a;

  // Reduce to Hessenberg form.
  MakeHessenBerg(fEigenVectors,ortho,mH);

  // Reduce Hessenberg to real Schur form.
  MakeSchurr(fEigenVectors,fEigenValuesRe,fEigenValuesIm,mH);

  // Sort eigenvalues and corresponding vectors in descending order of Re^2+Im^2
  // of the complex eigenvalues .
  Sort(fEigenVectors,fEigenValuesRe,fEigenValuesIm);
}

//______________________________________________________________________________
 TMatrixDEigen::TMatrixDEigen(const TMatrixDEigen &another)
{
  *this = another;
}

//______________________________________________________________________________
 void TMatrixDEigen::MakeHessenBerg(TMatrixD &v,TVectorD &ortho,TMatrixD &H)
{
// Nonsymmetric reduction to Hessenberg form.
// This is derived from the Algol procedures orthes and ortran, by Martin and Wilkinson,
// Handbook for Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
// Fortran subroutines in EISPACK.
   
  Double_t *pV = v.GetMatrixArray();
  Double_t *pO = ortho.GetMatrixArray();
  Double_t *pH = H.GetMatrixArray();

  const Int_t n = v.GetNrows();

  const Int_t low  = 0;
  const Int_t high = n-1;
   
  Int_t i,j,m;
  for (m = low+1; m <= high-1; m++) {
    const Int_t off_m = m*n;
   
    // Scale column.
   
    Double_t scale = 0.0;
    for (i = m; i <= high; i++) {
      const Int_t off_i = i*n;
      scale = scale + TMath::Abs(pH[off_i+m-1]);
    }
    if (scale != 0.0) {
   
      // Compute Householder transformation.
   
      Double_t h = 0.0;
      for (i = high; i >= m; i--) {
        const Int_t off_i = i*n;
        pO[i] = pH[off_i+m-1]/scale;
        h += pO[i]*pO[i];
      }
      Double_t g = TMath::Sqrt(h);
      if (pO[m] > 0)
        g = -g;
      h = h-pO[m]*g;
      pO[m] = pO[m]-g;
   
      // Apply Householder similarity transformation
      // H = (I-u*u'/h)*H*(I-u*u')/h)
   
      for (j = m; j < n; j++) {
        Double_t f = 0.0;
        for (i = high; i >= m; i--) {
          const Int_t off_i = i*n;
          f += pO[i]*pH[off_i+j];
        }
        f = f/h;
        for (i = m; i <= high; i++) {
          const Int_t off_i = i*n;
          pH[off_i+j] -= f*pO[i];
        }
      }
   
      for (i = 0; i <= high; i++) {
        const Int_t off_i = i*n;
        Double_t f = 0.0;
        for (j = high; j >= m; j--) {
          const Int_t off_i = i*n;
          f += pO[j]*pH[off_i+j];
        }
        f = f/h;
        for (j = m; j <= high; j++)
          pH[off_i+j] -= f*pO[j];
      }
      pO[m] = scale*pO[m];
      pH[off_m+m-1] = scale*g;
    }
  }
   
  // Accumulate transformations (Algol's ortran).

  for (i = 0; i < n; i++) {
    const Int_t off_i = i*n;
    for (j = 0; j < n; j++)
      pV[off_i+j] = (i == j ? 1.0 : 0.0);
  }

  for (m = high-1; m >= low+1; m--) {
    const Int_t off_m = m*n;
    if (pH[off_m+m-1] != 0.0) {
      for (i = m+1; i <= high; i++) {
        const Int_t off_i = i*n;
        pO[i] = pH[off_i+m-1];
      }
      for (j = m; j <= high; j++) {
        Double_t g = 0.0;
        for (i = m; i <= high; i++) {
          const Int_t off_i = i*n;
          g += pO[i]*pV[off_i+j];
        }
        // Double division avoids possible underflow
        g = (g/pO[m])/pH[off_m+m-1];
        for (i = m; i <= high; i++) {
          const Int_t off_i = i*n;
          pV[off_i+j] += g*pO[i];
        }
      }
    }
  }
}

//______________________________________________________________________________
static Double_t gCdivr, gCdivi;
static void cdiv(Double_t xr,Double_t xi,Double_t yr,Double_t yi) {
// Complex scalar division.
  Double_t r,d;
  if (TMath::Abs(yr) > TMath::Abs(yi)) {
    r = yi/yr;
    d = yr+r*yi;
    gCdivr = (xr+r*xi)/d;
    gCdivi = (xi-r*xr)/d;
  } else {
    r = yr/yi;
    d = yi+r*yr;
    gCdivr = (r*xr+xi)/d;
    gCdivi = (r*xi-xr)/d; 
  }
}

//______________________________________________________________________________
 void TMatrixDEigen::MakeSchurr(TMatrixD &v,TVectorD &d,TVectorD &e,TMatrixD &H)
{
// Nonsymmetric reduction from Hessenberg to real Schur form.
// This is derived from the Algol procedure hqr2, by Martin and Wilkinson,
// Handbook for Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
// Fortran subroutine in EISPACK.
   
  // Initialize
   
  const Int_t nn = v.GetNrows();
        Int_t n = nn-1;
  const Int_t low = 0;
  const Int_t high = nn-1;
  const Double_t eps = TMath::Power(2.0,-52.0);
  Double_t exshift = 0.0;
  Double_t p=0,q=0,r=0,s=0,z=0,t,w,x,y;

  Double_t *pV = v.GetMatrixArray();
  Double_t *pD = d.GetMatrixArray();
  Double_t *pE = e.GetMatrixArray();
  Double_t *pH = H.GetMatrixArray();
   
  // Store roots isolated by balanc and compute matrix norm
   
  Double_t norm = 0.0;
  Int_t i,j,k;
  for (i = 0; i < nn; i++) {
    const Int_t off_i = i*nn;
    if ((i < low) || (i > high)) {
      pD[i] = pH[off_i+i];
      pE[i] = 0.0;
    }
    for (j = TMath::Max(i-1,0); j < nn; j++)
      norm += TMath::Abs(pH[off_i+j]);
  }
   
  // Outer loop over eigenvalue index
   
  Int_t iter = 0;
  while (n >= low) {
    const Int_t off_n  = n*nn;
    const Int_t off_n1 = (n-1)*nn;
   
    // Look for single small sub-diagonal element
   
    Int_t l = n;
    while (l > low) {
      const Int_t off_l1 = (l-1)*nn;
      const Int_t off_l  = l*nn;
      s = TMath::Abs(pH[off_l1+l-1])+TMath::Abs(pH[off_l+l]);
      if (s == 0.0)
        s = norm;
      if (TMath::Abs(pH[off_l+l-1]) < eps*s)
        break;
      l--;
    }
       
    // Check for convergence
    // One root found
   
    if (l == n) {
      pH[off_n+n] = pH[off_n+n]+exshift;
      pD[n] = pH[off_n+n];
      pE[n] = 0.0;
      n--;
      iter = 0;
   
      // Two roots found
   
    } else if (l == n-1) {
      w = pH[off_n+n-1]*pH[off_n1+n];
      p = (pH[off_n1+n-1]-pH[off_n+n])/2.0;
      q = p*p+w;
      z = TMath::Sqrt(TMath::Abs(q));
      pH[off_n+n] = pH[off_n+n]+exshift;
      pH[off_n1+n-1] = pH[off_n1+n-1]+exshift;
      x = pH[off_n+n];
   
      // Double_t pair
   
      if (q >= 0) {
        if (p >= 0)
          z = p+z;
        else
          z = p-z;
        pD[n-1] = x+z;
        pD[n] = pD[n-1];
        if (z != 0.0)
          pD[n] = x-w/z;
        pE[n-1] = 0.0;
        pE[n] = 0.0;
        x = pH[off_n+n-1];
        s = TMath::Abs(x)+TMath::Abs(z);
        p = x/s;
        q = z/s;
        r = TMath::Sqrt((p*p)+(q*q));
        p = p/r;
        q = q/r;
 
        // Row modification

        for (j = n-1; j < nn; j++) {
          z = pH[off_n1+j];
          pH[off_n1+j] = q*z+p*pH[off_n+j];
          pH[off_n+j]  = q*pH[off_n+j]-p*z;
        }

        // Column modification

        for (i = 0; i <= n; i++) {
          const Int_t off_i = i*nn;
          z = pH[off_i+n-1];
          pH[off_i+n-1] = q*z+p*pH[off_i+n];
          pH[off_i+n]  = q*pH[off_i+n]-p*z;
        }
   
        // Accumulate transformations

        for (i = low; i <= high; i++) {
          const Int_t off_i = i*nn;
          z = pV[off_i+n-1];
          pV[off_i+n-1] = q*z+p*pV[off_i+n];
          pV[off_i+n]   = q*pV[off_i+n]-p*z;
        }

        // Complex pair

      } else {
        pD[n-1] = x+p;
        pD[n] = x+p;
        pE[n-1] = z;
        pE[n] = -z;
      }
      n = n-2;
      iter = 0;

      // No convergence yet
   
    } else {
   
      // Form shift
   
      x = pH[off_n+n];
      y = 0.0;
      w = 0.0;
      if (l < n) {
        y = pH[off_n1+n-1];
        w = pH[off_n+n-1]*pH[off_n1+n];
      }
  
      // Wilkinson's original ad hoc shift
   
      if (iter == 10) {
        exshift += x;
        for (i = low; i <= n; i++) {
          const Int_t off_i = i*nn;
          pH[off_i+i] -= x;
        }
        s = TMath::Abs(pH[off_n+n-1])+TMath::Abs(pH[off_n1+n-2]);
        x = y = 0.75*s;
        w = -0.4375*s*s;
      }

      // MATLAB's new ad hoc shift

      if (iter == 30) {
         s = (y-x)/2.0;
         s = s*s+w;
         if (s > 0) {
           s = TMath::Sqrt(s);
           if (y<x)
             s = -s;
           s = x-w/((y-x)/2.0+s);
           for (i = low; i <= n; i++) {
             const Int_t off_i = i*nn;
             pH[off_i+i] -= s;
           }
           exshift += s;
           x = y = w = 0.964;
         }
      }
   
      if (iter++ == 50) {  // (check iteration count here.)
        Error("MakeSchurr","too many iterations");
        break;
      } 
   
      // Look for two consecutive small sub-diagonal elements
   
      Int_t m = n-2;
      while (m >= l) {
        const Int_t off_m   = m*nn;
        const Int_t off_m_1 = (m-1)*nn;
        const Int_t off_m1  = (m+1)*nn;
        const Int_t off_m2  = (m+2)*nn;
        z = pH[off_m+m];
        r = x-z;
        s = y-z;
        p = (r*s-w)/pH[off_m1+m]+pH[off_m+m+1];
        q = pH[off_m1+m+1]-z-r-s;
        r = pH[off_m2+m+1];
        s = TMath::Abs(p)+TMath::Abs(q)+TMath::Abs(r);
        p = p /s;
        q = q/s;
        r = r/s;
        if (m == l)
          break;
        if (TMath::Abs(pH[off_m+m-1])*(TMath::Abs(q)+TMath::Abs(r)) <
          eps*(TMath::Abs(p)*(TMath::Abs(pH[off_m_1+m-1])+TMath::Abs(z)+
          TMath::Abs(pH[off_m1+m+1]))))
            break;
        m--;
      }
   
      for (i = m+2; i <= n; i++) {
        const Int_t off_i = i*nn;
        pH[off_i+i-2] = 0.0;
        if (i > m+2)
          pH[off_i+i-3] = 0.0;
      }
   
      // Double QR step involving rows l:n and columns m:n
   
      for (k = m; k <= n-1; k++) {
        const Int_t off_k  = k*nn;
        const Int_t off_k1 = (k+1)*nn;
        const Int_t off_k2 = (k+2)*nn;
        const Int_t notlast = (k != n-1);
        if (k != m) {
          p = pH[off_k+k-1];
          q = pH[off_k1+k-1];
          r = (notlast ? pH[off_k2+k-1] : 0.0);
          x = TMath::Abs(p)+TMath::Abs(q)+TMath::Abs(r);
          if (x != 0.0) {
            p = p/x;
            q = q/x;
            r = r/x;
          }
       }
       if (x == 0.0)
         break;
       s = TMath::Sqrt(p*p+q*q+r*r);
       if (p < 0) {
         s = -s;
       }
       if (s != 0) {
         if (k != m)
           pH[off_k+k-1] = -s*x;
          else if (l != m)
            pH[off_k+k-1] = -pH[off_k+k-1];
          p = p+s;
          x = p/s;
          y = q/s;
          z = r/s;
          q = q/p;
          r = r/p;
   
          // Row modification
   
          for (j = k; j < nn; j++) {
            p = pH[off_k+j]+q*pH[off_k1+j];
            if (notlast) {
              p = p+r*pH[off_k2+j];
              pH[off_k2+j] = pH[off_k2+j]-p*z;
            }
            pH[off_k+j]  = pH[off_k+j]-p*x;
            pH[off_k1+j] = pH[off_k1+j]-p*y;
          }
  
          // Column modification
 
          for (i = 0; i <= TMath::Min(n,k+3); i++) {
            const Int_t off_i = i*nn;
            p = x*pH[off_i+k]+y*pH[off_i+k+1];
            if (notlast) {
              p = p+z*pH[off_i+k+2];
              pH[off_i+k+2] = pH[off_i+k+2]-p*r;
            }
            pH[off_i+k]   = pH[off_i+k]-p;
            pH[off_i+k+1] = pH[off_i+k+1]-p*q;
          }

          // Accumulate transformations

          for (i = low; i <= high; i++) {
            const Int_t off_i = i*nn;
            p = x*pV[off_i+k]+y*pV[off_i+k+1];
            if (notlast) {
              p = p+z*pV[off_i+k+2];
              pV[off_i+k+2] = pV[off_i+k+2]-p*r;
            }
            pV[off_i+k]   = pV[off_i+k]-p;
            pV[off_i+k+1] = pV[off_i+k+1]-p*q;
          }
        }  // (s != 0)
      }  // k loop
    }  // check convergence
  }  // while (n >= low)
      
  // Backsubstitute to find vectors of upper triangular form

  if (norm == 0.0)
    return;

  for (n = nn-1; n >= 0; n--) {
    p = pD[n];
    q = pE[n];
   
    // Double_t vector
   
    const Int_t off_n = n*nn;
    if (q == 0) {
      Int_t l = n;
      pH[off_n+n] = 1.0;
      for (i = n-1; i >= 0; i--) {
        const Int_t off_i  = i*nn;
        const Int_t off_i1 = (i+1)*nn;
        w = pH[off_i+i]-p;
        r = 0.0;
        for (j = l; j <= n; j++) {
          const Int_t off_j = j*nn;
          r = r+pH[off_i+j]*pH[off_j+n];
        }
        if (pE[i] < 0.0) {
          z = w;
          s = r;
        } else {
          l = i;
          if (pE[i] == 0.0) {
            if (w != 0.0)
              pH[off_i+n] = -r/w;
            else
              pH[off_i+n] = -r/(eps*norm);

            // Solve real equations

            } else {
              x = pH[off_i+i+1];
              y = pH[off_i1+i];
              q = (pD[i]-p)*(pD[i]-p)+pE[i]*pE[i];
              t = (x*s-z*r)/q;
              pH[off_i+n] = t;
              if (TMath::Abs(x) > TMath::Abs(z))
                pH[i+1+n] = (-r-w*t)/x;
              else
                pH[i+1+n] = (-s-y*t)/z;
            }
   
            // Overflow control

            t = TMath::Abs(pH[off_i+n]);
            if ((eps*t)*t > 1) {
              for (j = i; j <= n; j++) {
                const Int_t off_j = j*nn;
                pH[off_j+n] = pH[off_j+n]/t;
              }
            }
         }
      }
   
      // Complex vector
   
    } else if (q < 0) {
      Int_t l = n-1;
      const Int_t off_n1 = (n-1)*nn;

      // Last vector component imaginary so matrix is triangular
   
      if (TMath::Abs(pH[off_n+n-1]) > TMath::Abs(pH[off_n1+n])) {
        pH[off_n1+n-1] = q/pH[off_n+n-1];
        pH[off_n1+n]   = -(pH[off_n+n]-p)/pH[off_n+n-1];
      } else {
        cdiv(0.0,-pH[off_n1+n],pH[off_n1+n-1]-p,q);
        pH[off_n1+n-1] = gCdivr;
        pH[off_n1+n]   = gCdivi;
      }
      pH[off_n+n-1] = 0.0;
      pH[off_n+n]   = 1.0;
      for (i = n-2; i >= 0; i--) {
        const Int_t off_i  = i*nn;
        const Int_t off_i1 = (i+1)*nn;
        Double_t ra = 0.0;
        Double_t sa = 0.0;
        for (j = l; j <= n; j++) {
          const Int_t off_j = j*nn;
          ra += pH[off_i+j]*pH[off_j+n-1];
          sa += pH[off_i+j]*pH[off_j+n];
        }
        w = pH[off_i+i]-p;
   
        if (pE[i] < 0.0) {
          z = w;
          r = ra;
          s = sa;
        } else {
          l = i;
          if (pE[i] == 0) {
            cdiv(-ra,-sa,w,q);
            pH[off_i+n-1] = gCdivr;
            pH[off_i+n]   = gCdivi;
          } else {

            // Solve complex equations 

            x = pH[off_i+i+1];
            y = pH[off_i1+i];
            Double_t vr = (pD[i]-p)*(pD[i]-p)+pE[i]*pE[i]-q*q;
            Double_t vi = (pD[i]-p)*2.0*q;
            if ((vr == 0.0) && (vi == 0.0)) {
              vr = eps*norm*(TMath::Abs(w)+TMath::Abs(q)+
                   TMath::Abs(x)+TMath::Abs(y)+TMath::Abs(z));
            }
            cdiv(x*r-z*ra+q*sa,x*s-z*sa-q*ra,vr,vi);
            pH[off_i+n-1] = gCdivr;
            pH[off_i+n]   = gCdivi;
            if (TMath::Abs(x) > (TMath::Abs(z)+TMath::Abs(q))) {
              pH[off_i1+n-1] = (-ra-w*pH[off_i+n-1]+q*pH[off_i+n])/x;
              pH[off_i1+n]   = (-sa-w*pH[off_i+n]-q*pH[off_i+n-1])/x;
            } else {
              cdiv(-r-y*pH[off_i+n-1],-s-y*pH[off_i+n],z,q);
              pH[off_i1+n-1] = gCdivr;
              pH[off_i1+n]   = gCdivi;
            }
          }
   
          // Overflow control

          t = TMath::Max(TMath::Abs(pH[off_i+n-1]),TMath::Abs(pH[off_i+n]));
          if ((eps*t)*t > 1) {
            for (j = i; j <= n; j++) {
              const Int_t off_j = j*nn;
              pH[off_j+n-1] = pH[off_j+n-1]/t;
              pH[off_j+n]   = pH[off_j+n]/t;
            }
          }
        }
      }
    }
  }
   
  // Vectors of isolated roots
   
  for (i = 0; i < nn; i++) {
    if (i < low || i > high) {
      const Int_t off_i = i*nn;
      for (Int_t j = i; j < nn; j++)
        pV[off_i+j] = pH[off_i+j];
    }
  }
   
  // Back transformation to get eigenvectors of original matrix
   
  for (j = nn-1; j >= low; j--) {
    for (i = low; i <= high; i++) {
      const Int_t off_i = i*nn;
      z = 0.0;
      for (k = low; k <= TMath::Min(j,high); k++) {
        const Int_t off_k = k*nn;
        z = z+pV[off_i+k]*pH[off_k+j];
      }
      pV[off_i+j] = z;
    }
  }

}

//______________________________________________________________________________
 void TMatrixDEigen::Sort(TMatrixD &v,TVectorD &d,TVectorD &e)
{
// Sort eigenvalues and corresponding vectors in descending order of Re^2+Im^2
// of the complex eigenvalues .

  // Sort eigenvalues and corresponding vectors.
  Double_t *pV = v.GetMatrixArray();
  Double_t *pD = d.GetMatrixArray();
  Double_t *pE = e.GetMatrixArray();

  const Int_t n = v.GetNrows();

  for (Int_t i = 0; i < n-1; i++) {
    Int_t k = i;
    Double_t norm = pD[i]*pD[i]+pE[i]*pE[i];
    Int_t j;
    for (j = i+1; j < n; j++) {
      const Double_t norm_new = pD[j]*pD[j]+pE[j]*pE[j];
      if (norm_new > norm) { 
        k = j;  
        norm = norm_new;
      }
    }
    if (k != i) { 
      Double_t tmp;
      tmp   = pD[k];
      pD[k] = pD[i];
      pD[i] = tmp;
      tmp   = pE[k];
      pE[k] = pE[i];
      pE[i] = tmp;
      for (j = 0; j < n; j++) {
        const Int_t off_j = j*n; 
        tmp = pV[off_j+i];
        pV[off_j+i] = pV[off_j+k];
        pV[off_j+k] = tmp;
      }
    }
  }
}

//______________________________________________________________________________
TMatrixDEigen &TMatrixDEigen::operator=(const TMatrixDEigen &source)
{
  if (this != &source) {
    fEigenVectors.ResizeTo(source.fEigenVectors);
    fEigenValuesRe.ResizeTo(source.fEigenValuesRe);
    fEigenValuesIm.ResizeTo(source.fEigenValuesIm);
  }
  return *this;
}

//______________________________________________________________________________
 const TMatrixD TMatrixDEigen::GetEigenValues() const
{
// Computes the block diagonal eigenvalue matrix.
// If the original matrix 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,
//    a + i*b, in 2-by-2 blocks, [a, b; -b, a].
//  That is, if the complex eigenvalues look like
//
//     u + iv     .        .          .      .    .
//       .      u - iv     .          .      .    .
//       .        .      a + ib       .      .    .
//       .        .        .        a - ib   .    .
//       .        .        .          .      x    .
//       .        .        .          .      .    y
//
// then D looks like
//
//     u        v        .          .      .    .
//    -v        u        .          .      .    . 
//     .        .        a          b      .    .
//     .        .       -b          a      .    .
//     .        .        .          .      x    .
//     .        .        .          .      .    y
//
// This keeps V a real matrix in both symmetric and non-symmetric
// cases, and A*V = V*D.
//
// Indexing:
//  If matrix A has the index/shape (rowLwb,rowUpb,rowLwb,rowUpb)
//  each eigen-vector must have the shape (rowLwb,rowUpb) .
//  For convinience, the column index of the eigen-vector matrix
//  also runs from rowLwb to rowUpb so that the returned matrix
//  has also index/shape (rowLwb,rowUpb,rowLwb,rowUpb) .
//
  const Int_t nrows  = fEigenVectors.GetNrows();
  const Int_t rowLwb = fEigenVectors.GetRowLwb();
  const Int_t rowUpb = rowLwb+nrows-1;

  TMatrixD mD(rowLwb,rowUpb,rowLwb,rowUpb);

  Double_t *pD = mD.GetMatrixArray();
  const Double_t * const pd = fEigenValuesRe.GetMatrixArray();
  const Double_t * const pe = fEigenValuesIm.GetMatrixArray();

  for (Int_t i = 0; i < nrows; i++) {
    const Int_t off_i = i*nrows;
    for (Int_t j = 0; j < nrows; j++)
      pD[off_i+j] = 0.0;
    pD[off_i+i] = pd[i];
    if (pe[i] > 0) {
      pD[off_i+i+1] = pe[i];
    } else if (pe[i] < 0) {
      pD[off_i+i-1] = pe[i];
    }
  }

  return mD;
}