TUnfoldV17.cxx

Go to the documentation of this file.

// Author: Stefan Schmitt
// DESY, 13/10/08

// Version 17.6, updated doxygen-style comments, add one argument for scanLCurve
//
//  History:
//    Version 17.5, fix memory leak with fVyyInv, bugs in GetInputInverseEmatrix(), GetInput(), bug in MultiplyMSparseMSparseTranspVector
//    Version 17.4, in parallel to changes in TUnfoldBinning
//    Version 17.3, in parallel to changes in TUnfoldBinning
//    Version 17.2, bug fix with GetProbabilityMatrix
//    Version 17.1, bug fixes in GetFoldedOutput, GetOutput
//    Version 17.0, option to specify an error matrix with SetInput(), new ScanRho() method
//    Version 16.2, in parallel to bug-fix in TUnfoldSys
//    Version 16.1, fix bug with error matrix in case kEConstraintArea is used
//    Version 16.0, fix calculation of global correlations, improved error messages
//    Version 15, simplified L-curve scan, new tau definition, new error calc., area preservation
//    Version 14, with changes in TUnfoldSys.cxx
//    Version 13, new methods for derived classes and small bug fix
//    Version 12, report singular matrices
//    Version 11, reduce the amount of printout
//    Version 10, more correct definition of the L curve, update references
//    Version 9, faster matrix inversion and skip edge points for L-curve scan
//    Version 8, replace all TMatrixSparse matrix operations by private code
//    Version 7, fix problem with TMatrixDSparse,TMatrixD multiplication
//    Version 6, replace class XY by std::pair
//    Version 5, replace run-time dynamic arrays by new and delete[]
//    Version 4, fix new bug from V3 with initial regularisation condition
//    Version 3, fix bug with initial regularisation condition
//    Version 2, with improved ScanLcurve() algorithm
//    Version 1, added ScanLcurve() method
//    Version 0, stable version of basic unfolding algorithm


/** \class TUnfoldV17
An algorithm to unfold distributions from detector to truth level

TUnfold is used to decompose a measurement y into several sources x, 
given the measurement uncertainties and a matrix of migrations A.
The method can be applied to a large number of problems,
where the measured distribution y is a linear superposition
of several Monte Carlo shapes. Beyond such a simple template fit,
TUnfold has an adjustable regularisation term and also supports an
optional constraint on the total number of events.

<b>For most applications, it is better to use the derived class
TUnfoldDensity instead of TUnfold. TUnfoldDensity adds various
features to TUnfold, such as: 
background subtraction, propagation of systematic uncertainties,
complex multidimensional arrangements of the bins. For innocent
users, the most notable improvement of TUnfoldDensity over TUnfold are
the getter functions. For TUnfold, histograms have to be booked by the
user and the getter functions fill the histogram bins. TUnfoldDensity
simply returns a new, already filled histogram.</b>

If you use this software, please consider the following citation
<br/>
<b>S.Schmitt, JINST 7 (2012) T10003 [arXiv:1205.6201]</b>
<br/>
Detailed documentation and updates are available on 
http://desy.de/~sschmitt

Brief recipy to use TUnfold:
<ul>
<li>a matrix (truth,reconstructed) is given as a two-dimensional histogram
  as argument to the constructor of TUnfold</li>
<li>a vector of measurements is given as one-dimensional histogram using
  the SetInput() method</li>
<li>The unfolding is performed
<ul>
<li>either once with a fixed parameter tau, method DoUnfold(tau)</li>
<li>or multiple times in a scan to determine the best chouce of tau,
     method ScanLCurve()</li>
</ul>
<li>Unfolding results are retrieved using various GetXXX() methods
</ul>

Basic formulae:<br/>
&chi;<sup>2</sup><sub>A</sub>=(Ax-y)<sup>T</sup>V<sub>yy</sub><sup>-1</sup>(Ax-y)<br/>
&chi;<sup>2</sup><sub>L</sub>=(x-f*x<sub>0</sub>)<sup>T</sup>L<sup>T</sup>L(x-f*x<sub>0</sub>)<br/>
&chi;<sup>2</sup><sub>unf</sub>=&chi;<sup>2</sup><sub>A</sub>+&tau;<sup>2</sup>&chi;<sup>2</sup><sub>L</sub>+&lambda;&Sigma;<sub>i</sub>(Ax-y)<sub>i</sub><br/>
x:result, A:probabilities, y:data, V<sub>yy</sub>:data
covariance, f:bias scale, x<sub>0</sub>:bias, L:regularisation conditions,
&tau;:regularisation strength, &lambda;:Lagrangian multiplier<br/>
Without area constraint, &lambda; is set to zero, and
&chi;<sup>2</sup><sub>unf</sub> is minimized to determine x. With area
constraint, both x and &lambda; are determined.
*/


/*
  This file is part of TUnfold.

  TUnfold is free software: you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation, either version 3 of the License, or
  (at your option) any later version.

  TUnfold is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  GNU General Public License for more details.

  You should have received a copy of the GNU General Public License
  along with TUnfold.  If not, see <http://www.gnu.org/licenses/>.
*/

#include <iostream>
#include <TMatrixD.h>
#include <TMatrixDSparse.h>
#include <TMatrixDSym.h>
#include <TMatrixDSymEigen.h>
#include <TMath.h>
#include "TUnfold.h"

#include <map>
#include <vector>

//#define DEBUG
//#define DEBUG_DETAIL
//#define FORCE_EIGENVALUE_DECOMPOSITION

ClassImp(TUnfoldV17)

TUnfoldV17::~TUnfoldV17(void)
{
   // delete all data members

   DeleteMatrix(&fA);
   DeleteMatrix(&fL);
   DeleteMatrix(&fVyy);
   DeleteMatrix(&fY);
   DeleteMatrix(&fX0);
   DeleteMatrix(&fVyyInv);

   ClearResults();
}


////////////////////////////////////////////////////////////////////////
/// initialize data menbers, for use in constructors
void TUnfoldV17::InitTUnfold(void)
{
   // reset all data members
   fXToHist.Set(0);
   fHistToX.Set(0);
   fSumOverY.Set(0);
   fA = 0;
   fL = 0;
   fVyy = 0;
   fY = 0;
   fX0 = 0;
   fTauSquared = 0.0;
   fBiasScale = 0.0;
   fConstraint = kEConstraintNone;
   fRegMode = kRegModeNone;
   // output
   fX = 0;
   fVyyInv = 0;
   fVxx = 0;
   fVxxInv = 0;
   fAx = 0;
   fChi2A = 0.0;
   fLXsquared = 0.0;
   fRhoMax = 999.0;
   fRhoAvg = -1.0;
   fNdf = 0;
   fDXDAM[0] = 0;
   fDXDAZ[0] = 0;
   fDXDAM[1] = 0;
   fDXDAZ[1] = 0;
   fDXDtauSquared = 0;
   fDXDY = 0;
   fEinv = 0;
   fE = 0;
   fEpsMatrix=1.E-13;
   fIgnoredBins=0;
}

////////////////////////////////////////////////////////////////////////
/// delete matrix and invalidate pointer
///
/// \param[inout] m pointer to a matrix-pointer
///
/// if the matrix pointer os non-zero, thematrix id deleted. The matrox pointer is set to zero.
void TUnfoldV17::DeleteMatrix(TMatrixD **m)
{
   if(*m) delete *m;
   *m=0;
}

////////////////////////////////////////////////////////////////////////
/// delete sparse matrix and invalidate pointer
///
/// \param[inout] m pointer to a matrix-pointer
///
/// if the matrix pointer os non-zero, thematrix id deleted. The matrox pointer is set to zero.
void TUnfoldV17::DeleteMatrix(TMatrixDSparse **m)
{
   if(*m) delete *m;
   *m=0;
}

////////////////////////////////////////////////////////////////////////
/// reset all results
void TUnfoldV17::ClearResults(void)
{
   // delete old results (if any)
   // this function is virtual, so derived classes may implement their own
   // method to flag results as non-valid

   // note: the inverse of the input covariance is not cleared
   //       because it does not change until the input is changed

   DeleteMatrix(&fVxx);
   DeleteMatrix(&fX);
   DeleteMatrix(&fAx);
   for(Int_t i=0;i<2;i++) {
      DeleteMatrix(fDXDAM+i);
      DeleteMatrix(fDXDAZ+i);
   }
   DeleteMatrix(&fDXDtauSquared);
   DeleteMatrix(&fDXDY);
   DeleteMatrix(&fEinv);
   DeleteMatrix(&fE);
   DeleteMatrix(&fVxxInv);
   fChi2A = 0.0;
   fLXsquared = 0.0;
   fRhoMax = 999.0;
   fRhoAvg = -1.0;
}

////////////////////////////////////////////////////////////////////////
/// only for use by root streamer or derived classes
///
TUnfoldV17::TUnfoldV17(void)
{
   // set all matrix pointers to zero
   InitTUnfold();
}

////////////////////////////////////////////////////////////////////////
/// core unfolding algorithm
Double_t TUnfoldV17::DoUnfold(void)
{
   // main unfolding algorithm. Declared virtual, because other algorithms
   // could be implemented
   //
   // Purpose: unfold y -> x
   // Data members required:
   //     fA:  matrix to relate x and y
   //     fY:  measured data points
   //     fX0: bias on x
   //     fBiasScale: scale factor for fX0
   //     fVyy:  covariance matrix for y
   //     fL: regularisation conditions
   //     fTauSquared: regularisation strength
   //     fConstraint: whether the constraint is applied
   // Data members modified:
   //     fVyyInv: inverse of input data covariance matrix
   //     fNdf: number of degrees of freedom
   //     fEinv: inverse of the matrix needed for unfolding calculations
   //     fE:    the matrix needed for unfolding calculations
   //     fX:    unfolded data points
   //     fDXDY: derivative of x wrt y (for error propagation)
   //     fVxx:  error matrix (covariance matrix) on x
   //     fAx:   estimate of distribution y from unfolded data
   //     fChi2A:  contribution to chi**2 from y-Ax
   //     fChi2L:  contribution to chi**2 from L*(x-x0)
   //     fDXDtauSquared: derivative of x wrt tau
   //     fDXDAM[0,1]: matrix parts of derivative x wrt A
   //     fDXDAZ[0,1]: vector parts of derivative x wrt A
   //     fRhoMax: maximum global correlation coefficient
   //     fRhoAvg: average global correlation coefficient
   // return code:
   //     fRhoMax   if(fRhoMax>=1.0) then the unfolding has failed!

   ClearResults();

   // get pseudo-inverse matrix Vyyinv and NDF
   if(!fVyyInv) {
      GetInputInverseEmatrix(0);
      if(fConstraint != kEConstraintNone) {
   fNdf--;
      }
   }
   //
   // get matrix
   //              T
   //            fA fV  = mAt_V
   //
   TMatrixDSparse *AtVyyinv=MultiplyMSparseTranspMSparse(fA,fVyyInv);
   //
   // get
   //       T
   //     fA fVyyinv fY + fTauSquared fBiasScale Lsquared fX0 = rhs
   //
   TMatrixDSparse *rhs=MultiplyMSparseM(AtVyyinv,fY);
   TMatrixDSparse *lSquared=MultiplyMSparseTranspMSparse(fL,fL);
   if (fBiasScale != 0.0) {
     TMatrixDSparse *rhs2=MultiplyMSparseM(lSquared,fX0);
      AddMSparse(rhs, fTauSquared * fBiasScale ,rhs2);
      DeleteMatrix(&rhs2);
   }

   //
   // get matrix
   //              T
   //           (fA fV)fA + fTauSquared*fLsquared  = fEinv
   fEinv=MultiplyMSparseMSparse(AtVyyinv,fA);
   AddMSparse(fEinv,fTauSquared,lSquared);

   //
   // get matrix
   //             -1
   //        fEinv    = fE
   //
   Int_t rank=0;
   fE = InvertMSparseSymmPos(fEinv,&rank);
   if(rank != GetNx()) {
      Warning("DoUnfold","rank of matrix E %d expect %d",rank,GetNx());
   }

   //
   // get result
   //        fE rhs  = x
   //
   TMatrixDSparse *xSparse=MultiplyMSparseMSparse(fE,rhs);
   fX = new TMatrixD(*xSparse);
   DeleteMatrix(&rhs);
   DeleteMatrix(&xSparse);

   // additional correction for constraint
   Double_t lambda_half=0.0;
   Double_t one_over_epsEeps=0.0;
   TMatrixDSparse *epsilon=0;
   TMatrixDSparse *Eepsilon=0;
   if(fConstraint != kEConstraintNone) {
      // calculate epsilon: verctor of efficiencies
      const Int_t *A_rows=fA->GetRowIndexArray();
      const Int_t *A_cols=fA->GetColIndexArray();
      const Double_t *A_data=fA->GetMatrixArray();
      TMatrixD epsilonNosparse(fA->GetNcols(),1);
      for(Int_t i=0;i<A_rows[fA->GetNrows()];i++) {
         epsilonNosparse(A_cols[i],0) += A_data[i];
      }
      epsilon=new TMatrixDSparse(epsilonNosparse);
      // calculate vector EE*epsilon
      Eepsilon=MultiplyMSparseMSparse(fE,epsilon);
      // calculate scalar product epsilon#*Eepsilon
      TMatrixDSparse *epsilonEepsilon=MultiplyMSparseTranspMSparse(epsilon,
                                                                   Eepsilon);
      // if epsilonEepsilon is zero, nothing works...
      if(epsilonEepsilon->GetRowIndexArray()[1]==1) {
         one_over_epsEeps=1./epsilonEepsilon->GetMatrixArray()[0];
      } else {
         Fatal("Unfold","epsilon#Eepsilon has dimension %d != 1",
               epsilonEepsilon->GetRowIndexArray()[1]);
      }
      DeleteMatrix(&epsilonEepsilon);
      // calculate sum(Y)
      Double_t y_minus_epsx=0.0;
      for(Int_t iy=0;iy<fY->GetNrows();iy++) {
         y_minus_epsx += (*fY)(iy,0);
      }
      // calculate sum(Y)-epsilon#*X
      for(Int_t ix=0;ix<epsilonNosparse.GetNrows();ix++) {
         y_minus_epsx -=  epsilonNosparse(ix,0) * (*fX)(ix,0);
      }
      // calculate lambda_half
      lambda_half=y_minus_epsx*one_over_epsEeps;
      // calculate final vector X
      const Int_t *EEpsilon_rows=Eepsilon->GetRowIndexArray();
      const Double_t *EEpsilon_data=Eepsilon->GetMatrixArray();
      for(Int_t ix=0;ix<Eepsilon->GetNrows();ix++) {
         if(EEpsilon_rows[ix]<EEpsilon_rows[ix+1]) {
            (*fX)(ix,0) += lambda_half * EEpsilon_data[EEpsilon_rows[ix]];
         }
      }
   }
   //
   // get derivative dx/dy
   // for error propagation
   //     dx/dy = E A# Vyy^-1  ( = B )
   fDXDY = MultiplyMSparseMSparse(fE,AtVyyinv);

   // additional correction for constraint
   if(fConstraint != kEConstraintNone) {
      // transposed vector of dimension GetNy() all elements equal 1/epseEeps
      Int_t *rows=new Int_t[GetNy()];
      Int_t *cols=new Int_t[GetNy()];
      Double_t *data=new Double_t[GetNy()];
      for(Int_t i=0;i<GetNy();i++) {
         rows[i]=0;
         cols[i]=i;
         data[i]=one_over_epsEeps;
      } 
      TMatrixDSparse *temp=CreateSparseMatrix
         (1,GetNy(),GetNy(),rows,cols,data);
      delete[] data;
      delete[] rows;
      delete[] cols;
      // B# * epsilon
      TMatrixDSparse *epsilonB=MultiplyMSparseTranspMSparse(epsilon,fDXDY);
      // temp- one_over_epsEeps*Bepsilon
      AddMSparse(temp, -one_over_epsEeps, epsilonB);
      DeleteMatrix(&epsilonB);
      // correction matrix
      TMatrixDSparse *corr=MultiplyMSparseMSparse(Eepsilon,temp);
      DeleteMatrix(&temp);
      // determine new derivative
      AddMSparse(fDXDY,1.0,corr);
      DeleteMatrix(&corr);
   }

   DeleteMatrix(&AtVyyinv);

   //
   // get error matrix on x
   //   fDXDY * Vyy * fDXDY#
   TMatrixDSparse *fDXDYVyy = MultiplyMSparseMSparse(fDXDY,fVyy);
   fVxx = MultiplyMSparseMSparseTranspVector(fDXDYVyy,fDXDY,0);

   DeleteMatrix(&fDXDYVyy);

   //
   // get result
   //        fA x  =  fAx
   //
   fAx = MultiplyMSparseM(fA,fX);

   //
   // calculate chi**2 etc

   // chi**2 contribution from (y-Ax)V(y-Ax)
   TMatrixD dy(*fY, TMatrixD::kMinus, *fAx);
   TMatrixDSparse *VyyinvDy=MultiplyMSparseM(fVyyInv,&dy);

   const Int_t *VyyinvDy_rows=VyyinvDy->GetRowIndexArray();
   const Double_t *VyyinvDy_data=VyyinvDy->GetMatrixArray();
   fChi2A=0.0;
   for(Int_t iy=0;iy<VyyinvDy->GetNrows();iy++) {
      if(VyyinvDy_rows[iy]<VyyinvDy_rows[iy+1]) {
         fChi2A += VyyinvDy_data[VyyinvDy_rows[iy]]*dy(iy,0);
      }
   }
   TMatrixD dx( fBiasScale * (*fX0), TMatrixD::kMinus,(*fX));
   TMatrixDSparse *LsquaredDx=MultiplyMSparseM(lSquared,&dx);
   const Int_t *LsquaredDx_rows=LsquaredDx->GetRowIndexArray();
   const Double_t *LsquaredDx_data=LsquaredDx->GetMatrixArray();
   fLXsquared = 0.0;
   for(Int_t ix=0;ix<LsquaredDx->GetNrows();ix++) {
      if(LsquaredDx_rows[ix]<LsquaredDx_rows[ix+1]) {
         fLXsquared += LsquaredDx_data[LsquaredDx_rows[ix]]*dx(ix,0);
      }
   }

   //
   // get derivative dx/dtau
   fDXDtauSquared=MultiplyMSparseMSparse(fE,LsquaredDx);

   if(fConstraint != kEConstraintNone) {
      TMatrixDSparse *temp=MultiplyMSparseTranspMSparse(epsilon,fDXDtauSquared);
      Double_t f=0.0;
      if(temp->GetRowIndexArray()[1]==1) {
         f=temp->GetMatrixArray()[0]*one_over_epsEeps;
      } else if(temp->GetRowIndexArray()[1]>1) {
         Fatal("Unfold",
               "epsilon#fDXDtauSquared has dimension %d != 1",
               temp->GetRowIndexArray()[1]);
      }
      if(f!=0.0) {
         AddMSparse(fDXDtauSquared, -f,Eepsilon);
      }
      DeleteMatrix(&temp);
   }
   DeleteMatrix(&epsilon);

   DeleteMatrix(&LsquaredDx);
   DeleteMatrix(&lSquared);

   // calculate/store matrices defining the derivatives dx/dA
   fDXDAM[0]=new TMatrixDSparse(*fE);
   fDXDAM[1]=new TMatrixDSparse(*fDXDY); // create a copy
   fDXDAZ[0]=VyyinvDy; // instead of deleting VyyinvDy
   VyyinvDy=0;
   fDXDAZ[1]=new TMatrixDSparse(*fX); // create a copy

   if(fConstraint != kEConstraintNone) {
      // add correction to fDXDAM[0]
      TMatrixDSparse *temp1=MultiplyMSparseMSparseTranspVector
         (Eepsilon,Eepsilon,0);
      AddMSparse(fDXDAM[0], -one_over_epsEeps,temp1);
      DeleteMatrix(&temp1);
      // add correction to fDXDAZ[0]
      Int_t *rows=new Int_t[GetNy()];
      Int_t *cols=new Int_t[GetNy()];
      Double_t *data=new Double_t[GetNy()];
      for(Int_t i=0;i<GetNy();i++) {
         rows[i]=i;
         cols[i]=0;
         data[i]=lambda_half;
      }
      TMatrixDSparse *temp2=CreateSparseMatrix
         (GetNy(),1,GetNy(),rows,cols,data);
      delete[] data;
      delete[] rows;
      delete[] cols;
      AddMSparse(fDXDAZ[0],1.0,temp2);
      DeleteMatrix(&temp2);
   }

   DeleteMatrix(&Eepsilon);

   rank=0;
   fVxxInv = InvertMSparseSymmPos(fVxx,&rank);
   if(rank != GetNx()) {
      Warning("DoUnfold","rank of output covariance is %d expect %d",
              rank,GetNx());
   }

   TVectorD VxxInvDiag(fVxxInv->GetNrows());
   const Int_t *VxxInv_rows=fVxxInv->GetRowIndexArray();
   const Int_t *VxxInv_cols=fVxxInv->GetColIndexArray();
   const Double_t *VxxInv_data=fVxxInv->GetMatrixArray();
   for (int ix = 0; ix < fVxxInv->GetNrows(); ix++) {
      for(int ik=VxxInv_rows[ix];ik<VxxInv_rows[ix+1];ik++) {
         if(ix==VxxInv_cols[ik]) {
            VxxInvDiag(ix)=VxxInv_data[ik];
         }
      }
   }

   // maximum global correlation coefficient
   const Int_t *Vxx_rows=fVxx->GetRowIndexArray();
   const Int_t *Vxx_cols=fVxx->GetColIndexArray();
   const Double_t *Vxx_data=fVxx->GetMatrixArray();

   Double_t rho_squared_max = 0.0;
   Double_t rho_sum = 0.0;
   Int_t n_rho=0;
   for (int ix = 0; ix < fVxx->GetNrows(); ix++) {
      for(int ik=Vxx_rows[ix];ik<Vxx_rows[ix+1];ik++) {
         if(ix==Vxx_cols[ik]) {
            Double_t rho_squared =
               1. - 1. / (VxxInvDiag(ix) * Vxx_data[ik]);
            if (rho_squared > rho_squared_max)
               rho_squared_max = rho_squared;
            if(rho_squared>0.0) {
               rho_sum += TMath::Sqrt(rho_squared);
               n_rho++;               
            }
            break;
         }
      }
   }
   fRhoMax = TMath::Sqrt(rho_squared_max);
   fRhoAvg = (n_rho>0) ? (rho_sum/n_rho) : -1.0;

   return fRhoMax;
}

////////////////////////////////////////////////////////////////////////
/// create a sparse matrix, given the nonzero elements
///
/// \param[in] nrow number of rows
/// \param[in] ncol number of columns
/// \param[in] nel number of non-zero elements
/// \param[in] row row indexes of non-zero elements
/// \param[in] col column indexes of non-zero elements
/// \param[in] data non-zero elements data
///
/// return pointer to a new sparse matrix
///
/// shortcut to new TMatrixDSparse() followed by SetMatrixArray()
TMatrixDSparse *TUnfoldV17::CreateSparseMatrix
(Int_t nrow,Int_t ncol,Int_t nel,Int_t *row,Int_t *col,Double_t *data) const
{
   // create a sparse matri
   //   nrow,ncol : dimension of the matrix
   //   nel: number of non-zero elements
   //   row[nel],col[nel],data[nel] : indices and data of the non-zero elements
   TMatrixDSparse *A=new TMatrixDSparse(nrow,ncol);
   if(nel>0) {
      A->SetMatrixArray(nel,row,col,data);
   }
   return A;
}

////////////////////////////////////////////////////////////////////////
/// multiply two sparse matrices
///
/// \param[in] a sparse matrix
/// \param[in] b sparse matrix
///
/// returns a new sparse matrix a*b.
/// <br/>
/// A replacement for:
///  new TMatrixDSparse(a,TMatrixDSparse::kMult,b)
/// the root implementation had problems in older versions of root
TMatrixDSparse *TUnfoldV17::MultiplyMSparseMSparse(const TMatrixDSparse *a,
                                                const TMatrixDSparse *b) const
{
   if(a->GetNcols()!=b->GetNrows()) {
      Fatal("MultiplyMSparseMSparse",
            "inconsistent matrix col/ matrix row %d !=%d",
            a->GetNcols(),b->GetNrows());
   }

   TMatrixDSparse *r=new TMatrixDSparse(a->GetNrows(),b->GetNcols());
   const Int_t *a_rows=a->GetRowIndexArray();
   const Int_t *a_cols=a->GetColIndexArray();
   const Double_t *a_data=a->GetMatrixArray();
   const Int_t *b_rows=b->GetRowIndexArray();
   const Int_t *b_cols=b->GetColIndexArray();
   const Double_t *b_data=b->GetMatrixArray();
   // maximum size of the output matrix
   int nMax=0;
   for (Int_t irow = 0; irow < a->GetNrows(); irow++) {
      if(a_rows[irow+1]>a_rows[irow]) nMax += b->GetNcols();
   }
   if((nMax>0)&&(a_cols)&&(b_cols)) {
      Int_t *r_rows=new Int_t[nMax];
      Int_t *r_cols=new Int_t[nMax];
      Double_t *r_data=new Double_t[nMax];
      Double_t *row_data=new Double_t[b->GetNcols()];
      Int_t n=0;
      for (Int_t irow = 0; irow < a->GetNrows(); irow++) {
         if(a_rows[irow+1]<=a_rows[irow]) continue;
         // clear row data
         for(Int_t icol=0;icol<b->GetNcols();icol++) {
            row_data[icol]=0.0;
         }
         // loop over a-columns in this a-row
         for(Int_t ia=a_rows[irow];ia<a_rows[irow+1];ia++) {
            Int_t k=a_cols[ia];
            // loop over b-columns in b-row k
            for(Int_t ib=b_rows[k];ib<b_rows[k+1];ib++) {
               row_data[b_cols[ib]] += a_data[ia]*b_data[ib];
            }
         }
         // store nonzero elements
         for(Int_t icol=0;icol<b->GetNcols();icol++) {
            if(row_data[icol] != 0.0) {
               r_rows[n]=irow;
               r_cols[n]=icol;
               r_data[n]=row_data[icol];
               n++;
            }
         }
      }
      if(n>0) {
         r->SetMatrixArray(n,r_rows,r_cols,r_data);
      }
      delete[] r_rows;
      delete[] r_cols;
      delete[] r_data;
      delete[] row_data;
   }

   return r;
}

////////////////////////////////////////////////////////////////////////
/// multiply a transposed Sparse matrix with another Sparse matrix
///
/// \param[in] a sparse matrix (to be transposed)
/// \param[in] b sparse matrix
///
/// returns a new sparse matrix a<sup>T</sup>*b
/// <br/>
/// this is a replacement for the root constructors
/// new TMatrixDSparse(TMatrixDSparse(TMatrixDSparse::kTransposed,*a),TMatrixDSparse::kMult,*b)
 
TMatrixDSparse *TUnfoldV17::MultiplyMSparseTranspMSparse
(const TMatrixDSparse *a,const TMatrixDSparse *b) const
{
  if(a->GetNrows() != b->GetNrows()) {
      Fatal("MultiplyMSparseTranspMSparse",
            "inconsistent matrix row numbers %d!=%d",
            a->GetNrows(),b->GetNrows());
   }

   TMatrixDSparse *r=new TMatrixDSparse(a->GetNcols(),b->GetNcols());
   const Int_t *a_rows=a->GetRowIndexArray();
   const Int_t *a_cols=a->GetColIndexArray();
   const Double_t *a_data=a->GetMatrixArray();
   const Int_t *b_rows=b->GetRowIndexArray();
   const Int_t *b_cols=b->GetColIndexArray();
   const Double_t *b_data=b->GetMatrixArray();
   // maximum size of the output matrix

   // matrix multiplication
   typedef std::map<Int_t,Double_t> MMatrixRow_t;
   typedef std::map<Int_t, MMatrixRow_t > MMatrix_t;
   MMatrix_t matrix;

   for(Int_t iRowAB=0;iRowAB<a->GetNrows();iRowAB++) {
      for(Int_t ia=a_rows[iRowAB];ia<a_rows[iRowAB+1];ia++) {
         for(Int_t ib=b_rows[iRowAB];ib<b_rows[iRowAB+1];ib++) {
            // this creates a new row if necessary
            MMatrixRow_t &row=matrix[a_cols[ia]];
            MMatrixRow_t::iterator icol=row.find(b_cols[ib]);
            if(icol!=row.end()) {
               // update existing row
               (*icol).second += a_data[ia]*b_data[ib];
            } else {
               // create new row
               row[b_cols[ib]] = a_data[ia]*b_data[ib];
            }
         }
      }
   }

   Int_t n=0;
   for(MMatrix_t::const_iterator irow=matrix.begin();
       irow!=matrix.end();irow++) {
      n += (*irow).second.size();
   }
   if(n>0) {
      // pack matrix into arrays
      Int_t *r_rows=new Int_t[n];
      Int_t *r_cols=new Int_t[n];
      Double_t *r_data=new Double_t[n];
      n=0;
      for(MMatrix_t::const_iterator irow=matrix.begin();
          irow!=matrix.end();irow++) {
         for(MMatrixRow_t::const_iterator icol=(*irow).second.begin();
             icol!=(*irow).second.end();icol++) {
            r_rows[n]=(*irow).first;
            r_cols[n]=(*icol).first;
            r_data[n]=(*icol).second;
            n++;
         }
      }
      // pack arrays into TMatrixDSparse
      if(n>0) {
         r->SetMatrixArray(n,r_rows,r_cols,r_data);
      }
      delete[] r_rows;
      delete[] r_cols;
      delete[] r_data;
   }

   return r;
}

////////////////////////////////////////////////////////////////////////
/// multiply sparse matrix and a non-sparse matrix
///
/// \param[in] a sparse matrix
/// \param[in] b matrix
///
/// returns a new sparse matrix a*b.
/// <br/> A replacement for:
///  new TMatrixDSparse(a,TMatrixDSparse::kMult,b)
/// the root implementation had problems in older versions of root
TMatrixDSparse *TUnfoldV17::MultiplyMSparseM(const TMatrixDSparse *a,
                                          const TMatrixD *b) const
{
   if(a->GetNcols()!=b->GetNrows()) {
      Fatal("MultiplyMSparseM","inconsistent matrix col /matrix row %d!=%d",
            a->GetNcols(),b->GetNrows());
   }

   TMatrixDSparse *r=new TMatrixDSparse(a->GetNrows(),b->GetNcols());
   const Int_t *a_rows=a->GetRowIndexArray();
   const Int_t *a_cols=a->GetColIndexArray();
   const Double_t *a_data=a->GetMatrixArray();
   // maximum size of the output matrix
   int nMax=0;
   for (Int_t irow = 0; irow < a->GetNrows(); irow++) {
      if(a_rows[irow+1]-a_rows[irow]>0) nMax += b->GetNcols();
   }
   if(nMax>0) {
      Int_t *r_rows=new Int_t[nMax];
      Int_t *r_cols=new Int_t[nMax];
      Double_t *r_data=new Double_t[nMax];

      Int_t n=0;
      // fill matrix r
      for (Int_t irow = 0; irow < a->GetNrows(); irow++) {
         if(a_rows[irow+1]-a_rows[irow]<=0) continue;
         for(Int_t icol=0;icol<b->GetNcols();icol++) {
            r_rows[n]=irow;
            r_cols[n]=icol;
            r_data[n]=0.0;
            for(Int_t i=a_rows[irow];i<a_rows[irow+1];i++) {
               Int_t j=a_cols[i];
               r_data[n] += a_data[i]*(*b)(j,icol);
            }
            if(r_data[n]!=0.0) n++;
         }
      }
      if(n>0) {
         r->SetMatrixArray(n,r_rows,r_cols,r_data);
      }
      delete[] r_rows;
      delete[] r_cols;
      delete[] r_data;
   }
   return r;
}


////////////////////////////////////////////////////////////////////////
/// calculate a sparse matrix product M1*V*M2<sup>T</sup> where the diagonal matrix V is
/// given by a vector
///
/// \param[in] m1 pointer to sparse matrix with dimension I*K
/// \param[in] m2 pointer to sparse matrix with dimension J*K
/// \param[in] v pointer to vector (matrix) with dimension K*1
///
/// returns a sparse matrix R with elements
/// r<sub>ij</sub>=&Sigma;<sub>k</sub>M1<sub>ik</sub>V<sub>k</sub>M2<sub>jk</sub>

TMatrixDSparse *TUnfoldV17::MultiplyMSparseMSparseTranspVector
(const TMatrixDSparse *m1,const TMatrixDSparse *m2,
 const TMatrixTBase<Double_t> *v) const
{
   if((m1->GetNcols() != m2->GetNcols())||
      (v && ((m1->GetNcols()!=v->GetNrows())||(v->GetNcols()!=1)))) {
      if(v) {
         Fatal("MultiplyMSparseMSparseTranspVector",
               "matrix cols/vector rows %d!=%d!=%d or vector rows %d!=1\n",
               m1->GetNcols(),m2->GetNcols(),v->GetNrows(),v->GetNcols());
      } else {
         Fatal("MultiplyMSparseMSparseTranspVector",
               "matrix cols %d!=%d\n",m1->GetNcols(),m2->GetNcols());
      }
   }
   const Int_t *rows_m1=m1->GetRowIndexArray();
   const Int_t *cols_m1=m1->GetColIndexArray();
   const Double_t *data_m1=m1->GetMatrixArray();
   Int_t num_m1=0;
   for(Int_t i=0;i<m1->GetNrows();i++) {
      if(rows_m1[i]<rows_m1[i+1]) num_m1++;
   }
   const Int_t *rows_m2=m2->GetRowIndexArray();
   const Int_t *cols_m2=m2->GetColIndexArray();
   const Double_t *data_m2=m2->GetMatrixArray();
   Int_t num_m2=0;
   for(Int_t j=0;j<m2->GetNrows();j++) {
      if(rows_m2[j]<rows_m2[j+1]) num_m2++;
   }
   const TMatrixDSparse *v_sparse=dynamic_cast<const TMatrixDSparse *>(v);
   const Int_t *v_rows=0;
   const Double_t *v_data=0;
   if(v_sparse) {
      v_rows=v_sparse->GetRowIndexArray();
      v_data=v_sparse->GetMatrixArray();
   }
   Int_t num_r=num_m1*num_m2+1;
   Int_t *row_r=new Int_t[num_r];
   Int_t *col_r=new Int_t[num_r];
   Double_t *data_r=new Double_t[num_r];
   num_r=0;
   for(Int_t i=0;i<m1->GetNrows();i++) {
      for(Int_t j=0;j<m2->GetNrows();j++) {
         data_r[num_r]=0.0;
         Int_t index_m1=rows_m1[i];
         Int_t index_m2=rows_m2[j];
         while((index_m1<rows_m1[i+1])&&(index_m2<rows_m2[j+1])) {
            Int_t k1=cols_m1[index_m1];
            Int_t k2=cols_m2[index_m2];
            if(k1<k2) {
               index_m1++;
            } else if(k1>k2) {
               index_m2++;
            } else {
               if(v_sparse) {
                  Int_t v_index=v_rows[k1];
                  if(v_index<v_rows[k1+1]) {
                     data_r[num_r] += data_m1[index_m1] * data_m2[index_m2]
                        * v_data[v_index];
                  }
               } else if(v) {
                  data_r[num_r] += data_m1[index_m1] * data_m2[index_m2]
                     * (*v)(k1,0);
               } else {
                  data_r[num_r] += data_m1[index_m1] * data_m2[index_m2];
               }
               index_m1++;
               index_m2++;
            }
         }
         if(data_r[num_r] !=0.0) {
            row_r[num_r]=i;
            col_r[num_r]=j;
            num_r++;
         }
      }
   }
   TMatrixDSparse *r=CreateSparseMatrix(m1->GetNrows(),m2->GetNrows(),
                                        num_r,row_r,col_r,data_r);
   delete[] row_r;
   delete[] col_r;
   delete[] data_r;
   return r;
}

////////////////////////////////////////////////////////////////////////
/// add a sparse matrix, scaled by a factor, to another scaled matrix
///
/// \param[inout] dest destination matrix
/// \param[in] f scaling factor
/// \param[in] src matrix to be added to dest
///
/// a replacement for
///     (*dest) += f * (*src)
/// which suffered from a bug in old root versions

void TUnfoldV17::AddMSparse(TMatrixDSparse *dest,Double_t f,
                         const TMatrixDSparse *src) const
{
   const Int_t *dest_rows=dest->GetRowIndexArray();
   const Int_t *dest_cols=dest->GetColIndexArray();
   const Double_t *dest_data=dest->GetMatrixArray();
   const Int_t *src_rows=src->GetRowIndexArray();
   const Int_t *src_cols=src->GetColIndexArray();
   const Double_t *src_data=src->GetMatrixArray();

   if((dest->GetNrows()!=src->GetNrows())||
      (dest->GetNcols()!=src->GetNcols())) {
      Fatal("AddMSparse","inconsistent matrix rows %d!=%d OR cols %d!=%d",
            src->GetNrows(),dest->GetNrows(),src->GetNcols(),dest->GetNcols());
   }
   Int_t nmax=dest->GetNrows()*dest->GetNcols();
   Double_t *result_data=new Double_t[nmax];
   Int_t *result_rows=new Int_t[nmax];
   Int_t *result_cols=new Int_t[nmax];
   Int_t n=0;
   for(Int_t row=0;row<dest->GetNrows();row++) {
      Int_t i_dest=dest_rows[row];
      Int_t i_src=src_rows[row];
      while((i_dest<dest_rows[row+1])||(i_src<src_rows[row+1])) {
         Int_t col_dest=(i_dest<dest_rows[row+1]) ? 
            dest_cols[i_dest] : dest->GetNcols();
         Int_t col_src =(i_src <src_rows[row+1] ) ?
            src_cols [i_src] :  src->GetNcols();
         result_rows[n]=row;
         if(col_dest<col_src) {
            result_cols[n]=col_dest;
            result_data[n]=dest_data[i_dest++];
         } else if(col_dest>col_src) {
            result_cols[n]=col_src;
            result_data[n]=f*src_data[i_src++];
         } else {
            result_cols[n]=col_dest;
            result_data[n]=dest_data[i_dest++]+f*src_data[i_src++];
         }
         if(result_data[n] !=0.0) {
            if(!TMath::Finite(result_data[n])) {
               Fatal("AddMSparse","Nan detected %d %d %d",
                     row,i_dest,i_src);
            }
            n++;
         }
      }
   }
   if(n<=0) {
      n=1;
      result_rows[0]=0;
      result_cols[0]=0;
      result_data[0]=0.0;
   }
   dest->SetMatrixArray(n,result_rows,result_cols,result_data);
   delete[] result_data;
   delete[] result_rows;
   delete[] result_cols;
}

////////////////////////////////////////////////////////////////////////
/// get the inverse or pseudo-inverse of a positive, sparse matrix
///
/// \param[in] A the sparse matrix to be inverted, has to be positive
/// \param[inout] rankPtr if zero, suppress calculation of pseudo-inverse
/// otherwise the rank of the matrix is returned in *rankPtr
///
/// return value: 0 or a new sparse matrix
/// <ul>
/// <li>if(rankPtr==0) return the inverse if it exists, or return 0</li>
/// <li>else return a (pseudo-)inverse and store the rank of the matrix in
/// *rankPtr </li>
/// </ul>
///
/// the matrix inversion is optimized in performance for the case
/// where a large submatrix of A is diagonal

TMatrixDSparse *TUnfoldV17::InvertMSparseSymmPos
(const TMatrixDSparse *A,Int_t *rankPtr) const
{

   if(A->GetNcols()!=A->GetNrows()) {
      Fatal("InvertMSparseSymmPos","inconsistent matrix row/col %d!=%d",
            A->GetNcols(),A->GetNrows());
   }

   Bool_t *isZero=new Bool_t[A->GetNrows()];
   const Int_t *a_rows=A->GetRowIndexArray();
   const Int_t *a_cols=A->GetColIndexArray();
   const Double_t *a_data=A->GetMatrixArray();

   // determine diagonal elements
   //  Aii: diagonals of A
   Int_t iDiagonal=0;
   Int_t iBlock=A->GetNrows();
   Bool_t isDiagonal=kTRUE;
   TVectorD aII(A->GetNrows());
   Int_t nError=0;
   for(Int_t iA=0;iA<A->GetNrows();iA++) {
      for(Int_t indexA=a_rows[iA];indexA<a_rows[iA+1];indexA++) {
         Int_t jA=a_cols[indexA];
         if(iA==jA) {
            if(!(a_data[indexA]>=0.0)) nError++;
            aII(iA)=a_data[indexA];
         }
      }
   }
   if(nError>0) {
      Fatal("InvertMSparseSymmPos",
            "Matrix has %d negative elements on the diagonal", nError);
      return 0;
   }

   // reorder matrix such that the largest block of zeros is swapped
   // to the lowest indices
   // the result of this ordering:
   //  swap[i] : for index i point to the row in A
   //  swapBack[iA] : for index iA in A point to the swapped index i
   // the indices are grouped into three groups
   //   0..iDiagonal-1 : these rows only have diagonal elements
   //   iDiagonal..iBlock : these rows contain a diagonal block matrix
   //
   Int_t *swap=new Int_t[A->GetNrows()];
   for(Int_t j=0;j<A->GetNrows();j++) swap[j]=j;
   for(Int_t iStart=0;iStart<iBlock;iStart++) {
      Int_t nZero=0;
      for(Int_t i=iStart;i<iBlock;i++) {
         Int_t iA=swap[i];
         Int_t n=A->GetNrows()-(a_rows[iA+1]-a_rows[iA]);
         if(n>nZero) {
            Int_t tmp=swap[iStart];
            swap[iStart]=swap[i];
            swap[i]=tmp;
            nZero=n;
         }
      }
      for(Int_t i=0;i<A->GetNrows();i++) isZero[i]=kTRUE;
      Int_t iA=swap[iStart];
      for(Int_t indexA=a_rows[iA];indexA<a_rows[iA+1];indexA++) {
         Int_t jA=a_cols[indexA];
         isZero[jA]=kFALSE;
         if(iA!=jA) {
            isDiagonal=kFALSE;
         }
      }
      if(isDiagonal) {
         iDiagonal=iStart+1;
      } else {
         for(Int_t i=iStart+1;i<iBlock;) {
            if(isZero[swap[i]]) {
               i++;
            } else {
               iBlock--;
               Int_t tmp=swap[iBlock];
               swap[iBlock]=swap[i];
               swap[i]=tmp;
            }
         }
      }
   }

   // for tests uncomment this:
   //  iBlock=iDiagonal;


   // conditioning of the iBlock part
#ifdef CONDITION_BLOCK_PART
   Int_t nn=A->GetNrows()-iBlock;
   for(int inc=(nn+1)/2;inc;inc /= 2) {
      for(int i=inc;i<nn;i++) {
         int itmp=swap[i+iBlock];
         int j;
         for(j=i;(j>=inc)&&(aII(swap[j-inc+iBlock])<aII(itmp));j -=inc) {
            swap[j+iBlock]=swap[j-inc+iBlock];
         }
         swap[j+iBlock]=itmp;
      }
   }
#endif
   // construct array for swapping back
   Int_t *swapBack=new Int_t[A->GetNrows()];
   for(Int_t i=0;i<A->GetNrows();i++) {
      swapBack[swap[i]]=i;
   }
#ifdef DEBUG_DETAIL
   for(Int_t i=0;i<A->GetNrows();i++) {
      std::cout<<"    "<<i<<" "<<swap[i]<<" "<<swapBack[i]<<"\n";
   }
   std::cout<<"after sorting\n";
   for(Int_t i=0;i<A->GetNrows();i++) {
      if(i==iDiagonal) std::cout<<"iDiagonal="<<i<<"\n";
      if(i==iBlock) std::cout<<"iBlock="<<i<<"\n";
      std::cout<<" "<<swap[i]<<" "<<aII(swap[i])<<"\n";
   }
   {
      // sanity check:
      // (1) sub-matrix swapped[0]..swapped[iDiagonal]
      //       must not contain off-diagonals
      // (2) sub-matrix swapped[0]..swapped[iBlock-1] must be diagonal
      Int_t nDiag=0;
      Int_t nError1=0;
      Int_t nError2=0;
      Int_t nBlock=0;
      Int_t nNonzero=0;
      for(Int_t i=0;i<A->GetNrows();i++) {
         Int_t row=swap[i];
         for(Int_t indexA=a_rows[row];indexA<a_rows[row+1];indexA++) {
            Int_t j=swapBack[a_cols[indexA]];
            if(i==j) nDiag++;
            else if((i<iDiagonal)||(j<iDiagonal)) nError1++;
            else if((i<iBlock)&&(j<iBlock)) nError2++;
            else if((i<iBlock)||(j<iBlock)) nBlock++;
            else nNonzero++;
         }
      }
      if(nError1+nError2>0) {
         Fatal("InvertMSparseSymmPos","sparse matrix analysis failed %d %d %d %d %d",
               iDiagonal,iBlock,A->GetNrows(),nError1,nError2);
      }
   }
#endif
#ifdef DEBUG
   Info("InvertMSparseSymmPos","iDiagonal=%d iBlock=%d nRow=%d",
        iDiagonal,iBlock,A->GetNrows());
#endif

   //=============================================
   // matrix inversion starts here
   //
   //  the matrix is split into several parts
   //         D1  0   0
   //  A = (  0   D2  B# )
   //         0   B   C
   //
   //  D1,D2 are diagonal matrices
   //
   //  first, the D1 part is inverted by calculating 1/D1
   //   dim(D1)= iDiagonal
   //  next, the other parts are inverted using Schur complement
   //
   //           1/D1   0    0
   //  Ainv = (  0     E    G#   )
   //            0     G    F^-1
   //
   //  where F = C + (-B/D2) B#
   //        G = (F^-1) (-B/D2)
   //        E = 1/D2 + (-B#/D2) G)
   //
   //  the inverse of F is calculated using a Cholesky decomposition
   //
   // Error handling:
   // (a) if rankPtr==0: user requests inverse
   //
   //    if D1 is not strictly positive, return NULL
   //    if D2 is not strictly positive, return NULL
   //    if F is not strictly positive (Cholesky decomposition failed)
   //           return NULL
   //    else
   //      return Ainv as defined above
   //
   // (b) if rankPtr !=0: user requests pseudo-inverse
   //    if D2 is not strictly positive or F is not strictly positive
   //      calculate singular-value decomposition of
   //          D2   B#
   //         (       ) = O D O^-1
   //           B   C
   //      if some eigenvalues are negative, return NULL
   //      else calculate pseudo-inverse
   //        *rankPtr = rank(D1)+rank(D)
   //    else
   //      calculate pseudo-inverse of D1  (D1_ii==0) ? 0 : 1/D1_ii
   //      *rankPtr=rank(D1)+nrow(D2)+nrow(C)
   //      return Ainv as defined above

   Double_t *rEl_data=new Double_t[A->GetNrows()*A->GetNrows()];
   Int_t *rEl_col=new Int_t[A->GetNrows()*A->GetNrows()];
   Int_t *rEl_row=new Int_t[A->GetNrows()*A->GetNrows()];
   Int_t rNumEl=0;

   //====================================================
   // invert D1
   Int_t rankD1=0;
   for(Int_t i=0;i<iDiagonal;++i) {
      Int_t iA=swap[i];
      if(aII(iA)>0.0) {
         rEl_col[rNumEl]=iA;
         rEl_row[rNumEl]=iA;
         rEl_data[rNumEl]=1./aII(iA);
         ++rankD1;
         ++rNumEl;
      }
   }
   if((!rankPtr)&&(rankD1!=iDiagonal)) {
      Fatal("InvertMSparseSymmPos",
            "diagonal part 1 has rank %d != %d, matrix can not be inverted",
            rankD1,iDiagonal);
      rNumEl=-1;
   }


   //====================================================
   // invert D2
   Int_t nD2=iBlock-iDiagonal;
   TMatrixDSparse *D2inv=0;
   if((rNumEl>=0)&&(nD2>0)) {
      Double_t *D2inv_data=new Double_t[nD2];
      Int_t *D2inv_col=new Int_t[nD2];
      Int_t *D2inv_row=new Int_t[nD2];
      Int_t D2invNumEl=0;
      for(Int_t i=0;i<nD2;++i) {
         Int_t iA=swap[i+iDiagonal];
         if(aII(iA)>0.0) {
            D2inv_col[D2invNumEl]=i;
            D2inv_row[D2invNumEl]=i;
            D2inv_data[D2invNumEl]=1./aII(iA);
            ++D2invNumEl;
         }
      }
      if(D2invNumEl==nD2) {
         D2inv=CreateSparseMatrix(nD2,nD2,D2invNumEl,D2inv_row,D2inv_col,
                                  D2inv_data);
      } else if(!rankPtr) {
         Fatal("InvertMSparseSymmPos",
               "diagonal part 2 has rank %d != %d, matrix can not be inverted",
               D2invNumEl,nD2);
         rNumEl=-2;
      }
      delete [] D2inv_data;
      delete [] D2inv_col;
      delete [] D2inv_row;
   }

   //====================================================
   // invert F

   Int_t nF=A->GetNrows()-iBlock;
   TMatrixDSparse *Finv=0;
   TMatrixDSparse *B=0;
   TMatrixDSparse *minusBD2inv=0;
   if((rNumEl>=0)&&(nF>0)&&((nD2==0)||D2inv)) {
      // construct matrices F and B
      Int_t nFmax=nF*nF;
      Double_t epsilonF2=fEpsMatrix;
      Double_t *F_data=new Double_t[nFmax];
      Int_t *F_col=new Int_t[nFmax];
      Int_t *F_row=new Int_t[nFmax];
      Int_t FnumEl=0;

      Int_t nBmax=nF*(nD2+1);
      Double_t *B_data=new Double_t[nBmax];
      Int_t *B_col=new Int_t[nBmax];
      Int_t *B_row=new Int_t[nBmax];
      Int_t BnumEl=0;

      for(Int_t i=0;i<nF;i++) {
         Int_t iA=swap[i+iBlock];
         for(Int_t indexA=a_rows[iA];indexA<a_rows[iA+1];indexA++) {
            Int_t jA=a_cols[indexA];
            Int_t j0=swapBack[jA];
            if(j0>=iBlock) {
               Int_t j=j0-iBlock;
               F_row[FnumEl]=i;
               F_col[FnumEl]=j;
               F_data[FnumEl]=a_data[indexA];
               FnumEl++;
            } else if(j0>=iDiagonal) {
               Int_t j=j0-iDiagonal;
               B_row[BnumEl]=i;
               B_col[BnumEl]=j;
               B_data[BnumEl]=a_data[indexA];
               BnumEl++;               
            }
         }
      }
      TMatrixDSparse *F=0;
      if(FnumEl) {
#ifndef FORCE_EIGENVALUE_DECOMPOSITION
         F=CreateSparseMatrix(nF,nF,FnumEl,F_row,F_col,F_data);
#endif
      }
      delete [] F_data;
      delete [] F_col;
      delete [] F_row;
      if(BnumEl) {
         B=CreateSparseMatrix(nF,nD2,BnumEl,B_row,B_col,B_data);
      }
      delete [] B_data;
      delete [] B_col;
      delete [] B_row;

      if(B && D2inv) {
         minusBD2inv=MultiplyMSparseMSparse(B,D2inv);
         if(minusBD2inv) {
            Int_t mbd2_nMax=minusBD2inv->GetRowIndexArray()
               [minusBD2inv->GetNrows()];
            Double_t *mbd2_data=minusBD2inv->GetMatrixArray();
            for(Int_t i=0;i<mbd2_nMax;i++) {
               mbd2_data[i] = -  mbd2_data[i];
            }
         }
      }
      if(minusBD2inv && F) {
         TMatrixDSparse *minusBD2invBt=
            MultiplyMSparseMSparseTranspVector(minusBD2inv,B,0);
         AddMSparse(F,1.,minusBD2invBt);
         DeleteMatrix(&minusBD2invBt);
      }

      if(F) {
         // cholesky decomposition with preconditioning
         const Int_t *f_rows=F->GetRowIndexArray();
         const Int_t *f_cols=F->GetColIndexArray();
         const Double_t *f_data=F->GetMatrixArray();
         // cholesky-type decomposition of F
         TMatrixD c(nF,nF);
         Int_t nErrorF=0;
         for(Int_t i=0;i<nF;i++) {
            for(Int_t indexF=f_rows[i];indexF<f_rows[i+1];indexF++) {
               if(f_cols[indexF]>=i) c(f_cols[indexF],i)=f_data[indexF];
            }
            // calculate diagonal element
       Double_t c_ii=c(i,i);
            for(Int_t j=0;j<i;j++) {
               Double_t c_ij=c(i,j);
               c_ii -= c_ij*c_ij;
            }
            if(c_ii<=0.0) {
               nErrorF++;
               break;
            }
       c_ii=TMath::Sqrt(c_ii);
       c(i,i)=c_ii;
            // off-diagonal elements
            for(Int_t j=i+1;j<nF;j++) {
               Double_t c_ji=c(j,i);
               for(Int_t k=0;k<i;k++) {
                  c_ji -= c(i,k)*c(j,k);
               }
               c(j,i) = c_ji/c_ii;
            }
         }
         // check condition of dInv
         if(!nErrorF) {
            Double_t dmin=c(0,0);
            Double_t dmax=dmin;
            for(Int_t i=1;i<nF;i++) {
               dmin=TMath::Min(dmin,c(i,i));
               dmax=TMath::Max(dmax,c(i,i));
            }
#ifdef DEBUG
            std::cout<<"dmin,dmax: "<<dmin<<" "<<dmax<<"\n";
#endif
            if(dmin/dmax<epsilonF2*nF) nErrorF=2;
         }
         if(!nErrorF) {
            // here: F = c c#
            // construct inverse of c
            TMatrixD cinv(nF,nF);
            for(Int_t i=0;i<nF;i++) {
               cinv(i,i)=1./c(i,i);
            }
            for(Int_t i=0;i<nF;i++) {
               for(Int_t j=i+1;j<nF;j++) {
                  Double_t tmp=-c(j,i)*cinv(i,i);
                  for(Int_t k=i+1;k<j;k++) {
                     tmp -= cinv(k,i)*c(j,k);
                  }
                  cinv(j,i)=tmp*cinv(j,j);
               }
            }
            TMatrixDSparse cInvSparse(cinv);
            Finv=MultiplyMSparseTranspMSparse
               (&cInvSparse,&cInvSparse);
         }
         DeleteMatrix(&F);
      }
   }

   // here:
   //   rNumEl>=0: diagonal part has been inverted
   //   (nD2==0)||D2inv : D2 part has been inverted or is empty
   //   (nF==0)||Finv : F part has been inverted or is empty

   Int_t rankD2Block=0;
   if(rNumEl>=0) {
      if( ((nD2==0)||D2inv) && ((nF==0)||Finv) ) {
         // all matrix parts have been inverted, compose full matrix
         //           1/D1   0    0
         //  Ainv = (  0     E    G#   )
         //            0     G    F^-1
         //
         //        G = (F^-1) (-B/D2)
         //        E = 1/D2 + (-B#/D2) G)

         TMatrixDSparse *G=0;
         if(Finv && minusBD2inv) {
            G=MultiplyMSparseMSparse(Finv,minusBD2inv);
         }
         TMatrixDSparse *E=0;
         if(D2inv) E=new TMatrixDSparse(*D2inv);
         if(G && minusBD2inv) {
            TMatrixDSparse *minusBD2invTransG=
               MultiplyMSparseTranspMSparse(minusBD2inv,G);
            if(E) {
               AddMSparse(E,1.,minusBD2invTransG);
               DeleteMatrix(&minusBD2invTransG);
            } else {
               E=minusBD2invTransG;
            }
         }
         // pack matrix E to r
         if(E) {
            const Int_t *e_rows=E->GetRowIndexArray();
            const Int_t *e_cols=E->GetColIndexArray();
            const Double_t *e_data=E->GetMatrixArray();
            for(Int_t iE=0;iE<E->GetNrows();iE++) {
               Int_t iA=swap[iE+iDiagonal];
               for(Int_t indexE=e_rows[iE];indexE<e_rows[iE+1];++indexE) {
                  Int_t jE=e_cols[indexE];
                  Int_t jA=swap[jE+iDiagonal];
                  rEl_col[rNumEl]=iA;
                  rEl_row[rNumEl]=jA;
                  rEl_data[rNumEl]=e_data[indexE];
                  ++rNumEl;
               }
            }
            DeleteMatrix(&E);
         }
         // pack matrix G to r
         if(G) {
            const Int_t *g_rows=G->GetRowIndexArray();
            const Int_t *g_cols=G->GetColIndexArray();
            const Double_t *g_data=G->GetMatrixArray();
            for(Int_t iG=0;iG<G->GetNrows();iG++) {
               Int_t iA=swap[iG+iBlock];
               for(Int_t indexG=g_rows[iG];indexG<g_rows[iG+1];++indexG) {
                  Int_t jG=g_cols[indexG];
                  Int_t jA=swap[jG+iDiagonal];
                  // G
                  rEl_col[rNumEl]=iA;
                  rEl_row[rNumEl]=jA;
                  rEl_data[rNumEl]=g_data[indexG];
                  ++rNumEl;
                  // G#
                  rEl_col[rNumEl]=jA;
                  rEl_row[rNumEl]=iA;
                  rEl_data[rNumEl]=g_data[indexG];
                  ++rNumEl;
               }
            }
            DeleteMatrix(&G);
         }
         if(Finv) {
            // pack matrix Finv to r
            const Int_t *finv_rows=Finv->GetRowIndexArray();
            const Int_t *finv_cols=Finv->GetColIndexArray();
            const Double_t *finv_data=Finv->GetMatrixArray();
            for(Int_t iF=0;iF<Finv->GetNrows();iF++) {
               Int_t iA=swap[iF+iBlock];
               for(Int_t indexF=finv_rows[iF];indexF<finv_rows[iF+1];++indexF) {
                  Int_t jF=finv_cols[indexF];
                  Int_t jA=swap[jF+iBlock];
                  rEl_col[rNumEl]=iA;
                  rEl_row[rNumEl]=jA;
                  rEl_data[rNumEl]=finv_data[indexF];
                  ++rNumEl;
               }
            }
         }
         rankD2Block=nD2+nF;
      } else if(!rankPtr) {
         rNumEl=-3;
         Fatal("InvertMSparseSymmPos",
               "non-trivial part has rank < %d, matrix can not be inverted",
               nF);
      } else {
         // part of the matrix could not be inverted, get eingenvalue
         // decomposition
         Int_t nEV=nD2+nF;
         Double_t epsilonEV2=fEpsMatrix /* *nEV*nEV */;
         Info("InvertMSparseSymmPos",
              "cholesky-decomposition failed, try eigenvalue analysis");
#ifdef DEBUG
         std::cout<<"nEV="<<nEV<<" iDiagonal="<<iDiagonal<<"\n";
#endif
         TMatrixDSym EV(nEV);
         for(Int_t i=0;i<nEV;i++) {
            Int_t iA=swap[i+iDiagonal];
            for(Int_t indexA=a_rows[iA];indexA<a_rows[iA+1];indexA++) {
               Int_t jA=a_cols[indexA];
               Int_t j0=swapBack[jA];
               if(j0>=iDiagonal) {
                  Int_t j=j0-iDiagonal;
#ifdef DEBUG
                  if((i<0)||(j<0)||(i>=nEV)||(j>=nEV)) {
                     std::cout<<" error "<<nEV<<" "<<i<<" "<<j<<"\n";
                  }
#endif
                  if(!TMath::Finite(a_data[indexA])) {
                     Fatal("InvertMSparseSymmPos",
                           "non-finite number detected element %d %d\n",
                           iA,jA);
                  }
                  EV(i,j)=a_data[indexA];
               }
            }
         }
         // EV.Print();
         TMatrixDSymEigen Eigen(EV);
#ifdef DEBUG
         std::cout<<"Eigenvalues\n";
         // Eigen.GetEigenValues().Print();
#endif
         Int_t errorEV=0;
         Double_t condition=1.0;
         if(Eigen.GetEigenValues()(0)<0.0) {
            errorEV=1;
         } else if(Eigen.GetEigenValues()(0)>0.0) {
            condition=Eigen.GetEigenValues()(nEV-1)/Eigen.GetEigenValues()(0);
         }
         if(condition<-epsilonEV2) {
            errorEV=2;
         }
         if(errorEV) {
            if(errorEV==1) {
               Error("InvertMSparseSymmPos",
                     "Largest Eigenvalue is negative %f",
                     Eigen.GetEigenValues()(0));
            } else {
               Error("InvertMSparseSymmPos",
                     "Some Eigenvalues are negative (EV%d/EV0=%g epsilon=%g)",
                     nEV-1,
                     Eigen.GetEigenValues()(nEV-1)/Eigen.GetEigenValues()(0),
                     epsilonEV2);
            }
         }
         // compose inverse matrix
         rankD2Block=0;
         Double_t setToZero=epsilonEV2*Eigen.GetEigenValues()(0);
         TMatrixD inverseEV(nEV,1);
         for(Int_t i=0;i<nEV;i++) {
            Double_t x=Eigen.GetEigenValues()(i);
            if(x>setToZero) {
               inverseEV(i,0)=1./x;
               ++rankD2Block;
            }
         }
         TMatrixDSparse V(Eigen.GetEigenVectors());
         TMatrixDSparse *VDVt=MultiplyMSparseMSparseTranspVector
            (&V,&V,&inverseEV);
         
         // pack matrix VDVt to r
         const Int_t *vdvt_rows=VDVt->GetRowIndexArray();
         const Int_t *vdvt_cols=VDVt->GetColIndexArray();
         const Double_t *vdvt_data=VDVt->GetMatrixArray();
         for(Int_t iVDVt=0;iVDVt<VDVt->GetNrows();iVDVt++) {
            Int_t iA=swap[iVDVt+iDiagonal];
            for(Int_t indexVDVt=vdvt_rows[iVDVt];
                indexVDVt<vdvt_rows[iVDVt+1];++indexVDVt) {
               Int_t jVDVt=vdvt_cols[indexVDVt];
               Int_t jA=swap[jVDVt+iDiagonal];
               rEl_col[rNumEl]=iA;
               rEl_row[rNumEl]=jA;
               rEl_data[rNumEl]=vdvt_data[indexVDVt];
               ++rNumEl;
            }
         }
         DeleteMatrix(&VDVt);
      }
   }
   if(rankPtr) {
      *rankPtr=rankD1+rankD2Block;
   }
      

   DeleteMatrix(&D2inv);
   DeleteMatrix(&Finv);
   DeleteMatrix(&B);
   DeleteMatrix(&minusBD2inv);

   delete [] swap;
   delete [] swapBack;
   delete [] isZero;

   TMatrixDSparse *r=(rNumEl>=0) ?
      CreateSparseMatrix(A->GetNrows(),A->GetNrows(),rNumEl,
                         rEl_row,rEl_col,rEl_data) : 0;
   delete [] rEl_data;
   delete [] rEl_col;
   delete [] rEl_row;

#ifdef DEBUG_DETAIL
   // sanity test
   if(r) {
      TMatrixDSparse *Ar=MultiplyMSparseMSparse(A,r);
      TMatrixDSparse *ArA=MultiplyMSparseMSparse(Ar,A);
      TMatrixDSparse *rAr=MultiplyMSparseMSparse(r,Ar);

      TMatrixD ar(*Ar);
      TMatrixD a(*A);
      TMatrixD ara(*ArA);
      TMatrixD R(*r);
      TMatrixD rar(*rAr);

      DeleteMatrix(&Ar);
      DeleteMatrix(&ArA);
      DeleteMatrix(&rAr);

      Double_t epsilonA2=fEpsMatrix /* *a.GetNrows()*a.GetNcols() */;
      for(Int_t i=0;i<a.GetNrows();i++) {
         for(Int_t j=0;j<a.GetNcols();j++) {
            // ar should be symmetric
            if(TMath::Abs(ar(i,j)-ar(j,i))>
               epsilonA2*(TMath::Abs(ar(i,j))+TMath::Abs(ar(j,i)))) {
               std::cout<<"Ar is not symmetric Ar("<<i<<","<<j<<")="<<ar(i,j)
                        <<" Ar("<<j<<","<<i<<")="<<ar(j,i)<<"\n";
            }
            // ara should be equal a
            if(TMath::Abs(ara(i,j)-a(i,j))>
               epsilonA2*(TMath::Abs(ara(i,j))+TMath::Abs(a(i,j)))) {
               std::cout<<"ArA is not equal A ArA("<<i<<","<<j<<")="<<ara(i,j)
                        <<" A("<<i<<","<<j<<")="<<a(i,j)<<"\n";
            }
            // ara should be equal a
            if(TMath::Abs(rar(i,j)-R(i,j))>
               epsilonA2*(TMath::Abs(rar(i,j))+TMath::Abs(R(i,j)))) {
               std::cout<<"rAr is not equal r rAr("<<i<<","<<j<<")="<<rar(i,j)
                        <<" r("<<i<<","<<j<<")="<<R(i,j)<<"\n";
            }
         }
      }
      if(rankPtr) std::cout<<"rank="<<*rankPtr<<"\n";
   } else {
      std::cout<<"Matrix is not positive\n";
   }
#endif
   return r;

}

////////////////////////////////////////////////////////////////////////
/// Get bin name of an outpt bin
///
/// \param[in] iBinX bin number
///
/// Return value: name of the bin
/// <br/>
/// For TUnfold and TUnfoldSys, this function simply returns the bin
/// number as a string. This function really only makes sense in the
/// context of TUnfoldDensity, where binnig schemes are implemented 
/// using the class TUnfoldBinning, and non-trivial bin names are
/// returned. 
TString TUnfoldV17::GetOutputBinName(Int_t iBinX) const
{
   return TString::Format("#%d",iBinX);
}

////////////////////////////////////////////////////////////////////////
/// Set up response matrix and regularisation scheme
///
/// \param[in] hist_A matrix of MC events that describes the migrations
/// \param[in] histmap mapping of the histogram axes
/// \param[in] regmode (default=kRegModeSize) global regularisation mode
/// \param[in] constraint (default=kEConstraintArea) type of constraint
///
/// Treatment of overflow bins in the matrix hist_A
/// <ul>
/// <li>Events reconstructed in underflow or overflow bins are counted
/// as inefficiency. They have to be filled properly.</li>
/// <li>Events where the truth level is in underflow or overflow bins are
/// treated as a part of the generator level distribution.
/// The full truth level distribution (including underflow and
/// overflow) is unfolded.</li>
/// </ul>
/// If unsure, do the following:
/// <ul>
/// <li>store evens where the truth is in underflow or overflow
/// (sometimes called "fakes") in a separate TH1. Ensure that the
/// truth-level underflow and overflow bins of hist_A are all zero.
/// <li>the fakes are background to the
/// measurement. Use the classes TUnfoldSys and TUnfoldDensity instead
/// of the plain TUnfold for subtracting background</li>
/// </ul>

TUnfoldV17::TUnfoldV17(const TH2 *hist_A, EHistMap histmap, ERegMode regmode,
                 EConstraint constraint)
{
   // data members initialized to something different from zero:
   //    fA: filled from hist_A
   //    fDA: filled from hist_A
   //    fX0: filled from hist_A
   //    fL: filled depending on the regularisation scheme
   InitTUnfold();
   SetConstraint(constraint);
   Int_t nx0, nx, ny;
   if (histmap == kHistMapOutputHoriz) {
      // include overflow bins on the X axis
      nx0 = hist_A->GetNbinsX()+2;
      ny = hist_A->GetNbinsY();
   } else {
      // include overflow bins on the X axis
      nx0 = hist_A->GetNbinsY()+2;
      ny = hist_A->GetNbinsX();
   }
   nx = 0;
   // fNx is expected to be nx0, but the input matrix may be ill-formed
   // -> all columns with zero events have to be removed
   //    (because y does not contain any information on that bin in x)
   fSumOverY.Set(nx0);
   fXToHist.Set(nx0);
   fHistToX.Set(nx0);
   Int_t nonzeroA=0;
   // loop
   //  - calculate bias distribution
   //      sum_over_y
   //  - count those generator bins which can be unfolded
   //      fNx
   //  - histogram bins are added to the lookup-tables
   //      fHistToX[] and fXToHist[]
   //    these convert histogram bins to matrix indices and vice versa
   //  - the number of non-zero elements is counted
   //      nonzeroA
   Int_t nskipped=0;
   for (Int_t ix = 0; ix < nx0; ix++) {
      // calculate sum over all detector bins
      // excluding the overflow bins
      Double_t sum = 0.0;
      Int_t nonZeroY = 0;
      for (Int_t iy = 0; iy < ny; iy++) {
         Double_t z;
         if (histmap == kHistMapOutputHoriz) {
            z = hist_A->GetBinContent(ix, iy + 1);
         } else {
            z = hist_A->GetBinContent(iy + 1, ix);
         }
         if (z != 0.0) {
            nonzeroA++;
            nonZeroY++;
            sum += z;
         }
      }
      // check whether there is any sensitivity to this generator bin

      if (nonZeroY) {
         // update mapping tables to convert bin number to matrix index
         fXToHist[nx] = ix;
         fHistToX[ix] = nx;
         // add overflow bins -> important to keep track of the
         // non-reconstructed events
         fSumOverY[nx] = sum;
         if (histmap == kHistMapOutputHoriz) {
            fSumOverY[nx] +=
               hist_A->GetBinContent(ix, 0) +
               hist_A->GetBinContent(ix, ny + 1);
         } else {
            fSumOverY[nx] +=
               hist_A->GetBinContent(0, ix) +
               hist_A->GetBinContent(ny + 1, ix);
         }
         nx++;
      } else {
         nskipped++;
         // histogram bin pointing to -1 (non-existing matrix entry)
         fHistToX[ix] = -1;
      }
   }
   Int_t underflowBin=0,overflowBin=0;
   for (Int_t ix = 0; ix < nx; ix++) {
      Int_t ibinx = fXToHist[ix];
      if(ibinx<1) underflowBin=1;
      if (histmap == kHistMapOutputHoriz) {
         if(ibinx>hist_A->GetNbinsX()) overflowBin=1;
      } else {
         if(ibinx>hist_A->GetNbinsY()) overflowBin=1;
      }
   }
   if(nskipped) {
      Int_t nprint=0;
      Int_t ixfirst=-1,ixlast=-1;
      TString binlist;
      int nDisconnected=0;
      for (Int_t ix = 0; ix < nx0; ix++) {
         if(fHistToX[ix]<0) {
            nprint++;
            if(ixlast<0) {
               binlist +=" ";
               binlist +=ix;
               ixfirst=ix;
            }
            ixlast=ix;
            nDisconnected++;
         }
         if(((fHistToX[ix]>=0)&&(ixlast>=0))||
            (nprint==nskipped)) {
            if(ixlast>ixfirst) {
               binlist += "-";
               binlist += ixlast;
            }
            ixfirst=-1;
            ixlast=-1;
         }
         if(nprint==nskipped) {
            break;
         }
      }
      if(nskipped==(2-underflowBin-overflowBin)) {
         Info("TUnfold","underflow and overflow bin "
         "do not depend on the input data");
      } else {
         Warning("TUnfold","%d output bins "
                 "do not depend on the input data %s",nDisconnected,
                 static_cast<const char *>(binlist));         
      }
   }
   // store bias as matrix
   fX0 = new TMatrixD(nx, 1, fSumOverY.GetArray());
   // store non-zero elements in sparse matrix fA
   // construct sparse matrix fA
   Int_t *rowA = new Int_t[nonzeroA];
   Int_t *colA = new Int_t[nonzeroA];
   Double_t *dataA = new Double_t[nonzeroA];
   Int_t index=0;
   for (Int_t iy = 0; iy < ny; iy++) {
      for (Int_t ix = 0; ix < nx; ix++) {
         Int_t ibinx = fXToHist[ix];
         Double_t z;
         if (histmap == kHistMapOutputHoriz) {
            z = hist_A->GetBinContent(ibinx, iy + 1);
         } else {
            z = hist_A->GetBinContent(iy + 1, ibinx);
         }
         if (z != 0.0) {
            rowA[index]=iy;
            colA[index] = ix;
            dataA[index] = z / fSumOverY[ix];
            index++;
         }
      }
   }
   if(underflowBin && overflowBin) {
      Info("TUnfold","%d input bins and %d output bins (includes 2 underflow/overflow bins)",ny,nx);
   } else if(underflowBin) {
      Info("TUnfold","%d input bins and %d output bins (includes 1 underflow bin)",ny,nx);
   } else if(overflowBin) {
      Info("TUnfold","%d input bins and %d output bins (includes 1 overflow bin)",ny,nx);
   } else {
      Info("TUnfold","%d input bins and %d output bins",ny,nx);
   }
   fA = CreateSparseMatrix(ny,nx,index,rowA,colA,dataA);
   if(ny<nx) {
      Error("TUnfold","too few (ny=%d) input bins for nx=%d output bins",ny,nx);
   } else if(ny==nx) {
      Warning("TUnfold","too few (ny=%d) input bins for nx=%d output bins",ny,nx);
   }
   delete[] rowA;
   delete[] colA;
   delete[] dataA;
   // regularisation conditions squared
   fL = new TMatrixDSparse(1, GetNx());
   if (regmode != kRegModeNone) {
      Int_t nError=RegularizeBins(0, 1, nx0, regmode);
      if(nError>0) {
         if(nError>1) {
            Info("TUnfold",
                 "%d regularisation conditions have been skipped",nError);
         } else {
            Info("TUnfold",
                 "One regularisation condition has been skipped");
         }
      }
   }
}

////////////////////////////////////////////////////////////////////////
/// set bias vector
///
/// \param[in] bias histogram with new bias vector
///
/// the initial bias vector is determined from the response matrix
/// but may be changed by using this method
void TUnfoldV17::SetBias(const TH1 *bias)
{
   DeleteMatrix(&fX0);
   fX0 = new TMatrixD(GetNx(), 1);
   for (Int_t i = 0; i < GetNx(); i++) {
      (*fX0) (i, 0) = bias->GetBinContent(fXToHist[i]);
   }
}

////////////////////////////////////////////////////////////////////////
/// add a row of regularisation conditions to the matrix L
///
/// \param[in] i0 truth histogram bin number 
/// \param[in] f0 entry in the matrix L, column i0
/// \param[in] i1 truth histogram bin number 
/// \param[in] f1 entry in the matrix L, column i1
/// \param[in] i2 truth histogram bin number 
/// \param[in] f2 entry in the matrix L, column i2
///
/// the arguments are used to form one row (k) of the matrix L, where 
/// <br/>  L<sub>k,i0</sub>=f0 and  L<sub>k,i1</sub>=f1 and  L<sub>k,i2</sub>=f2
/// <br>negative indexes i0,i1,i2 are ignored
Bool_t TUnfoldV17::AddRegularisationCondition
(Int_t i0,Double_t f0,Int_t i1,Double_t f1,Int_t i2,Double_t f2)
{
   Int_t indices[3];
   Double_t data[3];
   Int_t nEle=0;

   if(i2>=0) {   
      data[nEle]=f2;
      indices[nEle]=i2;
      nEle++;
   }
   if(i1>=0) {
      data[nEle]=f1;
      indices[nEle]=i1;
      nEle++;
   }
   if(i0>=0) {
      data[nEle]=f0;
      indices[nEle]=i0;
      nEle++;
   }
   return AddRegularisationCondition(nEle,indices,data);
}

////////////////////////////////////////////////////////////////////////
/// add a row of regularisation conditions to the matrix L
///
/// \param[in] nEle number of valid entries in indeces and rowData
/// \param[in] indices column numbers of L to fill
/// \param[in] rowData data to fill into the new row of L
///
/// returns true if a row was added, false otherwise
/// <br/>
/// A new row k is added to the matrix L, its dimension is expanded.
/// The new elements L<sub>ki</sub> are filled from the array rowData[]
/// where the indices i which are taken from the array indices[].
Bool_t TUnfoldV17::AddRegularisationCondition
(Int_t nEle,const Int_t *indices,const Double_t *rowData)
{
   Bool_t r=kTRUE;
   const Int_t *l0_rows=fL->GetRowIndexArray();
   const Int_t *l0_cols=fL->GetColIndexArray();
   const Double_t *l0_data=fL->GetMatrixArray();

   Int_t nF=l0_rows[fL->GetNrows()]+nEle;
   Int_t *l_row=new Int_t[nF];
   Int_t *l_col=new Int_t[nF];
   Double_t *l_data=new Double_t[nF];
   // decode original matrix
   nF=0;
   for(Int_t row=0;row<fL->GetNrows();row++) {
      for(Int_t k=l0_rows[row];k<l0_rows[row+1];k++) {
         l_row[nF]=row;
         l_col[nF]=l0_cols[k];
         l_data[nF]=l0_data[k];
         nF++;
      }
   }

   // if the original matrix does not have any data, reset its row count
   Int_t rowMax=0;
   if(nF>0) {
      rowMax=fL->GetNrows();
   }

   // add the new regularisation conditions
   for(Int_t i=0;i<nEle;i++) {
      Int_t col=fHistToX[indices[i]];
      if(col<0) {
         r=kFALSE;
         break;
      }
      l_row[nF]=rowMax;
      l_col[nF]=col;
      l_data[nF]=rowData[i];
      nF++;
   }

   // replace the old matrix fL
   if(r) {
      DeleteMatrix(&fL);
      fL=CreateSparseMatrix(rowMax+1,GetNx(),nF,l_row,l_col,l_data);
   }
   delete [] l_row;
   delete [] l_col;
   delete [] l_data;
   return r;
}

////////////////////////////////////////////////////////////////////////
/// add  a regularisation condition on the magnitude of a truth bin
///
/// \param[in] bin bin number
/// \param[in] scale (default=1) scale factor
///
/// this adds one row to L, where the element <b>bin</b> takes the
/// value <b>scale</b>
///
/// return value: 0 if ok, 1 if the condition has not been
/// added. Conditions which are not added typically correspond to bin
/// numbers where the truth can not be unfolded (either response
/// matrix is empty or the data do not constrain).
///
/// The RegularizeXXX() methods can be used to set up a custom matrix
/// of regularisation conditions. In this case, start with an empty
/// matrix L (argument regmode=kRegModeNone in the constructor)

Int_t TUnfoldV17::RegularizeSize(int bin, Double_t scale)
{
   // add regularisation on the size of bin i
   //    bin: bin number
   //    scale: size of the regularisation
   // return value: number of conditions which have been skipped
   // modifies data member fL

   if(fRegMode==kRegModeNone) fRegMode=kRegModeSize;
   if(fRegMode!=kRegModeSize) fRegMode=kRegModeMixed;

   return AddRegularisationCondition(bin,scale) ? 0 : 1;
}

////////////////////////////////////////////////////////////////////////
/// add  a regularisation condition on the difference of two truth bin
///
/// \param[in] left_bin bin number
/// \param[in] right_bin bin number
/// \param[in] scale (default=1) scale factor
///
/// this adds one row to L, where the element <b>left_bin</b> takes the
/// value <b>-scale</b> and the element  <b>right_bin</b> takes the
/// value <b>+scale</b>
///
/// return value: 0 if ok, 1 if the condition has not been
/// added. Conditions which are not added typically correspond to bin
/// numbers where the truth can not be unfolded (either response
/// matrix is empty or the data do not constrain).
///
/// The RegularizeXXX() methods can be used to set up a custom matrix
/// of regularisation conditions. In this case, start with an empty
/// matrix L (argument regmode=kRegModeNone in the constructor)

Int_t TUnfoldV17::RegularizeDerivative(int left_bin, int right_bin,
                                   Double_t scale)
{
   // add regularisation on the difference of two bins
   //   left_bin: 1st bin
   //   right_bin: 2nd bin
   //   scale: size of the regularisation
   // return value: number of conditions which have been skipped
   // modifies data member fL

   if(fRegMode==kRegModeNone) fRegMode=kRegModeDerivative;
   if(fRegMode!=kRegModeDerivative) fRegMode=kRegModeMixed;

   return AddRegularisationCondition(left_bin,-scale,right_bin,scale) ? 0 : 1;
}

////////////////////////////////////////////////////////////////////////
/// add  a regularisation condition on the curvature of three truth bin
///
/// \param[in] left_bin bin number
/// \param[in] center_bin bin number
/// \param[in] right_bin bin number
/// \param[in] scale_left (default=1) scale factor
/// \param[in] scale_right (default=1) scale factor
///
/// this adds one row to L, where the element <b>left_bin</b> takes the
/// value <b>-scale_left</b>, the element  <b>right_bin</b> takes the
/// value <b>-scale_right</b> and the element  <b>center_bin</b> takes
/// the value <b>scale_left+scale_right</b> 
///
/// return value: 0 if ok, 1 if the condition has not been
/// added. Conditions which are not added typically correspond to bin
/// numbers where the truth can not be unfolded (either response
/// matrix is empty or the data do not constrain).
///
/// The RegularizeXXX() methods can be used to set up a custom matrix
/// of regularisation conditions. In this case, start with an empty
/// matrix L (argument regmode=kRegModeNone in the constructor)

Int_t TUnfoldV17::RegularizeCurvature(int left_bin, int center_bin,
                                  int right_bin,
                                  Double_t scale_left,
                                  Double_t scale_right)
{
   // add regularisation on the curvature through 3 bins (2nd derivative)
   //   left_bin: 1st bin
   //   center_bin: 2nd bin
   //   right_bin: 3rd bin
   //   scale_left: scale factor on center-left difference
   //   scale_right: scale factor on right-center difference
   // return value: number of conditions which have been skipped
   // modifies data member fL

   if(fRegMode==kRegModeNone) fRegMode=kRegModeCurvature;
   if(fRegMode!=kRegModeCurvature) fRegMode=kRegModeMixed;

   return AddRegularisationCondition
      (left_bin,-scale_left,
       center_bin,scale_left+scale_right,
       right_bin,-scale_right)
          ? 0 : 1;
}

////////////////////////////////////////////////////////////////////////
/// add  regularisation conditions for a group of bins
///
/// \param[in] start first bin number
/// \param[in] step step size
/// \param[in] nbin number of bins
/// \param[in] regmode regularisation mode (one of: kRegModeSize,
/// kRegModeDerivative, kRegModeCurvature)
///
/// add regularisation conditions for a group of equidistant
/// bins. There are <b>nbin</b> bins, starting with bin <b>start</b>
/// and with a distance of <b>step</b> between bins.
///
/// Return value: number of regularisation conditions which could not
/// be added.
/// <br/>
/// Conditions which are not added typically correspond to bin
/// numbers where the truth can not be unfolded (either response
/// matrix is empty or the data do not constrain).
///

Int_t TUnfoldV17::RegularizeBins(int start, int step, int nbin,
                             ERegMode regmode)
{
   // set regulatisation on a 1-dimensional curve
   //   start: first bin
   //   step:  distance between neighbouring bins
   //   nbin:  total number of bins
   //   regmode:  regularisation mode
   // return value:
   //   number of errors (i.e. conditions which have been skipped)
   // modifies data member fL

   Int_t i0, i1, i2;
   i0 = start;
   i1 = i0 + step;
   i2 = i1 + step;
   Int_t nSkip = 0;
   Int_t nError= 0;
   if (regmode == kRegModeDerivative) {
      nSkip = 1;
   } else if (regmode == kRegModeCurvature) {
      nSkip = 2;
   } else if(regmode != kRegModeSize) {
      Error("RegularizeBins","regmode = %d is not valid",regmode);
   }
   for (Int_t i = nSkip; i < nbin; i++) {
      if (regmode == kRegModeSize) {
         nError += RegularizeSize(i0);
      } else if (regmode == kRegModeDerivative) {
         nError += RegularizeDerivative(i0, i1);
      } else if (regmode == kRegModeCurvature) {
         nError += RegularizeCurvature(i0, i1, i2);
      }
      i0 = i1;
      i1 = i2;
      i2 += step;
   }
   return nError;
}

////////////////////////////////////////////////////////////////////////
/// add  regularisation conditions for 2d unfolding
///
/// \param[in] start_bin first bin number
/// \param[in] step1 step size, 1st dimension
/// \param[in] nbin1 number of bins, 1st dimension
/// \param[in] step2 step size, 2nd dimension
/// \param[in] nbin2 number of bins, 2nd dimension
/// \param[in] regmode regularisation mode (one of: kRegModeSize,
/// kRegModeDerivative, kRegModeCurvature)
///
/// add regularisation conditions for a grid of bins. The start bin is
/// <b>start_bin</b>. Along the first (second) dimension, there are 
/// <b>nbin1</b> (<b>nbin2</b>) bins and adjacent bins are spaced by 
/// <b>step1</b> (<b>step2</b>) units.
///
/// Return value: number of regularisation conditions which could not
/// be added. Conditions which are not added typically correspond to bin
/// numbers where the truth can not be unfolded (either response
/// matrix is empty or the data do not constrain).
///
Int_t TUnfoldV17::RegularizeBins2D(int start_bin, int step1, int nbin1,
                               int step2, int nbin2, ERegMode regmode)
{
   // set regularisation on a 2-dimensional grid of bins
   //     start_bin: first bin
   //     step1: distance between bins in 1st direction
   //     nbin1: number of bins in 1st direction
   //     step2: distance between bins in 2nd direction
   //     nbin2: number of bins in 2nd direction
    // return value:
   //   number of errors (i.e. conditions which have been skipped)
  // modifies data member fL

   Int_t nError = 0;
   for (Int_t i1 = 0; i1 < nbin1; i1++) {
      nError += RegularizeBins(start_bin + step1 * i1, step2, nbin2, regmode);
   }
   for (Int_t i2 = 0; i2 < nbin2; i2++) {
      nError += RegularizeBins(start_bin + step2 * i2, step1, nbin1, regmode);
   }
   return nError;
}

////////////////////////////////////////////////////////////////////////
/// perform the unfolding for a given input and regularisation
///
/// \param[in] tau_reg regularisation parameter
/// \param[in] input input distribution with uncertainties
/// \param[in] scaleBias (default=0.0) scale factor applied to the bias
///
/// This is a shortcut for { SetInput(input,scaleBias); DoUnfold(tau); }
Double_t TUnfoldV17::DoUnfold(Double_t tau_reg,const TH1 *input,
                           Double_t scaleBias)
{
// Data members required:
   //   fA, fX0, fL
   // Data members modified:
   //   those documented in SetInput()
   //   and those documented in DoUnfold(Double_t)
   // Return value:
   //   maximum global correlation coefficient
   //   NOTE!!! return value >=1.0 means error, and the result is junk
   //
   // Overflow bins of the input distribution are ignored!
   SetInput(input,scaleBias);
   return DoUnfold(tau_reg);
}

////////////////////////////////////////////////////////////////////////
/// Define input data for subsequent calls to DoUnfold(tau)
/// \param[in] input input distribution with uncertainties
/// \param[in] scaleBias (default=0) scale factor applied to the bias
/// \param[in] oneOverZeroError (default=0) for bins with zero error, this number defines 1/error.
/// \param[in] hist_vyy (default=0) if non-zero, this defines the data covariance matrix
/// \param[in] hist_vyy_inv (default=0) if non-zero and hist_vyy is
/// set, defines the inverse of the data covariance matrix. This
/// feature can be useful for repeated unfoldings in cases where the
/// inversion of the input covariance matrix is lengthy
///
/// Return value: nError1+10000*nError2
/// <ul>
/// <li>nError1: number of bins where the uncertainty is zero.
/// these bins either are not used for the unfolding (if
/// oneOverZeroError==0) or 1/uncertainty is set to oneOverZeroError.</li>
/// <li>nError2: return values>10000 are fatal errors, because the
/// unfolding can not be done. The number nError2 corresponds to the
/// number of truth bins which are not constrained by data points.
/// </li>
/// </ul>

Int_t TUnfoldV17::SetInput(const TH1 *input, Double_t scaleBias,
                        Double_t oneOverZeroError,const TH2 *hist_vyy,
                        const TH2 *hist_vyy_inv)
{
  // Data members modified:
  //   fY, fVyy, , fBiasScale
  // Data members cleared
  //   fVyyInv, fNdf
  //   + see ClearResults

  DeleteMatrix(&fVyyInv);
  fNdf=0;

  fBiasScale = scaleBias;

   // delete old results (if any)
  ClearResults();

  // construct error matrix and inverted error matrix of measured quantities
  // from errors of input histogram or use error matrix

  Int_t *rowVyyN=new Int_t[GetNy()*GetNy()+1];
  Int_t *colVyyN=new Int_t[GetNy()*GetNy()+1];
  Double_t *dataVyyN=new Double_t[GetNy()*GetNy()+1];

  Int_t *rowVyy1=new Int_t[GetNy()];
  Int_t *colVyy1=new Int_t[GetNy()];
  Double_t *dataVyy1=new Double_t[GetNy()];
  Double_t *dataVyyDiag=new Double_t[GetNy()];

  Int_t nVarianceZero=0;
  Int_t nVarianceForced=0;
  Int_t nVyyN=0;
  Int_t nVyy1=0;
  for (Int_t iy = 0; iy < GetNy(); iy++) {
     // diagonals
     Double_t dy2;
     if(!hist_vyy) {
        Double_t dy = input->GetBinError(iy + 1);
        dy2=dy*dy;
        if (dy2 <= 0.0) {
           if(oneOverZeroError>0.0) {
              dy2 = 1./ ( oneOverZeroError*oneOverZeroError);
              nVarianceForced++;
           } else {
              nVarianceZero++;
           }
        }
     } else {
        dy2 = hist_vyy->GetBinContent(iy+1,iy+1);
        if (dy2 <= 0.0) {
           nVarianceZero++;
        }
     }
     rowVyyN[nVyyN] = iy;
     colVyyN[nVyyN] = iy;
     rowVyy1[nVyy1] = iy;
     colVyy1[nVyy1] = 0;
     dataVyyDiag[iy] = dy2;
     if(dy2>0.0) {
        dataVyyN[nVyyN++] = dy2;
        dataVyy1[nVyy1++] = dy2;
     }
  }
  if(hist_vyy) {
     // non-diagonal elements
     Int_t nOffDiagNonzero=0;
     for (Int_t iy = 0; iy < GetNy(); iy++) {
        // ignore rows where the diagonal is zero
        if(dataVyyDiag[iy]<=0.0) {
           for (Int_t jy = 0; jy < GetNy(); jy++) {
              if(hist_vyy->GetBinContent(iy+1,jy+1)!=0.0) {
                 nOffDiagNonzero++;
              }
           }
           continue;
        }
        for (Int_t jy = 0; jy < GetNy(); jy++) {
           // skip diagonal elements
           if(iy==jy) continue;
           // ignore columns where the diagonal is zero
           if(dataVyyDiag[jy]<=0.0) continue;

           rowVyyN[nVyyN] = iy;
           colVyyN[nVyyN] = jy;
           dataVyyN[nVyyN]= hist_vyy->GetBinContent(iy+1,jy+1);
           if(dataVyyN[nVyyN] == 0.0) continue;
           nVyyN ++;
        }
     }
     if(hist_vyy_inv) {
        Warning("SetInput",
                "inverse of input covariance is taken from user input");
        Int_t *rowVyyInv=new Int_t[GetNy()*GetNy()+1];
        Int_t *colVyyInv=new Int_t[GetNy()*GetNy()+1];
        Double_t *dataVyyInv=new Double_t[GetNy()*GetNy()+1];
        Int_t nVyyInv=0;
        for (Int_t iy = 0; iy < GetNy(); iy++) {
           for (Int_t jy = 0; jy < GetNy(); jy++) {
              rowVyyInv[nVyyInv] = iy;
              colVyyInv[nVyyInv] = jy;
              dataVyyInv[nVyyInv]= hist_vyy_inv->GetBinContent(iy+1,jy+1);
              if(dataVyyInv[nVyyInv] == 0.0) continue;
              nVyyInv ++;
           }
        }
        fVyyInv=CreateSparseMatrix
           (GetNy(),GetNy(),nVyyInv,rowVyyInv,colVyyInv,dataVyyInv);
        delete [] rowVyyInv;
        delete [] colVyyInv;
        delete [] dataVyyInv;
     } else {
        if(nOffDiagNonzero) {
           Error("SetInput",
                 "input covariance has elements C(X,Y)!=0 where V(X)==0");
        }
     }
  }
  DeleteMatrix(&fVyy);
  fVyy = CreateSparseMatrix
     (GetNy(),GetNy(),nVyyN,rowVyyN,colVyyN,dataVyyN);

  delete[] rowVyyN;
  delete[] colVyyN;
  delete[] dataVyyN;

  TMatrixDSparse *vecV=CreateSparseMatrix
     (GetNy(),1,nVyy1,rowVyy1,colVyy1, dataVyy1);

  delete[] rowVyy1;
  delete[] colVyy1;
  delete[] dataVyy1;

  //
  // get input vector
  DeleteMatrix(&fY);
  fY = new TMatrixD(GetNy(), 1);
  for (Int_t i = 0; i < GetNy(); i++) {
     (*fY) (i, 0) = input->GetBinContent(i + 1);
  }
  // simple check whether unfolding is possible, given the matrices fA and  fV
  TMatrixDSparse *mAtV=MultiplyMSparseTranspMSparse(fA,vecV);
  DeleteMatrix(&vecV);
  Int_t nError2=0;
  for (Int_t i = 0; i <mAtV->GetNrows();i++) {
     if(mAtV->GetRowIndexArray()[i]==
        mAtV->GetRowIndexArray()[i+1]) {
        nError2 ++;
     }
  }
  if(nVarianceForced) {
     if(nVarianceForced>1) {
        Warning("SetInput","%d/%d input bins have zero error,"
                " 1/error set to %lf.",
                nVarianceForced,GetNy(),oneOverZeroError);
     } else {
        Warning("SetInput","One input bin has zero error,"
                " 1/error set to %lf.",oneOverZeroError);
     }
  }
  if(nVarianceZero) {
     if(nVarianceZero>1) {
        Warning("SetInput","%d/%d input bins have zero error,"
                " and are ignored.",nVarianceZero,GetNy());
     } else {
        Warning("SetInput","One input bin has zero error,"
                " and is ignored.");
     }
     fIgnoredBins=nVarianceZero;
  }
  if(nError2>0) {
     // check whether data points with zero error are responsible
     if(oneOverZeroError<=0.0) {
        //const Int_t *a_rows=fA->GetRowIndexArray();
        //const Int_t *a_cols=fA->GetColIndexArray();
        for (Int_t col = 0; col <mAtV->GetNrows();col++) {
           if(mAtV->GetRowIndexArray()[col]==
              mAtV->GetRowIndexArray()[col+1]) {
              TString binlist("no data to constrain output bin ");
              binlist += GetOutputBinName(fXToHist[col]);
              /* binlist +=" depends on ignored input bins ";
              for(Int_t row=0;row<fA->GetNrows();row++) {
                 if(dataVyyDiag[row]>0.0) continue;
                 for(Int_t i=a_rows[row];i<a_rows[row+1];i++) {
                    if(a_cols[i]!=col) continue;
                    binlist +=" ";
                    binlist +=row;
                 }
                 } */
              Warning("SetInput",binlist);
           }
        }
     }
     if(nError2>1) {
        Error("SetInput","%d/%d output bins are not constrained by any data.",
                nError2,mAtV->GetNrows());
     } else {
        Error("SetInput","One output bins is not constrained by any data.");
     }
  }
  DeleteMatrix(&mAtV);

  delete[] dataVyyDiag;

  return nVarianceForced+nVarianceZero+10000*nError2;
}

////////////////////////////////////////////////////////////////////////
/// perform the unfolding for a given regularisation parameter tau
///
/// \param[in] tau regularisation parameter
///
/// this method sets tau and then calls the core unfolding algorithm

Double_t TUnfoldV17::DoUnfold(Double_t tau)
{
   // required data members:
   //     fA:  matrix to relate x and y
   //     fY:  measured data points
   //     fX0: bias on x
   //     fBiasScale: scale factor for fX0
   //     fV:  inverse of covariance matrix for y
   //     fL: regularisation conditions
   // modified data members:
   //     fTauSquared and those documented in DoUnfold(void)
   fTauSquared=tau*tau;
   return DoUnfold();
}

////////////////////////////////////////////////////////////////////////
/// scan the L curve, determine tau and unfold at the final value of
/// tau
///
/// \param[in] nPoint number of points used for the scan
/// \param[in] tauMin smallest tau value to study
/// \param[in] tauMax largest tau value to study. If tauMin=tauMax=0,
/// a scan interval is determined automatically.
/// \param[out] lCurve if nonzero, a new TGraph is returned,
/// containing the L-curve
/// \param[out] logTauX if nonzero, a new TSpline is returned, to
/// parameterize the L-curve's x-coordinates as a function of log10(tau)
/// \param[out] logTauY if nonzero, a new TSpline is returned, to
/// parameterize the L-curve's y-coordinates as a function of log10(tau)
/// \param[out] logTauCurvature if nonzero, a new TSpline is returned
/// of the L-curve curvature as a function of log10(tau)
///
/// return value: the coordinate number in the logTauX,logTauY graphs
/// corresponding to the "final" choice of tau
///
/// Recommendation: always check <b>logTauCurvature</b>, it
/// should be a peaked function (similar to a Gaussian), the maximum
/// corresponding to the final choice of tau. Also, check the <b>lCurve</b>
/// it should be approximately L-shaped. If in doubt, adjust tauMin
/// and tauMax until the results are satisfactory.

Int_t TUnfoldV17::ScanLcurve(Int_t nPoint,
                          Double_t tauMin,Double_t tauMax,
           TGraph **lCurve,TSpline **logTauX,
                             TSpline **logTauY,TSpline **logTauCurvature)
{
  typedef std::map<Double_t,std::pair<Double_t,Double_t> > XYtau_t;
  XYtau_t curve;

  //==========================================================
  // algorithm:
  //  (1) do the unfolding for nPoint-1 points
  //      and store the results in the map
  //        curve
  //    (1a) store minimum and maximum tau to curve
  //    (1b) insert additional points, until nPoint-1 values
  //          have been calculated
  //
  //  (2) determine the best choice of tau
  //      do the unfolding for this point
  //      and store the result in
  //        curve
  //  (3) return the result in
  //       lCurve logTauX logTauY

  //==========================================================
  //  (1) do the unfolding for nPoint-1 points
  //      and store the results in
  //        curve
  //    (1a) store minimum and maximum tau to curve

  if((tauMin<=0)||(tauMax<=0.0)||(tauMin>=tauMax)) {
     // here no range is given, has to be determined automatically
     // the maximum tau is determined from the chi**2 values 
     // observed from unfolding without regulatisation

     // first unfolding, without regularisation
     DoUnfold(0.0);

     // if the number of degrees of freedom is too small, create an error
     if(GetNdf()<=0) {
        Error("ScanLcurve","too few input bins, NDF<=0 %d",GetNdf());  
     }

     Double_t x0=GetLcurveX();
     Double_t y0=GetLcurveY();
     Info("ScanLcurve","logtau=-Infinity X=%lf Y=%lf",x0,y0);
     if(!finite(x0)) {
        Fatal("ScanLcurve","problem (too few input bins?) X=%f",x0);
     }
     if(!finite(y0)) {
        Fatal("ScanLcurve","problem (missing regularisation?) Y=%f",y0);
     }
     {
        // unfolding guess maximum tau and store it
        Double_t logTau=
           0.5*(TMath::Log10(fChi2A+3.*TMath::Sqrt(GetNdf()+1.0))
                -GetLcurveY());
        DoUnfold(TMath::Power(10.,logTau));
        if((!finite(GetLcurveX())) ||(!finite(GetLcurveY()))) {
           Fatal("ScanLcurve","problem (missing regularisation?) X=%f Y=%f",
                 GetLcurveX(),GetLcurveY());
        }
        curve[logTau]=std::make_pair(GetLcurveX(),GetLcurveY());
        Info("ScanLcurve","logtau=%lf X=%lf Y=%lf",
             logTau,GetLcurveX(),GetLcurveY());
     }
     if((*curve.begin()).second.first<x0) {
        // if the point at tau==0 seems numerically unstable,
        // try to find the minimum chi**2 as start value
        //
        // "unstable" means that there is a finite tau where the
        // unfolding chi**2 is smaller than for the case of no
        // regularisation. Ideally this should never happen
        do {
           x0=GetLcurveX();
           Double_t logTau=(*curve.begin()).first-0.5;
           DoUnfold(TMath::Power(10.,logTau));
           if((!finite(GetLcurveX())) ||(!finite(GetLcurveY()))) {
              Fatal("ScanLcurve","problem (missing regularisation?) X=%f Y=%f",
                    GetLcurveX(),GetLcurveY());
           }
           curve[logTau]=std::make_pair(GetLcurveX(),GetLcurveY());
           Info("ScanLcurve","logtau=%lf X=%lf Y=%lf",
                logTau,GetLcurveX(),GetLcurveY());
        }
        while(((int)curve.size()<(nPoint-1)/2)&&
              ((*curve.begin()).second.first<x0));
     } else {
        // minimum tau is chosen such that is less than
        // 1% different from the case of no regularusation
        // log10(1.01) = 0.00432

        // here, more than one point are inserted if necessary
        while(((int)curve.size()<nPoint-1)&&
              (((*curve.begin()).second.first-x0>0.00432)||
               ((*curve.begin()).second.second-y0>0.00432)||
               (curve.size()<2))) {
           Double_t logTau=(*curve.begin()).first-0.5;
           DoUnfold(TMath::Power(10.,logTau));
           if((!finite(GetLcurveX())) ||(!finite(GetLcurveY()))) {
              Fatal("ScanLcurve","problem (missing regularisation?) X=%f Y=%f",
                    GetLcurveX(),GetLcurveY());
           }
           curve[logTau]=std::make_pair(GetLcurveX(),GetLcurveY());
           Info("ScanLcurve","logtau=%lf X=%lf Y=%lf",
                logTau,GetLcurveX(),GetLcurveY());
        }
     }
  } else {
     Double_t logTauMin=TMath::Log10(tauMin);
     Double_t logTauMax=TMath::Log10(tauMax);
     if(nPoint>1) {
        // insert maximum tau
        DoUnfold(TMath::Power(10.,logTauMax));
        if((!finite(GetLcurveX())) ||(!finite(GetLcurveY()))) {
           Fatal("ScanLcurve","problem (missing regularisation?) X=%f Y=%f",
                 GetLcurveX(),GetLcurveY());
        }
        Info("ScanLcurve","logtau=%lf X=%lf Y=%lf",
             logTauMax,GetLcurveX(),GetLcurveY());
        curve[logTauMax]=std::make_pair(GetLcurveX(),GetLcurveY());
     }
     // insert minimum tau
     DoUnfold(TMath::Power(10.,logTauMin));
     if((!finite(GetLcurveX())) ||(!finite(GetLcurveY()))) {
        Fatal("ScanLcurve","problem (missing regularisation?) X=%f Y=%f",
              GetLcurveX(),GetLcurveY());
     }
     Info("ScanLcurve","logtau=%lf X=%lf Y=%lf",
          logTauMin,GetLcurveX(),GetLcurveY());
     curve[logTauMin]=std::make_pair(GetLcurveX(),GetLcurveY());
  }


  //==========================================================
  //    (1b) insert additional points, until nPoint-1 values
  //          have been calculated

  while(int(curve.size())<nPoint-1) {
    // insert additional points, such that the sizes of the delta(XY) vectors
    // are getting smaller and smaller
    XYtau_t::const_iterator i0,i1;
    i0=curve.begin();
    i1=i0;
    Double_t logTau=(*i0).first;
    Double_t distMax=0.0;
    for(i1++;i1!=curve.end();i1++) {
      const std::pair<Double_t,Double_t> &xy0=(*i0).second;
      const std::pair<Double_t,Double_t> &xy1=(*i1).second;
      Double_t dx=xy1.first-xy0.first;
      Double_t dy=xy1.second-xy0.second;
      Double_t d=TMath::Sqrt(dx*dx+dy*dy);
      if(d>=distMax) {
        distMax=d;
        logTau=0.5*((*i0).first+(*i1).first);
      }
      i0=i1;
    }
    DoUnfold(TMath::Power(10.,logTau));
    if((!finite(GetLcurveX())) ||(!finite(GetLcurveY()))) {
       Fatal("ScanLcurve","problem (missing regularisation?) X=%f Y=%f",
             GetLcurveX(),GetLcurveY());
    }
    Info("ScanLcurve","logtau=%lf X=%lf Y=%lf",logTau,GetLcurveX(),GetLcurveY());
    curve[logTau]=std::make_pair(GetLcurveX(),GetLcurveY());
  }

  //==========================================================
  //  (2) determine the best choice of tau
  //      do the unfolding for this point
  //      and store the result in
  //        curve
  XYtau_t::const_iterator i0,i1;
  i0=curve.begin();
  i1=i0;
  i1++;
  Double_t logTauFin=(*i0).first;
  if( ((int)curve.size())<nPoint) {
    // set up splines and determine (x,y) curvature in each point
    Double_t *cTi=new Double_t[curve.size()-1];
    Double_t *cCi=new Double_t[curve.size()-1];
    Int_t n=0;
    {
      Double_t *lXi=new Double_t[curve.size()];
      Double_t *lYi=new Double_t[curve.size()];
      Double_t *lTi=new Double_t[curve.size()];
      for( XYtau_t::const_iterator i=curve.begin();i!=curve.end();i++) {
        lXi[n]=(*i).second.first;
        lYi[n]=(*i).second.second;
        lTi[n]=(*i).first;
        n++;
      }
      TSpline3 *splineX=new TSpline3("x vs tau",lTi,lXi,n);
      TSpline3 *splineY=new TSpline3("y vs tau",lTi,lYi,n);
      // calculate (x,y) curvature for all points
      // the curvature is stored in the array cCi[] as a function of cTi[] 
      for(Int_t i=0;i<n-1;i++) {
        Double_t ltau,xy,bi,ci,di;
        splineX->GetCoeff(i,ltau,xy,bi,ci,di);
        Double_t tauBar=0.5*(lTi[i]+lTi[i+1]);
        Double_t dTau=0.5*(lTi[i+1]-lTi[i]);
        Double_t dx1=bi+dTau*(2.*ci+3.*di*dTau);
        Double_t dx2=2.*ci+6.*di*dTau;
        splineY->GetCoeff(i,ltau,xy,bi,ci,di);
        Double_t dy1=bi+dTau*(2.*ci+3.*di*dTau);
        Double_t dy2=2.*ci+6.*di*dTau;
        cTi[i]=tauBar;
        cCi[i]=(dy2*dx1-dy1*dx2)/TMath::Power(dx1*dx1+dy1*dy1,1.5);
      }
      delete splineX;
      delete splineY;
      delete[] lXi;
      delete[] lYi;
      delete[] lTi;
    }
    // create curvature Spline
    TSpline3 *splineC=new TSpline3("L curve curvature",cTi,cCi,n-1);
    // find the maximum of the curvature
    // if the parameter iskip is non-zero, then iskip points are
    // ignored when looking for the largest curvature
    // (there are problems with the curvature determined from the first
    //  few points of splineX,splineY in the algorithm above)
    Int_t iskip=0;
    if(n>4) iskip=1;
    if(n>7) iskip=2;
    Double_t cCmax=cCi[iskip];
    Double_t cTmax=cTi[iskip];
    for(Int_t i=iskip;i<n-2-iskip;i++) {
      // find maximum on this spline section
      // check boundary conditions for x[i+1]
      Double_t xMax=cTi[i+1];
      Double_t yMax=cCi[i+1];
      if(cCi[i]>yMax) {
        yMax=cCi[i];
        xMax=cTi[i];
      }
      // find maximum for x[i]<x<x[i+1]
      // get spline coefficiencts and solve equation
      //   derivative(x)==0
      Double_t x,y,b,c,d;
      splineC->GetCoeff(i,x,y,b,c,d);
      // coefficiencts of quadratic equation
      Double_t m_p_half=-c/(3.*d);
      Double_t q=b/(3.*d);
      Double_t discr=m_p_half*m_p_half-q;
      if(discr>=0.0) {
        // solution found
        discr=TMath::Sqrt(discr);
        Double_t xx;
        if(m_p_half>0.0) {
          xx = m_p_half + discr;
        } else {
          xx = m_p_half - discr;
        }
        Double_t dx=cTi[i+1]-x;
        // check first solution
        if((xx>0.0)&&(xx<dx)) {
          y=splineC->Eval(x+xx);
          if(y>yMax) {
            yMax=y;
            xMax=x+xx;
          }
        }
        // second solution
        if(xx !=0.0) {
          xx= q/xx;
        } else {
          xx=0.0;
        }
        // check second solution
        if((xx>0.0)&&(xx<dx)) {
          y=splineC->Eval(x+xx);
          if(y>yMax) {
            yMax=y;
            xMax=x+xx;
          }
        }
      }
      // check whether this local minimum is a global minimum
      if(yMax>cCmax) {
        cCmax=yMax;
        cTmax=xMax;
      }
    }
    if(logTauCurvature) {
       *logTauCurvature=splineC;
    } else {
       delete splineC;
    }
    delete[] cTi;
    delete[] cCi;
    logTauFin=cTmax;
    DoUnfold(TMath::Power(10.,logTauFin));
    if((!finite(GetLcurveX())) ||(!finite(GetLcurveY()))) {
       Fatal("ScanLcurve","problem (missing regularisation?) X=%f Y=%f",
             GetLcurveX(),GetLcurveY());
    }
    Info("ScanLcurve","Result logtau=%lf X=%lf Y=%lf",
         logTauFin,GetLcurveX(),GetLcurveY());
    curve[logTauFin]=std::make_pair(GetLcurveX(),GetLcurveY());
  }


  //==========================================================
  //  (3) return the result in
  //       lCurve logTauX logTauY

  Int_t bestChoice=-1;
  if(curve.size()>0) {
    Double_t *x=new Double_t[curve.size()];
    Double_t *y=new Double_t[curve.size()];
    Double_t *logT=new Double_t[curve.size()];
    int n=0;
    for( XYtau_t::const_iterator i=curve.begin();i!=curve.end();i++) {
      if(logTauFin==(*i).first) {
        bestChoice=n;
      }
      x[n]=(*i).second.first;
      y[n]=(*i).second.second;
      logT[n]=(*i).first;
      n++;
    }
    if(lCurve) {
       (*lCurve)=new TGraph(n,x,y); 
       (*lCurve)->SetTitle("L curve");
   }
    if(logTauX) (*logTauX)=new TSpline3("log(chi**2)%log(tau)",logT,x,n);
    if(logTauY) (*logTauY)=new TSpline3("log(reg.cond)%log(tau)",logT,y,n);
    delete[] x;
    delete[] y;
    delete[] logT;
  }

  return bestChoice;
}

////////////////////////////////////////////////////////////////////////
/// histogram of truth bins, determined from suming over the response matrix
///
/// \param[out] out histogram to store the truth bins. The bin contents
/// are overwritten
/// \param[in] binMap (default=0) array for mapping truth bins to histogram bins
///
/// This vector is also used to initialize the bias
/// x<sub>0</sub>. However, the bias vector may be changed using the
/// SetBias() method.
///
/// The use of <b>binMap</b> is explained with the documentation of
/// the GetOutput() method
///

void TUnfoldV17::GetNormalisationVector(TH1 *out,const Int_t *binMap) const
{

   ClearHistogram(out);
   for (Int_t i = 0; i < GetNx(); i++) {
      int dest=binMap ? binMap[fXToHist[i]] : fXToHist[i];
      if(dest>=0) {
         out->SetBinContent(dest, fSumOverY[i] + out->GetBinContent(dest));
      }
   }
}

////////////////////////////////////////////////////////////////////////
/// get bias vector including bias scale
///
/// \param[out] out histogram to store the scaled bias vector. The bin
/// contents are overwritten
/// \param[in] binMap (default=0) array for mapping truth bins to histogram bins
///
/// This method returns the bias vector times scaling factor, f*x<sub>0</sub>
///
/// The use of <b>binMap</b> is explained with the documentation of
/// the GetOutput() method
///

void TUnfoldV17::GetBias(TH1 *out,const Int_t *binMap) const
{

   ClearHistogram(out);
   for (Int_t i = 0; i < GetNx(); i++) {
      int dest=binMap ? binMap[fXToHist[i]] : fXToHist[i];
      if(dest>=0) {
         out->SetBinContent(dest, fBiasScale*(*fX0) (i, 0) +
                            out->GetBinContent(dest));
      }
   }
}

////////////////////////////////////////////////////////////////////////
/// get unfolding result on detector level
///
/// \param[out] out histogram to store the correlation coefficiencts. The bin
/// contents and errors are overwritten.
/// \param[in] binMap (default=0) array for mapping truth bins to histogram bins
///
/// This method returns the unfolding output folded by the response
/// matrix, i.e. the vector Ax.
///
/// The use of <b>binMap</b> is explained with the documentation of
/// the GetOutput() method
///

void TUnfoldV17::GetFoldedOutput(TH1 *out,const Int_t *binMap) const
{
   ClearHistogram(out);
  
   TMatrixDSparse *AVxx=MultiplyMSparseMSparse(fA,fVxx);

   const Int_t *rows_A=fA->GetRowIndexArray();
   const Int_t *cols_A=fA->GetColIndexArray();
   const Double_t *data_A=fA->GetMatrixArray();
   const Int_t *rows_AVxx=AVxx->GetRowIndexArray();
   const Int_t *cols_AVxx=AVxx->GetColIndexArray();
   const Double_t *data_AVxx=AVxx->GetMatrixArray();
   
   for (Int_t i = 0; i < GetNy(); i++) {
      Int_t destI = binMap ? binMap[i+1] : i+1;
      if(destI<0) continue;

      out->SetBinContent(destI, (*fAx) (i, 0)+ out->GetBinContent(destI));
      Double_t e2=0.0;
      Int_t index_a=rows_A[i];
      Int_t index_av=rows_AVxx[i];
      while((index_a<rows_A[i+1])&&(index_av<rows_AVxx[i+1])) {
         Int_t j_a=cols_A[index_a];
         Int_t j_av=cols_AVxx[index_av];
         if(j_a<j_av) {
            index_a++;
         } else if(j_a>j_av) {
            index_av++;
         } else {
            e2 += data_AVxx[index_av] * data_A[index_a];
            index_a++;
            index_av++;
         }
      }
      out->SetBinError(destI,TMath::Sqrt(e2));
   }
   DeleteMatrix(&AVxx);
}

////////////////////////////////////////////////////////////////////////
/// get matrix of probabilities
///
/// \param[out] A two-dimensional histogram to store the
/// probabilities (normalized response matrix). The bin contents are
/// overwritten
/// \param[in] histmap specify axis along which the truth bins are
/// oriented

void TUnfoldV17::GetProbabilityMatrix(TH2 *A,EHistMap histmap) const
{
   // retreive matrix of probabilities
   //    histmap: on which axis to arrange the input/output vector
   //    A: histogram to store the probability matrix
   const Int_t *rows_A=fA->GetRowIndexArray();
   const Int_t *cols_A=fA->GetColIndexArray();
   const Double_t *data_A=fA->GetMatrixArray();
   for (Int_t iy = 0; iy <fA->GetNrows(); iy++) {
      for(Int_t indexA=rows_A[iy];indexA<rows_A[iy+1];indexA++) {
         Int_t ix = cols_A[indexA];
         Int_t ih=fXToHist[ix];
         if (histmap == kHistMapOutputHoriz) {
            A->SetBinContent(ih, iy+1,data_A[indexA]);
         } else {
            A->SetBinContent(iy+1, ih,data_A[indexA]);
         }
      }
   }
}

////////////////////////////////////////////////////////////////////////
/// Input vector of measurements
///
/// \param[out] out histogram to store the measurements. Bin content
/// and bin errors are overwritte.
/// \param[in] binMap (default=0) array for mapping truth bins to histogram bins
///
/// Bins which had an uncertainty of zero in the call to SetInput()
/// maye acquire bin contents or bin errors different from the
/// original settings in SetInput().
///
/// The use of <b>binMap</b> is explained with the documentation of
/// the GetOutput() method
///

void TUnfoldV17::GetInput(TH1 *out,const Int_t *binMap) const
{
   ClearHistogram(out);

   const Int_t *rows_Vyy=fVyy->GetRowIndexArray();
   const Int_t *cols_Vyy=fVyy->GetColIndexArray();
   const Double_t *data_Vyy=fVyy->GetMatrixArray();

   for (Int_t i = 0; i < GetNy(); i++) {
      Int_t destI=binMap ? binMap[i+1] : i+1;
      if(destI<0) continue;

      out->SetBinContent(destI, (*fY) (i, 0)+out->GetBinContent(destI));

      Double_t e=0.0;
      for(int index=rows_Vyy[i];index<rows_Vyy[i+1];index++) {
         if(cols_Vyy[index]==i) {
            e=TMath::Sqrt(data_Vyy[index]);
         }
      }
      out->SetBinError(destI, e);
   }
}

////////////////////////////////////////////////////////////////////////
/// get inverse of the measurement's covariance matrix
///
/// \param[out] out histogram to store the inverted covariance

void TUnfoldV17::GetInputInverseEmatrix(TH2 *out)
{
   // calculate the inverse of the contribution to the error matrix 
   // corresponding to the input data
   if(!fVyyInv) {
      Int_t rank=0;
      fVyyInv=InvertMSparseSymmPos(fVyy,&rank);
      // and count number of degrees of freedom
      fNdf = rank-GetNpar();

      if(rank<GetNy()-fIgnoredBins) {
         Warning("GetInputInverseEmatrix","input covariance matrix has rank %d expect %d",
                 rank,GetNy());
      }
      if(fNdf<0) {
         Error("GetInputInverseEmatrix","number of parameters %d > %d (rank of input covariance). Problem can not be solved",GetNpar(),rank);
      } else if(fNdf==0) {
         Warning("GetInputInverseEmatrix","number of parameters %d = input rank %d. Problem is ill posed",GetNpar(),rank);
      }
   }
   if(out) {
      // return matrix as histogram
      const Int_t *rows_VyyInv=fVyyInv->GetRowIndexArray();
      const Int_t *cols_VyyInv=fVyyInv->GetColIndexArray();
      const Double_t *data_VyyInv=fVyyInv->GetMatrixArray();

      for(int i=0;i<=out->GetNbinsX()+1;i++) {
         for(int j=0;j<=out->GetNbinsY()+1;j++) {
            out->SetBinContent(i,j,0.);
         }
      }

      for (Int_t i = 0; i < fVyyInv->GetNrows(); i++) {
         for(int index=rows_VyyInv[i];index<rows_VyyInv[i+1];index++) {
            Int_t j=cols_VyyInv[index];
            out->SetBinContent(i+1,j+1,data_VyyInv[index]);
         }
      }
   }
}

////////////////////////////////////////////////////////////////////////
/// get matrix of regularisation conditions squared
///
/// \param[out] out histogram to store the squared matrix of
/// regularisation conditions. the bin contents are overwritten
///
/// This returns the square matrix L<sup>T</sup>L as a histogram
///
/// The histogram should have dimension nx times nx, where nx
/// corresponds to the number of histogram bins in the response matrix
/// along the truth axis.

void TUnfoldV17::GetLsquared(TH2 *out) const
{
   // retreive matrix of regularisation conditions squared
   //   out: prebooked matrix

   TMatrixDSparse *lSquared=MultiplyMSparseTranspMSparse(fL,fL);
   // loop over sparse matrix
   const Int_t *rows=lSquared->GetRowIndexArray();
   const Int_t *cols=lSquared->GetColIndexArray();
   const Double_t *data=lSquared->GetMatrixArray();
   for (Int_t i = 0; i < GetNx(); i++) {
      for (Int_t cindex = rows[i]; cindex < rows[i+1]; cindex++) {
        Int_t j=cols[cindex];
        out->SetBinContent(fXToHist[i], fXToHist[j],data[cindex]);
      }
   }
   DeleteMatrix(&lSquared);
}

////////////////////////////////////////////////////////////////////////
/// get number of regularisation conditions
///
/// Ths returns the number of regularisation conditions, useful for
/// booking a histogram for a subsequent call of GetL().

Int_t TUnfoldV17::GetNr(void) const {
   return fL->GetNrows();
}

////////////////////////////////////////////////////////////////////////
/// get matrix of regularisation conditions
///
/// \param[out] out histogram to store the regularisation conditions.
/// the bincontents are overwritten
///
/// The histogram should have dimension nr (x-axis) times nx (y-axis).
/// nr corresponds to the number of regularisation conditions, it can
/// be obtained using the method GetNr(). nx corresponds to the number
/// of histogram bins in the response matrix along the truth axis.

void TUnfoldV17::GetL(TH2 *out) const
{
   // loop over sparse matrix
   const Int_t *rows=fL->GetRowIndexArray();
   const Int_t *cols=fL->GetColIndexArray();
   const Double_t *data=fL->GetMatrixArray();
   for (Int_t row = 0; row < GetNr(); row++) {
      for (Int_t cindex = rows[row]; cindex < rows[row+1]; cindex++) {
        Int_t col=cols[cindex];
        Int_t indexH=fXToHist[col];
        out->SetBinContent(indexH,row+1,data[cindex]);
      }
   }
}

////////////////////////////////////////////////////////////////////////
/// set type of area constraint
///
/// results of a previous unfolding are reset

void TUnfoldV17::SetConstraint(EConstraint constraint)
{
   // set type of constraint for the next unfolding
   if(fConstraint !=constraint) ClearResults();
   fConstraint=constraint;
   Info("SetConstraint","fConstraint=%d",fConstraint);
}


////////////////////////////////////////////////////////////////////////
/// return regularisation parameter
///
Double_t TUnfoldV17::GetTau(void) const
{
   // return regularisation parameter
   return TMath::Sqrt(fTauSquared);
}

////////////////////////////////////////////////////////////////////////
/// get &chi;<sup>2</sup><sub>L</sub> contribution determined in recent unfolding
Double_t TUnfoldV17::GetChi2L(void) const
{
   // return chi**2 contribution from regularisation conditions
   return fLXsquared*fTauSquared;
}

////////////////////////////////////////////////////////////////////////
/// get number of truth parameters determined in recent unfolding
///
/// empty bins of the response matrix or bins which can not be
/// unfolded due to rank deficits are not counted
Int_t TUnfoldV17::GetNpar(void) const
{
   return GetNx();
}

////////////////////////////////////////////////////////////////////////
/// get value on x-axis of L-curve determined in recent unfolding
///
/// x=log<sub>10</sub>(GetChi2A())
Double_t TUnfoldV17::GetLcurveX(void) const
{
  return TMath::Log10(fChi2A);
}

////////////////////////////////////////////////////////////////////////
/// get value on y-axis of L-curve determined in recent unfolding
///
/// y=log<sub>10</sub>(GetChi2L())
Double_t TUnfoldV17::GetLcurveY(void) const
{
  return TMath::Log10(fLXsquared);
}

////////////////////////////////////////////////////////////////////////
/// get output distribution, possibly cumulated over several bins
///
/// \param[out] output existing output histogram. content and errors
/// will be updated.
/// \param[in] binMap (default=0) array for mapping truth bins to histogram bins
///
/// If nonzero, the array <b>binMap</b> must have dimension n+2, where n
/// corresponds to the number of bins on the truth axis of the response
/// matrix (the histogram specified with the TUnfold
/// constructor). The indexes of <b>binMap</b> correspond to the truth
/// bins (including underflow and overflow) of the response matrix.
/// The element binMap[i] specifies the histogram number in
/// <b>output</b> where the corresponding truth bin will be stored. It is
/// possible to specify the same <b>output</b> bin number for multiple
/// indexes, in which case these bins are added. Set binMap[i]=-1 to
/// ignore an unfolded truth bin. The uncertainties are
/// calculated from the corresponding parts of the covariance matrix,
/// properly taking care of added truth bins.
/// <br/>
/// If the pointer <b>binMap</b> is zero, the bins are mapped
/// one-to-one. Truth bin zero (underflow) is stored in the
/// <b>output</b> underflow, truth bin 1 is stored in bin number 1, etc.

void TUnfoldV17::GetOutput(TH1 *output,const Int_t *binMap) const
{
   // 
   //   output: output histogram
   //   binMap: for each bin of the original output distribution
   //           specify the destination bin. A value of -1 means that the bin
   //           is discarded. 0 means underflow bin, 1 first bin, ...
   //        binMap[0] : destination of underflow bin
   //        binMap[1] : destination of first bin
   //          ...
   
   ClearHistogram(output);
   /* Int_t nbin=output->GetNbinsX();
   Double_t *c=new Double_t[nbin+2];
   Double_t *e2=new Double_t[nbin+2];
   for(Int_t i=0;i<nbin+2;i++) {
      c[i]=0.0;
      e2[i]=0.0;
      } */

   std::map<Int_t,Double_t> e2;

   const Int_t *rows_Vxx=fVxx->GetRowIndexArray();
   const Int_t *cols_Vxx=fVxx->GetColIndexArray();
   const Double_t *data_Vxx=fVxx->GetMatrixArray();

   Int_t binMapSize = fHistToX.GetSize();
   for(Int_t i=0;i<binMapSize;i++) {
      Int_t destBinI=binMap ? binMap[i] : i; // histogram bin
      Int_t srcBinI=fHistToX[i]; // matrix row index
      if((destBinI>=0)&&(srcBinI>=0)) {
         output->SetBinContent
            (destBinI, (*fX)(srcBinI,0)+ output->GetBinContent(destBinI));
         // here we loop over the columns of the error matrix
         //   j: counts histogram bins
         //   index: counts sparse matrix index
         // the algorithm makes use of the fact that fHistToX is ordered
         Int_t j=0; // histogram bin
         Int_t index_vxx=rows_Vxx[srcBinI];
         //Double_t e2=TMath::Power(output->GetBinError(destBinI),2.);
         while((j<binMapSize)&&(index_vxx<rows_Vxx[srcBinI+1])) {
            Int_t destBinJ=binMap ? binMap[j] : j; // histogram bin
            if(destBinI!=destBinJ) {
               // only diagonal elements are calculated
               j++;
            } else {
               Int_t srcBinJ=fHistToX[j]; // matrix column index
               if(srcBinJ<0) {
                  // bin was not unfolded, check next bin
                  j++;
               } else {
                  if(cols_Vxx[index_vxx]<srcBinJ) {
                     // index is too small
                     index_vxx++;
                  } else if(cols_Vxx[index_vxx]>srcBinJ) {
                     // index is too large, skip bin
                     j++;
                  } else {
                     // add this bin
                     e2[destBinI] += data_Vxx[index_vxx];
                     j++;
                     index_vxx++;
                  }
               }
            }
         }
         //output->SetBinError(destBinI,TMath::Sqrt(e2));
      }
   }
   for(std::map<Int_t,Double_t>::const_iterator i=e2.begin();
       i!=e2.end();i++) {
      //cout<<(*i).first<<" "<<(*i).second<<"\n";
      output->SetBinError((*i).first,TMath::Sqrt((*i).second));
   }
}

////////////////////////////////////////////////////////////////////////
/// add up an error matrix, also respecting the bin mapping
///
/// \param[inout] ematrix error matrix histogram
/// \param[in] emat error matrix stored with internal mapping (member fXToHist)
/// \param[in] binMap mapping of histogram bins
/// \param[in] doClear if true, ematrix is cleared prior to adding
/// elements of emat to it.
///
/// the array <b>binMap</b> is explained with the method GetOutput(). The
/// matrix emat must have dimension NxN where N=fXToHist.size()
/// The flag <b>doClear</b> may be used to add covariance matrices from
/// several uncertainty sources.
/// 
void TUnfoldV17::ErrorMatrixToHist(TH2 *ematrix,const TMatrixDSparse *emat,
                                const Int_t *binMap,Bool_t doClear) const
{
   Int_t nbin=ematrix->GetNbinsX();
   if(doClear) {
      for(Int_t i=0;i<nbin+2;i++) {
         for(Int_t j=0;j<nbin+2;j++) {
            ematrix->SetBinContent(i,j,0.0);
            ematrix->SetBinError(i,j,0.0);
         }
      }
   }

   if(emat) {
      const Int_t *rows_emat=emat->GetRowIndexArray();
      const Int_t *cols_emat=emat->GetColIndexArray();
      const Double_t *data_emat=emat->GetMatrixArray();

      Int_t binMapSize = fHistToX.GetSize();
      for(Int_t i=0;i<binMapSize;i++) {
         Int_t destBinI=binMap ? binMap[i] : i;
         Int_t srcBinI=fHistToX[i];
         if((destBinI>=0)&&(destBinI<nbin+2)&&(srcBinI>=0)) {
            // here we loop over the columns of the source matrix
            //   j: counts histogram bins
            //   index: counts sparse matrix index
            // the algorithm makes use of the fact that fHistToX is ordered
            Int_t j=0;
            Int_t index_vxx=rows_emat[srcBinI];
            while((j<binMapSize)&&(index_vxx<rows_emat[srcBinI+1])) {
               Int_t destBinJ=binMap ? binMap[j] : j;
               Int_t srcBinJ=fHistToX[j];
               if((destBinJ<0)||(destBinJ>=nbin+2)||(srcBinJ<0)) {
                  // check next bin
                  j++;
               } else {
                  if(cols_emat[index_vxx]<srcBinJ) {
                     // index is too small
                     index_vxx++;
                  } else if(cols_emat[index_vxx]>srcBinJ) {
                     // index is too large, skip bin
                     j++;
                  } else {
                     // add this bin
                     Double_t e2= ematrix->GetBinContent(destBinI,destBinJ)
                        + data_emat[index_vxx];
                     ematrix->SetBinContent(destBinI,destBinJ,e2);
                     j++;
                     index_vxx++;
                  }
               }
            }
         }
      }
   }
}

////////////////////////////////////////////////////////////////////////
/// get output covariance matrix, possibly cumulated over several bins
///
/// \param[out] ematrix histogram to store the covariance. The bin
/// contents are overwritten.
/// \param[in] binMap (default=0) array for mapping truth bins to histogram bins
///
/// The use of <b>binMap</b> is explained with the documentation of
/// the GetOutput() method

void TUnfoldV17::GetEmatrix(TH2 *ematrix,const Int_t *binMap) const
{
   ErrorMatrixToHist(ematrix,fVxx,binMap,kTRUE);
}

////////////////////////////////////////////////////////////////////////
/// get correlation coefficiencts, possibly cumulated over several bins
///
/// \param[out] rhoij histogram to store the correlation coefficiencts. The bin
/// contents are overwritten.
/// \param[in] binMap (default=0) array for mapping truth bins to histogram bins
///
/// The use of <b>binMap</b> is explained with the documentation of
/// the GetOutput() method

void TUnfoldV17::GetRhoIJ(TH2 *rhoij,const Int_t *binMap) const
{
   GetEmatrix(rhoij,binMap);
   Int_t nbin=rhoij->GetNbinsX();  
   Double_t *e=new Double_t[nbin+2];
   for(Int_t i=0;i<nbin+2;i++) {
      e[i]=TMath::Sqrt(rhoij->GetBinContent(i,i));
   }
   for(Int_t i=0;i<nbin+2;i++) {
      for(Int_t j=0;j<nbin+2;j++) {
         if((e[i]>0.0)&&(e[j]>0.0)) {
            rhoij->SetBinContent(i,j,rhoij->GetBinContent(i,j)/e[i]/e[j]);
         } else {
            rhoij->SetBinContent(i,j,0.0);
         }
      }
   }
   delete[] e;
}

////////////////////////////////////////////////////////////////////////
/// get global correlation coefficiencts, possibly cumulated over several bins
///
/// \param[out] rhoi histogram to store the global correlation
/// coefficients. The bin contents are overwritten.
/// \param[in] binMap (default=0) array for mapping truth bins to
/// histogram bins
/// \param[out] invEmat (default=0) histogram to store the inverted
/// covariance matrix
///
/// for a given bin, the global correlation coefficient is defined
/// as<br/>
///  &rho;<sub>i</sub>=sqrt(1-1/(V<sub>ii</sub>*V<sup>-1</sup><sub>ii</sub>))
/// <br/>
/// such that the calculation of global correlation coefficients
/// possibly involves the inversion of a covariance matrix.
///
/// return value: maximum global correlation coefficient
///
/// The use of <b>binMap</b> is explained with the documentation of
/// the GetOutput() method
///

Double_t TUnfoldV17::GetRhoI(TH1 *rhoi,const Int_t *binMap,TH2 *invEmat) const
{
   ClearHistogram(rhoi,-1.);

   if(binMap) {
      // if there is a bin map, the matrix needs to be inverted
      // otherwise, use the existing inverse, such that
      // no matrix inversion is needed
      return GetRhoIFromMatrix(rhoi,fVxx,binMap,invEmat);
   } else {
      Double_t rhoMax=0.0;

      const Int_t *rows_Vxx=fVxx->GetRowIndexArray();
      const Int_t *cols_Vxx=fVxx->GetColIndexArray();
      const Double_t *data_Vxx=fVxx->GetMatrixArray();

      const Int_t *rows_VxxInv=fVxxInv->GetRowIndexArray();
      const Int_t *cols_VxxInv=fVxxInv->GetColIndexArray();
      const Double_t *data_VxxInv=fVxxInv->GetMatrixArray();
      
      for(Int_t i=0;i<GetNx();i++) {
         Int_t destI=fXToHist[i];
         Double_t e_ii=0.0,einv_ii=0.0;
         for(int index_vxx=rows_Vxx[i];index_vxx<rows_Vxx[i+1];index_vxx++) {
            if(cols_Vxx[index_vxx]==i) {
               e_ii=data_Vxx[index_vxx];
               break;
            }
         }
         for(int index_vxxinv=rows_VxxInv[i];index_vxxinv<rows_VxxInv[i+1];
             index_vxxinv++) {
            if(cols_VxxInv[index_vxxinv]==i) {
               einv_ii=data_VxxInv[index_vxxinv];
               break;
            }
         }
         Double_t rho=1.0;
         if((einv_ii>0.0)&&(e_ii>0.0)) rho=1.-1./(einv_ii*e_ii);
         if(rho>=0.0) rho=TMath::Sqrt(rho);
         else rho=-TMath::Sqrt(-rho);
         if(rho>rhoMax) {
            rhoMax = rho;
         }
         rhoi->SetBinContent(destI,rho);
      }
      return rhoMax;
   }
}

Double_t TUnfoldV17::GetRhoIFromMatrix(TH1 *rhoi,const TMatrixDSparse *eOrig,
                                    const Int_t *binMap,TH2 *invEmat) const
{
   // get global correlation coefficients with arbitrary min map
   //   rhoi: global correlation histogram
   //   emat: error matrix
   //   binMap: for each bin of the original output distribution
   //           specify the destination bin. A value of -1 means that the bin
   //           is discarded. 0 means underflow bin, 1 first bin, ...
   //        binMap[0] : destination of underflow bin
   //        binMap[1] : destination of first bin
   //          ...
   // return value: maximum global correlation

   Double_t rhoMax=0.;
   // original number of bins:
   //    fHistToX.GetSize()
   // loop over binMap and find number of used bins

   Int_t binMapSize = fHistToX.GetSize();

   // histToLocalBin[iBin] points to a local index
   //    only bins iBin of the histogram rhoi whih are referenced
   //    in the bin map have a local index
   std::map<int,int> histToLocalBin;
   Int_t nBin=0;
   for(Int_t i=0;i<binMapSize;i++) {
      Int_t mapped_i=binMap ? binMap[i] : i;
      if(mapped_i>=0) {
         if(histToLocalBin.find(mapped_i)==histToLocalBin.end()) {
            histToLocalBin[mapped_i]=nBin;
            nBin++;
         }
      }
   }
   // number of local indices: nBin
   if(nBin>0) {
      // construct inverse mapping function
      //    local index -> histogram bin
      Int_t *localBinToHist=new Int_t[nBin];
      for(std::map<int,int>::const_iterator i=histToLocalBin.begin();
          i!=histToLocalBin.end();i++) {
         localBinToHist[(*i).second]=(*i).first;
      }

      const Int_t *rows_eOrig=eOrig->GetRowIndexArray();
      const Int_t *cols_eOrig=eOrig->GetColIndexArray();
      const Double_t *data_eOrig=eOrig->GetMatrixArray();

      // remap error matrix
      //   matrix row  i  -> origI  (fXToHist[i])
      //   origI  -> destI  (binMap)
      //   destI -> ematBinI  (histToLocalBin)
      TMatrixD eRemap(nBin,nBin);
      // i:  row of the matrix eOrig
      for(Int_t i=0;i<GetNx();i++) {
         // origI: pointer in output histogram with all bins
         Int_t origI=fXToHist[i];
         // destI: pointer in the histogram rhoi
         Int_t destI=binMap ? binMap[origI] : origI;
         if(destI<0) continue;
         Int_t ematBinI=histToLocalBin[destI];
         for(int index_eOrig=rows_eOrig[i];index_eOrig<rows_eOrig[i+1];
             index_eOrig++) {
            // j: column of the matrix fVxx
            Int_t j=cols_eOrig[index_eOrig];
            // origJ: pointer in output histogram with all bins
            Int_t origJ=fXToHist[j];
            // destJ: pointer in the histogram rhoi
            Int_t destJ=binMap ? binMap[origJ] : origJ;
            if(destJ<0) continue;
            Int_t ematBinJ=histToLocalBin[destJ];
            eRemap(ematBinI,ematBinJ) += data_eOrig[index_eOrig];
         }
      }
      // invert remapped error matrix
      TMatrixDSparse eSparse(eRemap);
      Int_t rank=0;
      TMatrixDSparse *einvSparse=InvertMSparseSymmPos(&eSparse,&rank);
      if(rank!=einvSparse->GetNrows()) {
         Warning("GetRhoIFormMatrix","Covariance matrix has rank %d expect %d",
                 rank,einvSparse->GetNrows());
      }
      // fill to histogram
      const Int_t *rows_eInv=einvSparse->GetRowIndexArray();
      const Int_t *cols_eInv=einvSparse->GetColIndexArray();
      const Double_t *data_eInv=einvSparse->GetMatrixArray();

      for(Int_t i=0;i<einvSparse->GetNrows();i++) {
         Double_t e_ii=eRemap(i,i);
         Double_t einv_ii=0.0;
         for(Int_t index_einv=rows_eInv[i];index_einv<rows_eInv[i+1];
             index_einv++) {
            Int_t j=cols_eInv[index_einv];
            if(j==i) {
               einv_ii=data_eInv[index_einv];
            }
            if(invEmat) {
               invEmat->SetBinContent(localBinToHist[i],localBinToHist[j],
                                      data_eInv[index_einv]);
            }
         }
         Double_t rho=1.0;
         if((einv_ii>0.0)&&(e_ii>0.0)) rho=1.-1./(einv_ii*e_ii);
         if(rho>=0.0) rho=TMath::Sqrt(rho);
         else rho=-TMath::Sqrt(-rho);
         if(rho>rhoMax) {
            rhoMax = rho;
         }
         //std::cout<<i<<" "<<localBinToHist[i]<<" "<<rho<<"\n";
         rhoi->SetBinContent(localBinToHist[i],rho);
      }
         
      DeleteMatrix(&einvSparse);
      delete [] localBinToHist;
   }
   return rhoMax;
}


////////////////////////////////////////////////////////////////////////
/// Initialize bin contents and bin errors for a given histogram
///
/// \param[out] h histogram
/// \param[in] x new histogram content
///
/// all histgram errors are set to zero, all contents are set to <b>x</b>
void TUnfoldV17::ClearHistogram(TH1 *h,Double_t x) const
{
   Int_t nxyz[3];
   nxyz[0]=h->GetNbinsX()+1;
   nxyz[1]=h->GetNbinsY()+1;
   nxyz[2]=h->GetNbinsZ()+1;
   for(int i=h->GetDimension();i<3;i++) nxyz[i]=0;
   Int_t ixyz[3];
   for(int i=0;i<3;i++) ixyz[i]=0;
   while((ixyz[0]<=nxyz[0])&&
         (ixyz[1]<=nxyz[1])&&
         (ixyz[2]<=nxyz[2])){
      Int_t ibin=h->GetBin(ixyz[0],ixyz[1],ixyz[2]);
      h->SetBinContent(ibin,x);
      h->SetBinError(ibin,0.0);
      for(Int_t i=0;i<3;i++) {
         ixyz[i] += 1;
         if(ixyz[i]<=nxyz[i]) break;
         if(i<2) ixyz[i]=0;
      }
   }
}

void TUnfoldV17::SetEpsMatrix(Double_t eps) {
   // set accuracy for matrix inversion
   if((eps>0.0)&&(eps<1.0)) fEpsMatrix=eps;
}

////////////////////////////////////////////////////////////////////////
/// return a string describing the TUnfold version
///
/// The version is reported in the form  Vmajor.minor
/// Changes of the minor version number typically correspond to
/// bug-fixes. Changes of the major version may result in adding or
/// removing data attributes, such that the streamer methods are not
/// compatible between different major versions.

const char *TUnfoldV17::GetTUnfoldVersion(void)
{
   return TUnfold_VERSION;
}

---