text: // This file is part of Eigen, a lightweight C++ template library
  // for linear algebra.
  //
  // Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
  //
  // This Source Code Form is subject to the terms of the Mozilla
  // Public License v. 2.0. If a copy of the MPL was not distributed
  // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
  
  #ifndef EIGEN_SKYLINEMATRIX_H
  #define EIGEN_SKYLINEMATRIX_H
  
  #include "SkylineStorage.h"
  #include "SkylineMatrixBase.h"
  
  namespace Eigen { 
  
  /** \ingroup Skyline_Module
   *
   * \class SkylineMatrix
   *
   * \brief The main skyline matrix class
   *
   * This class implements a skyline matrix using the very uncommon storage
   * scheme.
   *
   * \param Scalar_ the scalar type, i.e. the type of the coefficients
   * \param Options_ Union of bit flags controlling the storage scheme. Currently the only possibility
   *                 is RowMajor. The default is 0 which means column-major.
   *
   *
   */
  namespace internal {
  template<typename Scalar_, int Options_>
  struct traits<SkylineMatrix<Scalar_, Options_> > {
      typedef Scalar_ Scalar;
      typedef Sparse StorageKind;
  
      enum {
          RowsAtCompileTime = Dynamic,
          ColsAtCompileTime = Dynamic,
          MaxRowsAtCompileTime = Dynamic,
          MaxColsAtCompileTime = Dynamic,
          Flags = SkylineBit | Options_,
          CoeffReadCost = NumTraits<Scalar>::ReadCost,
      };
  };
  }
  
  template<typename Scalar_, int Options_>
  class SkylineMatrix
  : public SkylineMatrixBase<SkylineMatrix<Scalar_, Options_> > {
  public:
      EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(SkylineMatrix)
      EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, +=)
      EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, -=)
  
      using Base::IsRowMajor;
  
  protected:
  
      typedef SkylineMatrix<Scalar, (Flags&~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0) > TransposedSkylineMatrix;
  
      Index m_outerSize;
      Index m_innerSize;
  
  public:
      Index* m_colStartIndex;
      Index* m_rowStartIndex;
      SkylineStorage<Scalar> m_data;
  
  public:
  
      inline Index rows() const {
          return IsRowMajor ? m_outerSize : m_innerSize;
      }
  
      inline Index cols() const {
          return IsRowMajor ? m_innerSize : m_outerSize;
      }
  
      inline Index innerSize() const {
          return m_innerSize;
      }
  
      inline Index outerSize() const {
          return m_outerSize;
      }
  
      inline Index upperNonZeros() const {
          return m_data.upperSize();
      }
  
      inline Index lowerNonZeros() const {
          return m_data.lowerSize();
      }
  
      inline Index upperNonZeros(Index j) const {
          return m_colStartIndex[j + 1] - m_colStartIndex[j];
      }
  
      inline Index lowerNonZeros(Index j) const {
          return m_rowStartIndex[j + 1] - m_rowStartIndex[j];
      }
  
      inline const Scalar* _diagPtr() const {
          return &m_data.diag(0);
      }
  
      inline Scalar* _diagPtr() {
          return &m_data.diag(0);
      }
  
      inline const Scalar* _upperPtr() const {
          return &m_data.upper(0);
      }
  
      inline Scalar* _upperPtr() {
          return &m_data.upper(0);
      }
  
      inline const Scalar* _lowerPtr() const {
          return &m_data.lower(0);
      }
  
      inline Scalar* _lowerPtr() {
          return &m_data.lower(0);
      }
  
      inline const Index* _upperProfilePtr() const {
          return &m_data.upperProfile(0);
      }
  
      inline Index* _upperProfilePtr() {
          return &m_data.upperProfile(0);
      }
  
      inline const Index* _lowerProfilePtr() const {
          return &m_data.lowerProfile(0);
      }
  
      inline Index* _lowerProfilePtr() {
          return &m_data.lowerProfile(0);
      }
  
      inline Scalar coeff(Index row, Index col) const {
          const Index outer = IsRowMajor ? row : col;
          const Index inner = IsRowMajor ? col : row;
  
          eigen_assert(outer < outerSize());
          eigen_assert(inner < innerSize());
  
          if (outer == inner)
              return this->m_data.diag(outer);
  
          if (IsRowMajor) {
              if (inner > outer) //upper matrix
              {
                  const Index minOuterIndex = inner - m_data.upperProfile(inner);
                  if (outer >= minOuterIndex)
                      return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
                  else
                      return Scalar(0);
              }
              if (inner < outer) //lower matrix
              {
                  const Index minInnerIndex = outer - m_data.lowerProfile(outer);
                  if (inner >= minInnerIndex)
                      return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
                  else
                      return Scalar(0);
              }
              return m_data.upper(m_colStartIndex[inner] + outer - inner);
          } else {
              if (outer > inner) //upper matrix
              {
                  const Index maxOuterIndex = inner + m_data.upperProfile(inner);
                  if (outer <= maxOuterIndex)
                      return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
                  else
                      return Scalar(0);
              }
              if (outer < inner) //lower matrix
              {
                  const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
  
                  if (inner <= maxInnerIndex)
                      return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
                  else
                      return Scalar(0);
              }
          }
      }
  
      inline Scalar& coeffRef(Index row, Index col) {
          const Index outer = IsRowMajor ? row : col;
          const Index inner = IsRowMajor ? col : row;
  
          eigen_assert(outer < outerSize());
          eigen_assert(inner < innerSize());
  
          if (outer == inner)
              return this->m_data.diag(outer);
  
          if (IsRowMajor) {
              if (col > row) //upper matrix
              {
                  const Index minOuterIndex = inner - m_data.upperProfile(inner);
                  eigen_assert(outer >= minOuterIndex && "You tried to access a coeff that does not exist in the storage");
                  return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
              }
              if (col < row) //lower matrix
              {
                  const Index minInnerIndex = outer - m_data.lowerProfile(outer);
                  eigen_assert(inner >= minInnerIndex && "You tried to access a coeff that does not exist in the storage");
                  return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
              }
          } else {
              if (outer > inner) //upper matrix
              {
                  const Index maxOuterIndex = inner + m_data.upperProfile(inner);
                  eigen_assert(outer <= maxOuterIndex && "You tried to access a coeff that does not exist in the storage");
                  return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
              }
              if (outer < inner) //lower matrix
              {
                  const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
                  eigen_assert(inner <= maxInnerIndex && "You tried to access a coeff that does not exist in the storage");
                  return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
              }
          }
      }
  
      inline Scalar coeffDiag(Index idx) const {
          eigen_assert(idx < outerSize());
          eigen_assert(idx < innerSize());
          return this->m_data.diag(idx);
      }
  
      inline Scalar coeffLower(Index row, Index col) const {
          const Index outer = IsRowMajor ? row : col;
          const Index inner = IsRowMajor ? col : row;
  
          eigen_assert(outer < outerSize());
          eigen_assert(inner < innerSize());
          eigen_assert(inner != outer);
  
          if (IsRowMajor) {
              const Index minInnerIndex = outer - m_data.lowerProfile(outer);
              if (inner >= minInnerIndex)
                  return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
              else
                  return Scalar(0);
  
          } else {
              const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
              if (inner <= maxInnerIndex)
                  return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
              else
                  return Scalar(0);
          }
      }
  
      inline Scalar coeffUpper(Index row, Index col) const {
          const Index outer = IsRowMajor ? row : col;
          const Index inner = IsRowMajor ? col : row;
  
          eigen_assert(outer < outerSize());
          eigen_assert(inner < innerSize());
          eigen_assert(inner != outer);
  
          if (IsRowMajor) {
              const Index minOuterIndex = inner - m_data.upperProfile(inner);
              if (outer >= minOuterIndex)
                  return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
              else
                  return Scalar(0);
          } else {
              const Index maxOuterIndex = inner + m_data.upperProfile(inner);
              if (outer <= maxOuterIndex)
                  return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
              else
                  return Scalar(0);
          }
      }
  
      inline Scalar& coeffRefDiag(Index idx) {
          eigen_assert(idx < outerSize());
          eigen_assert(idx < innerSize());
          return this->m_data.diag(idx);
      }
  
      inline Scalar& coeffRefLower(Index row, Index col) {
          const Index outer = IsRowMajor ? row : col;
          const Index inner = IsRowMajor ? col : row;
  
          eigen_assert(outer < outerSize());
          eigen_assert(inner < innerSize());
          eigen_assert(inner != outer);
  
          if (IsRowMajor) {
              const Index minInnerIndex = outer - m_data.lowerProfile(outer);
              eigen_assert(inner >= minInnerIndex && "You tried to access a coeff that does not exist in the storage");
              return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
          } else {
              const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
              eigen_assert(inner <= maxInnerIndex && "You tried to access a coeff that does not exist in the storage");
              return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
          }
      }
  
      inline bool coeffExistLower(Index row, Index col) {
          const Index outer = IsRowMajor ? row : col;
          const Index inner = IsRowMajor ? col : row;
  
          eigen_assert(outer < outerSize());
          eigen_assert(inner < innerSize());
          eigen_assert(inner != outer);
  
          if (IsRowMajor) {
              const Index minInnerIndex = outer - m_data.lowerProfile(outer);
              return inner >= minInnerIndex;
          } else {
              const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
              return inner <= maxInnerIndex;
          }
      }
  
      inline Scalar& coeffRefUpper(Index row, Index col) {
          const Index outer = IsRowMajor ? row : col;
          const Index inner = IsRowMajor ? col : row;
  
          eigen_assert(outer < outerSize());
          eigen_assert(inner < innerSize());
          eigen_assert(inner != outer);
  
          if (IsRowMajor) {
              const Index minOuterIndex = inner - m_data.upperProfile(inner);
              eigen_assert(outer >= minOuterIndex && "You tried to access a coeff that does not exist in the storage");
              return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
          } else {
              const Index maxOuterIndex = inner + m_data.upperProfile(inner);
              eigen_assert(outer <= maxOuterIndex && "You tried to access a coeff that does not exist in the storage");
              return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
          }
      }
  
      inline bool coeffExistUpper(Index row, Index col) {
          const Index outer = IsRowMajor ? row : col;
          const Index inner = IsRowMajor ? col : row;
  
          eigen_assert(outer < outerSize());
          eigen_assert(inner < innerSize());
          eigen_assert(inner != outer);
  
          if (IsRowMajor) {
              const Index minOuterIndex = inner - m_data.upperProfile(inner);
              return outer >= minOuterIndex;
          } else {
              const Index maxOuterIndex = inner + m_data.upperProfile(inner);
              return outer <= maxOuterIndex;
          }
      }
  
  
  protected:
  
  public:
      class InnerUpperIterator;
      class InnerLowerIterator;
  
      class OuterUpperIterator;
      class OuterLowerIterator;
  
      /** Removes all non zeros */
      inline void setZero() {
          m_data.clear();
          std::fill_n(m_colStartIndex, m_outerSize + 1, Index(0));
          std::fill_n(m_rowStartIndex, m_outerSize + 1, Index(0));
      }
  
      /** \returns the number of non zero coefficients */
      inline Index nonZeros() const {
          return m_data.diagSize() + m_data.upperSize() + m_data.lowerSize();
      }
  
      /** Preallocates \a reserveSize non zeros */
      inline void reserve(Index reserveSize, Index reserveUpperSize, Index reserveLowerSize) {
          m_data.reserve(reserveSize, reserveUpperSize, reserveLowerSize);
      }
  
      /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
  
       *
       * \warning This function can be extremely slow if the non zero coefficients
       * are not inserted in a coherent order.
       *
       * After an insertion session, you should call the finalize() function.
       */
      EIGEN_DONT_INLINE Scalar & insert(Index row, Index col) {
          const Index outer = IsRowMajor ? row : col;
          const Index inner = IsRowMajor ? col : row;
  
          eigen_assert(outer < outerSize());
          eigen_assert(inner < innerSize());
  
          if (outer == inner)
              return m_data.diag(col);
  
          if (IsRowMajor) {
              if (outer < inner) //upper matrix
              {
                  Index minOuterIndex = 0;
                  minOuterIndex = inner - m_data.upperProfile(inner);
  
                  if (outer < minOuterIndex) //The value does not yet exist
                  {
                      const Index previousProfile = m_data.upperProfile(inner);
  
                      m_data.upperProfile(inner) = inner - outer;
  
  
                      const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
                      //shift data stored after this new one
                      const Index stop = m_colStartIndex[cols()];
                      const Index start = m_colStartIndex[inner];
  
  
                      for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
                          m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
                      }
  
                      for (Index innerIdx = cols(); innerIdx > inner; innerIdx--) {
                          m_colStartIndex[innerIdx] += bandIncrement;
                      }
  
                      //zeros new data
                      std::fill_n(this->_upperPtr() + start, bandIncrement - 1, Scalar(0));
  
                      return m_data.upper(m_colStartIndex[inner]);
                  } else {
                      return m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
                  }
              }
  
              if (outer > inner) //lower matrix
              {
                  const Index minInnerIndex = outer - m_data.lowerProfile(outer);
                  if (inner < minInnerIndex) //The value does not yet exist
                  {
                      const Index previousProfile = m_data.lowerProfile(outer);
                      m_data.lowerProfile(outer) = outer - inner;
  
                      const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
                      //shift data stored after this new one
                      const Index stop = m_rowStartIndex[rows()];
                      const Index start = m_rowStartIndex[outer];
  
  
                      for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
                          m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
                      }
  
                      for (Index innerIdx = rows(); innerIdx > outer; innerIdx--) {
                          m_rowStartIndex[innerIdx] += bandIncrement;
                      }
  
                      //zeros new data
                      std::fill_n(this->_lowerPtr() + start, bandIncrement - 1, Scalar(0));
                      return m_data.lower(m_rowStartIndex[outer]);
                  } else {
                      return m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
                  }
              }
          } else {
              if (outer > inner) //upper matrix
              {
                  const Index maxOuterIndex = inner + m_data.upperProfile(inner);
                  if (outer > maxOuterIndex) //The value does not yet exist
                  {
                      const Index previousProfile = m_data.upperProfile(inner);
                      m_data.upperProfile(inner) = outer - inner;
  
                      const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
                      //shift data stored after this new one
                      const Index stop = m_rowStartIndex[rows()];
                      const Index start = m_rowStartIndex[inner + 1];
  
                      for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
                          m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
                      }
  
                      for (Index innerIdx = inner + 1; innerIdx < outerSize() + 1; innerIdx++) {
                          m_rowStartIndex[innerIdx] += bandIncrement;
                      }
                      std::fill_n(this->_upperPtr() + m_rowStartIndex[inner] + previousProfile + 1, bandIncrement - 1, Scalar(0));
                      return m_data.upper(m_rowStartIndex[inner] + m_data.upperProfile(inner));
                  } else {
                      return m_data.upper(m_rowStartIndex[inner] + (outer - inner));
                  }
              }
  
              if (outer < inner) //lower matrix
              {
                  const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
                  if (inner > maxInnerIndex) //The value does not yet exist
                  {
                      const Index previousProfile = m_data.lowerProfile(outer);
                      m_data.lowerProfile(outer) = inner - outer;
  
                      const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
                      //shift data stored after this new one
                      const Index stop = m_colStartIndex[cols()];
                      const Index start = m_colStartIndex[outer + 1];
  
                      for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
                          m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
                      }
  
                      for (Index innerIdx = outer + 1; innerIdx < outerSize() + 1; innerIdx++) {
                          m_colStartIndex[innerIdx] += bandIncrement;
                      }
                      std::fill_n(this->_lowerPtr() + m_colStartIndex[outer] + previousProfile + 1, bandIncrement - 1, Scalar(0));
                      return m_data.lower(m_colStartIndex[outer] + m_data.lowerProfile(outer));
                  } else {
                      return m_data.lower(m_colStartIndex[outer] + (inner - outer));
                  }
              }
          }
      }
  
      /** Must be called after inserting a set of non zero entries.
       */
      inline void finalize() {
          if (IsRowMajor) {
              if (rows() > cols())
                  m_data.resize(cols(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
              else
                  m_data.resize(rows(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
  
              //            eigen_assert(rows() == cols() && "memory reorganisatrion only works with suare matrix");
              //
              //            Scalar* newArray = new Scalar[m_colStartIndex[cols()] + 1 + m_rowStartIndex[rows()] + 1];
              //            Index dataIdx = 0;
              //            for (Index row = 0; row < rows(); row++) {
              //
              //                const Index nbLowerElts = m_rowStartIndex[row + 1] - m_rowStartIndex[row];
              //                //                std::cout << "nbLowerElts" << nbLowerElts << std::endl;
              //                memcpy(newArray + dataIdx, m_data.m_lower + m_rowStartIndex[row], nbLowerElts * sizeof (Scalar));
              //                m_rowStartIndex[row] = dataIdx;
              //                dataIdx += nbLowerElts;
              //
              //                const Index nbUpperElts = m_colStartIndex[row + 1] - m_colStartIndex[row];
              //                memcpy(newArray + dataIdx, m_data.m_upper + m_colStartIndex[row], nbUpperElts * sizeof (Scalar));
              //                m_colStartIndex[row] = dataIdx;
              //                dataIdx += nbUpperElts;
              //
              //
              //            }
              //            //todo : don't access m_data profile directly : add an accessor from SkylineMatrix
              //            m_rowStartIndex[rows()] = m_rowStartIndex[rows()-1] + m_data.lowerProfile(rows()-1);
              //            m_colStartIndex[cols()] = m_colStartIndex[cols()-1] + m_data.upperProfile(cols()-1);
              //
              //            delete[] m_data.m_lower;
              //            delete[] m_data.m_upper;
              //
              //            m_data.m_lower = newArray;
              //            m_data.m_upper = newArray;
          } else {
              if (rows() > cols())
                  m_data.resize(cols(), rows(), cols(), m_rowStartIndex[cols()] + 1, m_colStartIndex[cols()] + 1);
              else
                  m_data.resize(rows(), rows(), cols(), m_rowStartIndex[rows()] + 1, m_colStartIndex[rows()] + 1);
          }
      }
  
      inline void squeeze() {
          finalize();
          m_data.squeeze();
      }
  
      void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar > ()) {
          //TODO
      }
  
      /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero
       * \sa resizeNonZeros(Index), reserve(), setZero()
       */
      void resize(size_t rows, size_t cols) {
          const Index diagSize = rows > cols ? cols : rows;
          m_innerSize = IsRowMajor ? cols : rows;
  
          eigen_assert(rows == cols && "Skyline matrix must be square matrix");
  
          if (diagSize % 2) { // diagSize is odd
              const Index k = (diagSize - 1) / 2;
  
              m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
                      2 * k * k + k + 1,
                      2 * k * k + k + 1);
  
          } else // diagSize is even
          {
              const Index k = diagSize / 2;
              m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
                      2 * k * k - k + 1,
                      2 * k * k - k + 1);
          }
  
          if (m_colStartIndex && m_rowStartIndex) {
              delete[] m_colStartIndex;
              delete[] m_rowStartIndex;
          }
          m_colStartIndex = new Index [cols + 1];
          m_rowStartIndex = new Index [rows + 1];
          m_outerSize = diagSize;
  
          m_data.reset();
          m_data.clear();
  
          m_outerSize = diagSize;
          std::fill_n(m_colStartIndex, cols + 1, Index(0));
          std::fill_n(m_rowStartIndex, rows + 1, Index(0));
      }
  
      void resizeNonZeros(Index size) {
          m_data.resize(size);
      }
  
      inline SkylineMatrix()
      : m_outerSize(-1), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
          resize(0, 0);
      }
  
      inline SkylineMatrix(size_t rows, size_t cols)
      : m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
          resize(rows, cols);
      }
  
      template<typename OtherDerived>
      inline SkylineMatrix(const SkylineMatrixBase<OtherDerived>& other)
      : m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
          *this = other.derived();
      }
  
      inline SkylineMatrix(const SkylineMatrix & other)
      : Base(), m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
          *this = other.derived();
      }
  
      inline void swap(SkylineMatrix & other) {
          //EIGEN_DBG_SKYLINE(std::cout << "SkylineMatrix:: swap\n");
          std::swap(m_colStartIndex, other.m_colStartIndex);
          std::swap(m_rowStartIndex, other.m_rowStartIndex);
          std::swap(m_innerSize, other.m_innerSize);
          std::swap(m_outerSize, other.m_outerSize);
          m_data.swap(other.m_data);
      }
  
      inline SkylineMatrix & operator=(const SkylineMatrix & other) {
          std::cout << "SkylineMatrix& operator=(const SkylineMatrix& other)\n";
          if (other.isRValue()) {
              swap(other.const_cast_derived());
          } else {
              resize(other.rows(), other.cols());
              memcpy(m_colStartIndex, other.m_colStartIndex, (m_outerSize + 1) * sizeof (Index));
              memcpy(m_rowStartIndex, other.m_rowStartIndex, (m_outerSize + 1) * sizeof (Index));
              m_data = other.m_data;
          }
          return *this;
      }
  
      template<typename OtherDerived>
              inline SkylineMatrix & operator=(const SkylineMatrixBase<OtherDerived>& other) {
          const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
          if (needToTranspose) {
              //         TODO
              //            return *this;
          } else {
              // there is no special optimization
              return SkylineMatrixBase<SkylineMatrix>::operator=(other.derived());
          }
      }
  
      friend std::ostream & operator <<(std::ostream & s, const SkylineMatrix & m) {
  
          EIGEN_DBG_SKYLINE(
          std::cout << "upper elements : " << std::endl;
          for (Index i = 0; i < m.m_data.upperSize(); i++)
              std::cout << m.m_data.upper(i) << "\t";
          std::cout << std::endl;
          std::cout << "upper profile : " << std::endl;
          for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
              std::cout << m.m_data.upperProfile(i) << "\t";
          std::cout << std::endl;
          std::cout << "lower startIdx : " << std::endl;
          for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
              std::cout << (IsRowMajor ? m.m_colStartIndex[i] : m.m_rowStartIndex[i]) << "\t";
          std::cout << std::endl;
  
  
          std::cout << "lower elements : " << std::endl;
          for (Index i = 0; i < m.m_data.lowerSize(); i++)
              std::cout << m.m_data.lower(i) << "\t";
          std::cout << std::endl;
          std::cout << "lower profile : " << std::endl;
          for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
              std::cout << m.m_data.lowerProfile(i) << "\t";
          std::cout << std::endl;
          std::cout << "lower startIdx : " << std::endl;
          for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
              std::cout << (IsRowMajor ? m.m_rowStartIndex[i] : m.m_colStartIndex[i]) << "\t";
          std::cout << std::endl;
          );
          for (Index rowIdx = 0; rowIdx < m.rows(); rowIdx++) {
              for (Index colIdx = 0; colIdx < m.cols(); colIdx++) {
                  s << m.coeff(rowIdx, colIdx) << "\t";
              }
              s << std::endl;
          }
          return s;
      }
  
      /** Destructor */
      inline ~SkylineMatrix() {
          delete[] m_colStartIndex;
          delete[] m_rowStartIndex;
      }
  
      /** Overloaded for performance */
      Scalar sum() const;
  };
  
  template<typename Scalar, int Options_>
  class SkylineMatrix<Scalar, Options_>::InnerUpperIterator {
  public:
  
      InnerUpperIterator(const SkylineMatrix& mat, Index outer)
      : m_matrix(mat), m_outer(outer),
      m_id(Options_ == RowMajor ? mat.m_colStartIndex[outer] : mat.m_rowStartIndex[outer] + 1),
      m_start(m_id),
      m_end(Options_ == RowMajor ? mat.m_colStartIndex[outer + 1] : mat.m_rowStartIndex[outer + 1] + 1) {
      }
  
      inline InnerUpperIterator & operator++() {
          m_id++;
          return *this;
      }
  
      inline InnerUpperIterator & operator+=(Index shift) {
          m_id += shift;
          return *this;
      }
  
      inline Scalar value() const {
          return m_matrix.m_data.upper(m_id);
      }
  
      inline Scalar* valuePtr() {
          return const_cast<Scalar*> (&(m_matrix.m_data.upper(m_id)));
      }
  
      inline Scalar& valueRef() {
          return const_cast<Scalar&> (m_matrix.m_data.upper(m_id));
      }
  
      inline Index index() const {
          return IsRowMajor ? m_outer - m_matrix.m_data.upperProfile(m_outer) + (m_id - m_start) :
                  m_outer + (m_id - m_start) + 1;
      }
  
      inline Index row() const {
          return IsRowMajor ? index() : m_outer;
      }
  
      inline Index col() const {
          return IsRowMajor ? m_outer : index();
      }
  
      inline size_t size() const {
          return m_matrix.m_data.upperProfile(m_outer);
      }
  
      inline operator bool() const {
          return (m_id < m_end) && (m_id >= m_start);
      }
  
  protected:
      const SkylineMatrix& m_matrix;
      const Index m_outer;
      Index m_id;
      const Index m_start;
      const Index m_end;
  };
  
  template<typename Scalar, int Options_>
  class SkylineMatrix<Scalar, Options_>::InnerLowerIterator {
  public:
  
      InnerLowerIterator(const SkylineMatrix& mat, Index outer)
      : m_matrix(mat),
      m_outer(outer),
      m_id(Options_ == RowMajor ? mat.m_rowStartIndex[outer] : mat.m_colStartIndex[outer] + 1),
      m_start(m_id),
      m_end(Options_ == RowMajor ? mat.m_rowStartIndex[outer + 1] : mat.m_colStartIndex[outer + 1] + 1) {
      }
  
      inline InnerLowerIterator & operator++() {
          m_id++;
          return *this;
      }
  
      inline InnerLowerIterator & operator+=(Index shift) {
          m_id += shift;
          return *this;
      }
  
      inline Scalar value() const {
          return m_matrix.m_data.lower(m_id);
      }
  
      inline Scalar* valuePtr() {
          return const_cast<Scalar*> (&(m_matrix.m_data.lower(m_id)));
      }
  
      inline Scalar& valueRef() {
          return const_cast<Scalar&> (m_matrix.m_data.lower(m_id));
      }
  
      inline Index index() const {
          return IsRowMajor ? m_outer - m_matrix.m_data.lowerProfile(m_outer) + (m_id - m_start) :
                  m_outer + (m_id - m_start) + 1;
          ;
      }
  
      inline Index row() const {
          return IsRowMajor ? m_outer : index();
      }
  
      inline Index col() const {
          return IsRowMajor ? index() : m_outer;
      }
  
      inline size_t size() const {
          return m_matrix.m_data.lowerProfile(m_outer);
      }
  
      inline operator bool() const {
          return (m_id < m_end) && (m_id >= m_start);
      }
  
  protected:
      const SkylineMatrix& m_matrix;
      const Index m_outer;
      Index m_id;
      const Index m_start;
      const Index m_end;
  };
  
  } // end namespace Eigen
  
  #endif // EIGEN_SKYLINEMATRIX_H
  