Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ > Class Template Reference

Modified Incomplete Cholesky with dual threshold. More...

Inheritance diagram for Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >:

Public Types
enum	{ UpLo }
enum	{   ColsAtCompileTime ,   MaxColsAtCompileTime }
typedef SparseMatrix< Scalar, ColMajor, StorageIndex >	FactorType
typedef OrderingType_	OrderingType
typedef OrderingType::PermutationType	PermutationType
typedef NumTraits< Scalar >::Real	RealScalar
typedef PermutationType::StorageIndex	StorageIndex
typedef Matrix< StorageIndex, Dynamic, 1 >	VectorIx
typedef std::vector< std::list< StorageIndex > >	VectorList
typedef Matrix< RealScalar, Dynamic, 1 >	VectorRx
typedef Matrix< Scalar, Dynamic, 1 >	VectorSx

Public Member Functions
template<typename Rhs , typename Dest >
void	_solve_impl (const Rhs &b, Dest &x) const
template<typename MatrixType >
void	analyzePattern (const MatrixType &mat)
	Computes the fill reducing permutation vector using the sparsity pattern of mat. More...
EIGEN_CONSTEXPR Index	cols () const EIGEN_NOEXCEPT
template<typename MatrixType >
void	compute (const MatrixType &mat)
template<typename MatrixType >
void	factorize (const MatrixType &mat)
	Performs the numerical factorization of the input matrix mat. More...
template<typename MatrixType_ >
void	factorize (const MatrixType_ &mat)
	IncompleteCholesky ()
template<typename MatrixType >
	IncompleteCholesky (const MatrixType &matrix)
ComputationInfo	info () const
	Reports whether previous computation was successful. More...
const FactorType &	matrixL () const
const PermutationType &	permutationP () const
EIGEN_CONSTEXPR Index	rows () const EIGEN_NOEXCEPT
const VectorRx &	scalingS () const
void	setInitialShift (RealScalar shift)
	Set the initial shift parameter $\sigma$. More...
Public Member Functions inherited from Eigen::SparseSolverBase< IncompleteCholesky< Scalar, Lower, AMDOrdering< int > > >
IncompleteCholesky< Scalar, Lower, AMDOrdering< int > > &	derived ()
const IncompleteCholesky< Scalar, Lower, AMDOrdering< int > > &	derived () const
const Solve< IncompleteCholesky< Scalar, Lower, AMDOrdering< int > >, Rhs >	solve (const MatrixBase< Rhs > &b) const
const Solve< IncompleteCholesky< Scalar, Lower, AMDOrdering< int > >, Rhs >	solve (const SparseMatrixBase< Rhs > &b) const
	SparseSolverBase ()
	SparseSolverBase (SparseSolverBase &&other)
	~SparseSolverBase ()

Protected Types
typedef SparseSolverBase< IncompleteCholesky< Scalar, UpLo_, OrderingType_ > >	Base

Protected Attributes
bool	m_analysisIsOk
bool	m_factorizationIsOk
ComputationInfo	m_info
RealScalar	m_initialShift
bool	m_isInitialized
FactorType	m_L
PermutationType	m_perm
VectorRx	m_scale
Protected Attributes inherited from Eigen::SparseSolverBase< IncompleteCholesky< Scalar, Lower, AMDOrdering< int > > >
bool	m_isInitialized

Private Member Functions
void	updateList (Ref< const VectorIx > colPtr, Ref< VectorIx > rowIdx, Ref< VectorSx > vals, const Index &col, const Index &jk, VectorIx &firstElt, VectorList &listCol)

Detailed Description

template<typename Scalar, int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>
class Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >

Modified Incomplete Cholesky with dual threshold.

References : C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with Limited memory, SIAM J. Sci. Comput. 21(1), pp. 24-45, 1999

Template Parameters

Scalar	the scalar type of the input matrices
UpLo_	The triangular part that will be used for the computations. It can be Lower or Upper. Default is Lower.
OrderingType_	The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<int>.

This class follows the sparse solver concept .

It performs the following incomplete factorization: $SPA{P}^{\prime }S\approx L{L}^{\prime }$ where L is a lower triangular factor, S is a diagonal scaling matrix, and P is a fill-in reducing permutation as computed by the ordering method.

Shifting strategy: Let $B=SPA{P}^{\prime }S$ be the scaled matrix on which the factorization is carried out, and $\beta$ be the minimum value of the diagonal. If $\beta >0$ then, the factorization is directly performed on the matrix B. Otherwise, the factorization is performed on the shifted matrix $B+(\sigma +|\beta |I$ where $\sigma$ is the initial shift value as returned and set by setInitialShift() method. The default value is $\sigma ={10}^{-3}$. If the factorization fails, then the shift in doubled until it succeed or a maximum of ten attempts. If it still fails, as returned by the info() method, then you can either increase the initial shift, or better use another preconditioning technique.

Definition at line 46 of file IncompleteCholesky.h.

Member Typedef Documentation

Base

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

typedef SparseSolverBase<IncompleteCholesky<Scalar,UpLo_,OrderingType_> > Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::Base

protected

Definition at line 49 of file IncompleteCholesky.h.

FactorType

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

typedef SparseMatrix<Scalar,ColMajor,StorageIndex> Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::FactorType

Definition at line 56 of file IncompleteCholesky.h.

OrderingType

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

typedef OrderingType_ Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::OrderingType

Definition at line 53 of file IncompleteCholesky.h.

PermutationType

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

typedef OrderingType::PermutationType Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::PermutationType

Definition at line 54 of file IncompleteCholesky.h.

RealScalar

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

typedef NumTraits<Scalar>::Real Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::RealScalar

Definition at line 52 of file IncompleteCholesky.h.

StorageIndex

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

typedef PermutationType::StorageIndex Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::StorageIndex

Definition at line 55 of file IncompleteCholesky.h.

VectorIx

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

typedef Matrix<StorageIndex,Dynamic, 1> Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::VectorIx

Definition at line 59 of file IncompleteCholesky.h.

VectorList

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

typedef std::vector<std::list<StorageIndex> > Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::VectorList

Definition at line 60 of file IncompleteCholesky.h.

VectorRx

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

typedef Matrix<RealScalar,Dynamic,1> Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::VectorRx

Definition at line 58 of file IncompleteCholesky.h.

VectorSx

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

typedef Matrix<Scalar,Dynamic,1> Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::VectorSx

Definition at line 57 of file IncompleteCholesky.h.

Member Enumeration Documentation

anonymous enum

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

anonymous enum

Enumerator
UpLo

Definition at line 61 of file IncompleteCholesky.h.

{ UpLo = UpLo_ };

Code

anonymous enum

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

anonymous enum

Enumerator
ColsAtCompileTime
MaxColsAtCompileTime

Definition at line 62 of file IncompleteCholesky.h.

         {
      ColsAtCompileTime = Dynamic,
      MaxColsAtCompileTime = Dynamic
    };

Code

Constructor & Destructor Documentation

IncompleteCholesky() [1/2]

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::IncompleteCholesky ( )

inline

Default constructor leaving the object in a partly non-initialized stage.

You must call compute() or the pair analyzePattern()/factorize() to make it valid.

See also

IncompleteCholesky(const MatrixType&)

Definition at line 74 of file IncompleteCholesky.h.

: m_initialShift(1e-3),m_analysisIsOk(false),m_factorizationIsOk(false) {}

Code

IncompleteCholesky() [2/2]

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

template<typename MatrixType >

Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::IncompleteCholesky ( const MatrixType &  matrix )

inline

Constructor computing the incomplete factorization for the given matrix matrix.

Definition at line 79 of file IncompleteCholesky.h.

                                                 : m_initialShift(1e-3),m_analysisIsOk(false),m_factorizationIsOk(false)
    {
      compute(matrix);
    }

Code

Member Function Documentation

_solve_impl()

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

template<typename Rhs , typename Dest >

void Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::_solve_impl ( const Rhs &  b, Dest &  x  ) const

inline

Definition at line 150 of file IncompleteCholesky.h.

    {
      eigen_assert(m_factorizationIsOk && "factorize() should be called first");
      if (m_perm.rows() == b.rows())  x = m_perm * b;
      else                            x = b;
      x = m_scale.asDiagonal() * x;
      x = m_L.template triangularView<Lower>().solve(x);
      x = m_L.adjoint().template triangularView<Upper>().solve(x);
      x = m_scale.asDiagonal() * x;
      if (m_perm.rows() == b.rows())
        x = m_perm.inverse() * x;
    }

Code

analyzePattern()

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

template<typename MatrixType >

void Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::analyzePattern ( const MatrixType &  mat )

inline

Computes the fill reducing permutation vector using the sparsity pattern of mat.

Definition at line 112 of file IncompleteCholesky.h.

    {
      OrderingType ord;
      PermutationType pinv;
      ord(mat.template selfadjointView<UpLo>(), pinv);
      if(pinv.size()>0) m_perm = pinv.inverse();
      else              m_perm.resize(0);
      m_L.resize(mat.rows(), mat.cols());
      m_analysisIsOk = true;
      m_isInitialized = true;
      m_info = Success;
    }

Code

cols()

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

EIGEN_CONSTEXPR Index Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::cols ( void  ) const

inline

Returns

number of columns of the factored matrix

Definition at line 88 of file IncompleteCholesky.h.

{ return m_L.cols(); }

Code

compute()

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

template<typename MatrixType >

void Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::compute ( const MatrixType &  mat )

inline

Computes or re-computes the incomplete Cholesky factorization of the input matrix mat

It is a shortcut for a sequential call to the analyzePattern() and factorize() methods.

See also

analyzePattern(), factorize()

Definition at line 142 of file IncompleteCholesky.h.

    {
      analyzePattern(mat);
      factorize(mat);
    }

Code

factorize() [1/2]

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

template<typename MatrixType >

void Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::factorize

(

const MatrixType &

mat

)

Performs the numerical factorization of the input matrix mat.

The method analyzePattern() or compute() must have been called beforehand with a matrix having the same pattern.

See also

compute(), analyzePattern()

factorize() [2/2]

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

template<typename MatrixType_ >

void Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::factorize

(

const MatrixType_ &

mat

)

Definition at line 191 of file IncompleteCholesky.h.

{
  using std::sqrt;
  eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
 
  // Dropping strategy : Keep only the p largest elements per column, where p is the number of elements in the column of the original matrix. Other strategies will be added
 
  // Apply the fill-reducing permutation computed in analyzePattern()
  if (m_perm.rows() == mat.rows() ) // To detect the null permutation
  {
    // The temporary is needed to make sure that the diagonal entry is properly sorted
    FactorType tmp(mat.rows(), mat.cols());
    tmp = mat.template selfadjointView<UpLo_>().twistedBy(m_perm);
    m_L.template selfadjointView<Lower>() = tmp.template selfadjointView<Lower>();
  }
  else
  {
    m_L.template selfadjointView<Lower>() = mat.template selfadjointView<UpLo_>();
  }
 
  Index n = m_L.cols();
  Index nnz = m_L.nonZeros();
  Map<VectorSx> vals(m_L.valuePtr(), nnz);         //values
  Map<VectorIx> rowIdx(m_L.innerIndexPtr(), nnz);  //Row indices
  Map<VectorIx> colPtr( m_L.outerIndexPtr(), n+1); // Pointer to the beginning of each row
  VectorIx firstElt(n-1); // for each j, points to the next entry in vals that will be used in the factorization
  VectorList listCol(n);  // listCol(j) is a linked list of columns to update column j
  VectorSx col_vals(n);   // Store a  nonzero values in each column
  VectorIx col_irow(n);   // Row indices of nonzero elements in each column
  VectorIx col_pattern(n);
  col_pattern.fill(-1);
  StorageIndex col_nnz;
 
 
  // Computes the scaling factors
  m_scale.resize(n);
  m_scale.setZero();
  for (Index j = 0; j < n; j++)
    for (Index k = colPtr[j]; k < colPtr[j+1]; k++)
    {
      m_scale(j) += numext::abs2(vals(k));
      if(rowIdx[k]!=j)
        m_scale(rowIdx[k]) += numext::abs2(vals(k));
    }
 
  m_scale = m_scale.cwiseSqrt().cwiseSqrt();
 
  for (Index j = 0; j < n; ++j)
    if(m_scale(j)>(std::numeric_limits<RealScalar>::min)())
      m_scale(j) = RealScalar(1)/m_scale(j);
    else
      m_scale(j) = 1;
 
  // TODO disable scaling if not needed, i.e., if it is roughly uniform? (this will make solve() faster)
 
  // Scale and compute the shift for the matrix
  RealScalar mindiag = NumTraits<RealScalar>::highest();
  for (Index j = 0; j < n; j++)
  {
    for (Index k = colPtr[j]; k < colPtr[j+1]; k++)
      vals[k] *= (m_scale(j)*m_scale(rowIdx[k]));
    eigen_internal_assert(rowIdx[colPtr[j]]==j && "IncompleteCholesky: only the lower triangular part must be stored");
    mindiag = numext::mini(numext::real(vals[colPtr[j]]), mindiag);
  }
 
  FactorType L_save = m_L;
 
  RealScalar shift = 0;
  if(mindiag <= RealScalar(0.))
    shift = m_initialShift - mindiag;
 
  m_info = NumericalIssue;
 
  // Try to perform the incomplete factorization using the current shift
  int iter = 0;
  do
  {
    // Apply the shift to the diagonal elements of the matrix
    for (Index j = 0; j < n; j++)
      vals[colPtr[j]] += shift;
 
    // jki version of the Cholesky factorization
    Index j=0;
    for (; j < n; ++j)
    {
      // Left-looking factorization of the j-th column
      // First, load the j-th column into col_vals
      Scalar diag = vals[colPtr[j]];  // It is assumed that only the lower part is stored
      col_nnz = 0;
      for (Index i = colPtr[j] + 1; i < colPtr[j+1]; i++)
      {
        StorageIndex l = rowIdx[i];
        col_vals(col_nnz) = vals[i];
        col_irow(col_nnz) = l;
        col_pattern(l) = col_nnz;
        col_nnz++;
      }
      {
        typename std::list<StorageIndex>::iterator k;
        // Browse all previous columns that will update column j
        for(k = listCol[j].begin(); k != listCol[j].end(); k++)
        {
          Index jk = firstElt(*k); // First element to use in the column
          eigen_internal_assert(rowIdx[jk]==j);
          Scalar v_j_jk = numext::conj(vals[jk]);
 
          jk += 1;
          for (Index i = jk; i < colPtr[*k+1]; i++)
          {
            StorageIndex l = rowIdx[i];
            if(col_pattern[l]<0)
            {
              col_vals(col_nnz) = vals[i] * v_j_jk;
              col_irow[col_nnz] = l;
              col_pattern(l) = col_nnz;
              col_nnz++;
            }
            else
              col_vals(col_pattern[l]) -= vals[i] * v_j_jk;
          }
          updateList(colPtr,rowIdx,vals, *k, jk, firstElt, listCol);
        }
      }
 
      // Scale the current column
      if(numext::real(diag) <= 0)
      {
        if(++iter>=10)
          return;
 
        // increase shift
        shift = numext::maxi(m_initialShift,RealScalar(2)*shift);
        // restore m_L, col_pattern, and listCol
        vals = Map<const VectorSx>(L_save.valuePtr(), nnz);
        rowIdx = Map<const VectorIx>(L_save.innerIndexPtr(), nnz);
        colPtr = Map<const VectorIx>(L_save.outerIndexPtr(), n+1);
        col_pattern.fill(-1);
        for(Index i=0; i<n; ++i)
          listCol[i].clear();
 
        break;
      }
 
      RealScalar rdiag = sqrt(numext::real(diag));
      vals[colPtr[j]] = rdiag;
      for (Index k = 0; k<col_nnz; ++k)
      {
        Index i = col_irow[k];
        //Scale
        col_vals(k) /= rdiag;
        //Update the remaining diagonals with col_vals
        vals[colPtr[i]] -= numext::abs2(col_vals(k));
      }
      // Select the largest p elements
      // p is the original number of elements in the column (without the diagonal)
      Index p = colPtr[j+1] - colPtr[j] - 1 ;
      Ref<VectorSx> cvals = col_vals.head(col_nnz);
      Ref<VectorIx> cirow = col_irow.head(col_nnz);
      internal::QuickSplit(cvals,cirow, p);
      // Insert the largest p elements in the matrix
      Index cpt = 0;
      for (Index i = colPtr[j]+1; i < colPtr[j+1]; i++)
      {
        vals[i] = col_vals(cpt);
        rowIdx[i] = col_irow(cpt);
        // restore col_pattern:
        col_pattern(col_irow(cpt)) = -1;
        cpt++;
      }
      // Get the first smallest row index and put it after the diagonal element
      Index jk = colPtr(j)+1;
      updateList(colPtr,rowIdx,vals,j,jk,firstElt,listCol);
    }
 
    if(j==n)
    {
      m_factorizationIsOk = true;
      m_info = Success;
    }
  } while(m_info!=Success);
}

Code

info()

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

ComputationInfo Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::info ( ) const

inline

Reports whether previous computation was successful.

It triggers an assertion if *this has not been initialized through the respective constructor, or a call to compute() or analyzePattern().

Returns

Success if computation was successful, NumericalIssue if the matrix appears to be negative.

Definition at line 99 of file IncompleteCholesky.h.

    {
      eigen_assert(m_isInitialized && "IncompleteCholesky is not initialized.");
      return m_info;
    }

Code

matrixL()

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

const FactorType& Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::matrixL ( ) const

inline

Returns

the sparse lower triangular factor L

Definition at line 164 of file IncompleteCholesky.h.

{ eigen_assert(m_factorizationIsOk && "factorize() should be called first"); return m_L; }

Code

permutationP()

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

const PermutationType& Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::permutationP ( ) const

inline

Returns

the fill-in reducing permutation P (can be empty for a natural ordering)

Definition at line 170 of file IncompleteCholesky.h.

{ eigen_assert(m_analysisIsOk && "analyzePattern() should be called first"); return m_perm; }

Code

rows()

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

EIGEN_CONSTEXPR Index Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::rows ( void  ) const

inline

Returns

number of rows of the factored matrix

Definition at line 85 of file IncompleteCholesky.h.

{ return m_L.rows(); }

Code

scalingS()

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

const VectorRx& Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::scalingS ( ) const

inline

Returns

a vector representing the scaling factor S

Definition at line 167 of file IncompleteCholesky.h.

{ eigen_assert(m_factorizationIsOk && "factorize() should be called first"); return m_scale; }

Code

setInitialShift()

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

void Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::setInitialShift ( RealScalar  shift )

inline

Set the initial shift parameter $\sigma$.

Definition at line 107 of file IncompleteCholesky.h.

{ m_initialShift = shift; }

Code

updateList()

template<typename Scalar , int UpLo_, typename OrderingType >

void Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType >::updateList ( Ref< const VectorIx >  colPtr, Ref< VectorIx >  rowIdx, Ref< VectorSx >  vals, const Index &  col, const Index &  jk, VectorIx &  firstElt, VectorList &  listCol  )

inlineprivate

Definition at line 374 of file IncompleteCholesky.h.

{
  if (jk < colPtr(col+1) )
  {
    Index p = colPtr(col+1) - jk;
    Index minpos;
    rowIdx.segment(jk,p).minCoeff(&minpos);
    minpos += jk;
    if (rowIdx(minpos) != rowIdx(jk))
    {
      //Swap
      std::swap(rowIdx(jk),rowIdx(minpos));
      std::swap(vals(jk),vals(minpos));
    }
    firstElt(col) = internal::convert_index<StorageIndex,Index>(jk);
    listCol[rowIdx(jk)].push_back(internal::convert_index<StorageIndex,Index>(col));
  }
}

Code

Member Data Documentation

m_analysisIsOk

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

bool Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::m_analysisIsOk

protected

Definition at line 176 of file IncompleteCholesky.h.

m_factorizationIsOk

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

bool Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::m_factorizationIsOk

protected

Definition at line 177 of file IncompleteCholesky.h.

m_info

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

ComputationInfo Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::m_info

protected

Definition at line 178 of file IncompleteCholesky.h.

m_initialShift

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

RealScalar Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::m_initialShift

protected

Definition at line 175 of file IncompleteCholesky.h.

m_isInitialized

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

bool Eigen::SparseSolverBase< Derived >::m_isInitialized

mutableprotected

Definition at line 123 of file SparseSolverBase.h.

m_L

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

FactorType Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::m_L

protected

Definition at line 173 of file IncompleteCholesky.h.

m_perm

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

PermutationType Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::m_perm

protected

Definition at line 179 of file IncompleteCholesky.h.

m_scale

template<typename Scalar , int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int>>

VectorRx Eigen::IncompleteCholesky< Scalar, UpLo_, OrderingType_ >::m_scale

protected

Definition at line 174 of file IncompleteCholesky.h.

---

The documentation for this class was generated from the following file:

• IncompleteCholesky.h