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Fix Incomplete Cholesky factorization. Stable but need iterative robust shift

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Desire NUENTSA 2012-12-07 15:33:26 +01:00
parent 5afaacedc6
commit 71cb78e1ba
1 changed files with 97 additions and 70 deletions
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167
unsupported/Eigen/src/IterativeSolvers/IncompleteCholesky.h
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@ -38,10 +38,10 @@ class IncompleteCholesky : internal::noncopyable
typedef Matrix<Scalar,Dynamic,1> ScalarType;
typedef Matrix<Index,Dynamic, 1> IndexType;
typedef std::vector<std::list<Index> > VectorList;
enum { UpLo = _UpLo };
public:
IncompleteCholesky() {}
IncompleteCholesky(const MatrixType& matrix)
IncompleteCholesky() : m_shift(1),m_factorizationIsOk(false) {}
IncompleteCholesky(const MatrixType& matrix) : m_shift(1),m_factorizationIsOk(false)
{
compute(matrix);
}
@ -61,6 +61,12 @@ class IncompleteCholesky : internal::noncopyable
eigen_assert(m_isInitialized && "IncompleteLLT is not initialized.");
return m_info;
}
/**
* \brief Set the initial shift parameter
*/
void setShift( Scalar shift) { m_shift = shift; }
/**
* \brief Computes the fill reducing permutation vector.
*/
@ -68,7 +74,7 @@ class IncompleteCholesky : internal::noncopyable
void analyzePattern(const MatrixType& mat)
{
OrderingType ord;
ord(mat, m_perm);
ord(mat.template selfadjointView<UpLo>(), m_perm);
m_analysisIsOk = true;
}
@ -90,10 +96,12 @@ class IncompleteCholesky : internal::noncopyable
x = m_perm.inverse() * b;
else
x = b;
x = m_scal.asDiagonal() * x;
x = m_L.template triangularView<UnitLower>().solve(x);
x = m_L.adjoint().template triangularView<Upper>().solve(x);
if (m_perm.rows() == b.rows())
x = m_perm * x;
x = m_scal.asDiagonal() * x;
}
template<typename Rhs> inline const internal::solve_retval<IncompleteCholesky, Rhs>
solve(const MatrixBase<Rhs>& b) const
@ -106,6 +114,8 @@ class IncompleteCholesky : internal::noncopyable
}
protected:
SparseMatrix<Scalar,ColMajor> m_L; // The lower part stored in CSC
ScalarType m_scal; // The vector for scaling the matrix
Scalar m_shift; //The initial shift parameter
bool m_analysisIsOk;
bool m_factorizationIsOk;
bool m_isInitialized;
@ -123,13 +133,11 @@ void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType
{
using std::sqrt;
eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
// FIXME Stability: We should probably compute the scaling factors and the shifts that are needed to ensure a succesful LLT factorization and an efficient preconditioner.
// Dropping strategies : 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() )
if (m_perm.rows() == mat.rows() ) // To detect the null permutation
m_L.template selfadjointView<Lower>() = mat.template selfadjointView<_UpLo>().twistedBy(m_perm);
else
m_L.template selfadjointView<Lower>() = mat.template selfadjointView<_UpLo>();
@ -143,65 +151,84 @@ void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType
VectorList listCol(n); // listCol(j) is a linked list of columns to update column j
ScalarType curCol(n); // Store a nonzero values in each column
IndexType irow(n); // Row indices of nonzero elements in each column
// Computes the scaling factors
m_scal.resize(n);
for (int j = 0; j < n; j++)
{
m_scal(j) = m_L.col(j).norm();
m_scal(j) = sqrt(m_scal(j));
}
// Scale and compute the shift for the matrix
Scalar mindiag = vals[0];
for (int j = 0; j < n; j++){
for (int k = colPtr[j]; k < colPtr[j+1]; k++)
vals[k] /= (m_scal(j) * m_scal(rowIdx[k]));
mindiag = std::min(vals[colPtr[j]], mindiag);
}
if(mindiag < Scalar(0.)) m_shift = m_shift - mindiag;
// Apply the shift to the diagonal elements of the matrix
for (int j = 0; j < n; j++)
vals[colPtr[j]] += m_shift;
// jki version of the Cholesky factorization
for (int j=0; j < n; ++j)
{
//Debug
bool update_j = false; //This column has received updates
//Left-looking factorize the column j
// First, load the jth column into curCol
Scalar diag = vals[colPtr[j]]; // It is assumed that only the lower part is stored
curCol.setZero();
irow.setLinSpaced(n,0,n-1);
for (int i = colPtr[j] + 1; i < colPtr[j+1]; i++)
{
curCol(rowIdx[i]) = vals[i];
irow(rowIdx[i]) = rowIdx[i];
}
std::list<int>::iterator k;
// Browse all previous columns that will update column j
for(k = listCol[j].begin(); k != listCol[j].end(); k++)
{
update_j = true;
int jk = firstElt(*k); // First element to use in the column
Scalar a_jk = vals[jk];
diag -= a_jk * a_jk;
jk += 1;
for (int i = jk; i < colPtr[*k+1]; i++)
{
curCol(rowIdx[i]) -= vals[i] * a_jk ;
}
updateList(colPtr,rowIdx,vals, *k, jk, firstElt, listCol);
}
if(update_j)
{
// Select the largest p elements
// p is the original number of elements in the column (without the diagonal)
int p = colPtr[j+1] - colPtr[j] - 1 ;
internal::QuickSplit(curCol, irow, p);
if(RealScalar(diag) <= 0)
{ //FIXME We can use heuristics (Kershaw, 1978 or above reference ) to get a dynamic shift
std::cerr << "\nNegative diagonal during Incomplete factorization...abort...\n";
m_info = NumericalIssue;
return;
}
RealScalar rdiag = sqrt(RealScalar(diag));
vals[colPtr[j]] = rdiag;
Scalar scal = Scalar(1)/rdiag;
// Insert the largest p elements in the matrix and scale them meanwhile
int cpt = 0;
for (int i = colPtr[j]+1; i < colPtr[j+1]; i++)
{
vals[i] = curCol(cpt) * scal;
rowIdx[i] = irow(cpt);
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);
{
//Left-looking factorize the column j
// First, load the jth column into curCol
Scalar diag = vals[colPtr[j]]; // It is assumed that only the lower part is stored
curCol.setZero();
irow.setLinSpaced(n,0,n-1);
for (int i = colPtr[j] + 1; i < colPtr[j+1]; i++)
{
curCol(rowIdx[i]) = vals[i];
irow(rowIdx[i]) = rowIdx[i];
}
std::list<int>::iterator k;
// Browse all previous columns that will update column j
for(k = listCol[j].begin(); k != listCol[j].end(); k++)
{
int jk = firstElt(*k); // First element to use in the column
jk += 1;
for (int i = jk; i < colPtr[*k+1]; i++)
{
curCol(rowIdx[i]) -= vals[i] * vals[jk] ;
}
updateList(colPtr,rowIdx,vals, *k, jk, firstElt, listCol);
}
// Scale the current column
if(RealScalar(diag) <= 0)
{
std::cerr << "\nNegative diagonal during Incomplete factorization... "<< j << "\n";
m_info = NumericalIssue;
return;
}
RealScalar rdiag = sqrt(RealScalar(diag));
vals[colPtr[j]] = rdiag;
for (int i = j+1; i < n; i++)
{
//Scale
curCol(i) /= rdiag;
//Update the remaining diagonals with curCol
vals[colPtr[i]] -= curCol(i) * curCol(i);
}
// Select the largest p elements
// p is the original number of elements in the column (without the diagonal)
int p = colPtr[j+1] - colPtr[j] - 1 ;
internal::QuickSplit(curCol, irow, p);
// Insert the largest p elements in the matrix
int cpt = 0;
for (int i = colPtr[j]+1; i < colPtr[j+1]; i++)
{
vals[i] = curCol(cpt);
rowIdx[i] = irow(cpt);
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);
}
m_factorizationIsOk = true;
m_isInitialized = true;
@ -218,7 +245,7 @@ inline void IncompleteCholesky<Scalar,_UpLo, OrderingType>::updateList(const Idx
Index minpos;
rowIdx.segment(jk,p).minCoeff(&minpos);
minpos += jk;
if (minpos != rowIdx(jk))
if (rowIdx(minpos) != rowIdx(jk))
{
//Swap
std::swap(rowIdx(jk),rowIdx(minpos));
@ -230,11 +257,11 @@ inline void IncompleteCholesky<Scalar,_UpLo, OrderingType>::updateList(const Idx
}
namespace internal {
template<typename _MatrixType, typename Rhs>
struct solve_retval<IncompleteCholesky<_MatrixType>, Rhs>
: solve_retval_base<IncompleteCholesky<_MatrixType>, Rhs>
template<typename _Scalar, int _UpLo, typename OrderingType, typename Rhs>
struct solve_retval<IncompleteCholesky<_Scalar, _UpLo, OrderingType>, Rhs>
: solve_retval_base<IncompleteCholesky<_Scalar, _UpLo, OrderingType>, Rhs>
{
typedef IncompleteCholesky<_MatrixType> Dec;
typedef IncompleteCholesky<_Scalar, _UpLo, OrderingType> Dec;
EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
template<typename Dest> void evalTo(Dest& dst) const