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bug #1150: make IncompleteCholesky more robust by iteratively increase the shift until the factorization succeed (with at most 10 attempts).

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This commit is contained in:
Gael Guennebaud 2016-01-23 22:13:54 +01:00
parent cb4e53ff7f
commit 0caa4b1531
2 changed files with 135 additions and 81 deletions
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186
Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
View File

@ -240,7 +240,7 @@ void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType
else
m_scale(j) = 1;
// FIXME disable scaling if not needed, i.e., if it is roughly uniform? (this will make solve() faster)
// 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();
@ -251,96 +251,122 @@ void IncompleteCholesky<Scalar,_UpLo, OrderingType>::factorize(const _MatrixType
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;
// 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
for (Index j=0; 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++)
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)
{
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++)
// 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++)
{
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);
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);
}
// Scale the current column
if(numext::real(diag) <= 0)
if(j==n)
{
m_info = NumericalIssue;
return;
m_factorizationIsOk = true;
m_info = Success;
}
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);
}
m_factorizationIsOk = true;
m_info = Success;
} while(m_info!=Success);
}
template<typename Scalar, int _UpLo, typename OrderingType>

30
test/incomplete_cholesky.cpp
View File

@ -1,7 +1,7 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2015-2016 Gael Guennebaud <gael.guennebaud@inria.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
@ -34,4 +34,32 @@ void test_incomplete_cholesky()
CALL_SUBTEST_1(( test_incomplete_cholesky_T<double,int>() ));
CALL_SUBTEST_2(( test_incomplete_cholesky_T<std::complex<double>, int>() ));
CALL_SUBTEST_3(( test_incomplete_cholesky_T<double,long int>() ));
#ifdef EIGEN_TEST_PART_1
// regression for bug 1150
for(int N = 1; N<20; ++N)
{
Eigen::MatrixXd b( N, N );
b.setOnes();
Eigen::SparseMatrix<double> m( N, N );
m.reserve(Eigen::VectorXi::Constant(N,4));
for( int i = 0; i < N; ++i )
{
m.insert( i, i ) = 1;
m.coeffRef( i, i / 2 ) = 2;
m.coeffRef( i, i / 3 ) = 2;
m.coeffRef( i, i / 4 ) = 2;
}
Eigen::SparseMatrix<double> A;
A = m * m.transpose();
Eigen::ConjugateGradient<Eigen::SparseMatrix<double>,
Eigen::Lower | Eigen::Upper,
Eigen::IncompleteCholesky<double> > solver( A );
VERIFY(solver.preconditioner().info() == Eigen::Success);
VERIFY(solver.info() == Eigen::Success);
}
#endif
}