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mark deprecated sparse solvers as so.

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This commit is contained in:
Gael Guennebaud
2011-10-24 09:51:02 +02:00
parent 39d4585bff
commit a997dacc67
9 changed files with 47 additions and 24 deletions
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17
unsupported/Eigen/src/SparseExtra/SparseLLT.h
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@@ -25,7 +25,8 @@
#ifndef EIGEN_SPARSELLT_H
#define EIGEN_SPARSELLT_H
/** \ingroup Sparse_Module
/** \deprecated use class SimplicialLDLT, or class SimplicialLLT, class ConjugateGradient
* \ingroup Sparse_Module
*
* \class SparseLLT
*
@@ -52,16 +53,18 @@ class SparseLLT
typedef _MatrixType MatrixType;
typedef typename MatrixType::Index Index;
/** Creates a dummy LLT factorization object with flags \a flags. */
SparseLLT(int flags = 0)
/** \deprecated the entire class is deprecated
* Creates a dummy LLT factorization object with flags \a flags. */
EIGEN_DEPRECATED SparseLLT(int flags = 0)
: m_flags(flags), m_status(0)
{
m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision();
}
/** Creates a LLT object and compute the respective factorization of \a matrix using
/** \deprecated the entire class is deprecated
* Creates a LLT object and compute the respective factorization of \a matrix using
* flags \a flags. */
SparseLLT(const MatrixType& matrix, int flags = 0)
EIGEN_DEPRECATED SparseLLT(const MatrixType& matrix, int flags = 0)
: m_matrix(matrix.rows(), matrix.cols()), m_flags(flags), m_status(0)
{
m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision();
@@ -179,7 +182,7 @@ void SparseLLT<_MatrixType,Backend>::compute(const _MatrixType& a)
// TODO estimate the number of non zeros
m_matrix.setZero();
m_matrix.reserve(a.nonZeros()*2);
m_matrix.reserve(a.nonZeros()*10);
for (Index j = 0; j < size; ++j)
{
Scalar x = internal::real(a.coeff(j,j));
@@ -222,7 +225,7 @@ void SparseLLT<_MatrixType,Backend>::compute(const _MatrixType& a)
for (typename AmbiVector<Scalar,Index>::Iterator it(tempVector, m_precision*rx); it; ++it)
{
// FIXME use insertBack
m_matrix.insert(it.index(), j) = it.value() * y;
m_matrix.insertBack(it.index(), j) = it.value() * y;
}
}
m_matrix.finalize();