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Cleaning pass on rcond estimator.

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This commit is contained in:
Gael Guennebaud
2016-04-14 16:45:41 +02:00
parent d8a3bdaa24
commit 3551dea887
5 changed files with 99 additions and 150 deletions
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39
Eigen/src/Cholesky/LDLT.h
View File

@@ -193,12 +193,12 @@ template<typename _MatrixType, int _UpLo> class LDLT
LDLT& compute(const EigenBase<InputType>& matrix);
/** \returns an estimate of the reciprocal condition number of the matrix of
* which *this is the LDLT decomposition.
* which \c *this is the LDLT decomposition.
*/
RealScalar rcond() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return ReciprocalConditionNumberEstimate(m_l1_norm, *this);
return internal::rcond_estimate_helper(m_l1_norm, *this);
}
template <typename Derived>
@@ -216,10 +216,12 @@ template<typename _MatrixType, int _UpLo> class LDLT
MatrixType reconstructedMatrix() const;
/** \returns the decomposition itself to allow generic code to do
* ldlt.adjoint().solve(rhs).
*/
const LDLT<MatrixType, UpLo>& adjoint() const { return *this; };
/** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
*
* This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
* \code x = decomposition.adjoint().solve(b) \endcode
*/
const LDLT& adjoint() const { return *this; };
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
@@ -456,22 +458,15 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputTyp
// Compute matrix L1 norm = max abs column sum.
m_l1_norm = RealScalar(0);
if (_UpLo == Lower) {
for (int col = 0; col < size; ++col) {
const RealScalar abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() +
m_matrix.row(col).head(col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm) {
m_l1_norm = abs_col_sum;
}
}
} else {
for (int col = 0; col < a.cols(); ++col) {
const RealScalar abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() +
m_matrix.row(col).tail(size - col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm) {
m_l1_norm = abs_col_sum;
}
}
// TODO move this code to SelfAdjointView
for (Index col = 0; col < size; ++col) {
RealScalar abs_col_sum;
if (_UpLo == Lower)
abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
else
abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm)
m_l1_norm = abs_col_sum;
}
m_transpositions.resize(size);

41
Eigen/src/Cholesky/LLT.h
View File

@@ -136,13 +136,13 @@ template<typename _MatrixType, int _UpLo> class LLT
LLT& compute(const EigenBase<InputType>& matrix);
/** \returns an estimate of the reciprocal condition number of the matrix of
* which *this is the Cholesky decomposition.
*/
* which \c *this is the Cholesky decomposition.
*/
RealScalar rcond() const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
return ReciprocalConditionNumberEstimate(m_l1_norm, *this);
return internal::rcond_estimate_helper(m_l1_norm, *this);
}
/** \returns the LLT decomposition matrix
@@ -169,10 +169,12 @@ template<typename _MatrixType, int _UpLo> class LLT
return m_info;
}
/** \returns the decomposition itself to allow generic code to do
* llt.adjoint().solve(rhs).
*/
const LLT<MatrixType, UpLo>& adjoint() const { return *this; };
/** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
*
* This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
* \code x = decomposition.adjoint().solve(b) \endcode
*/
const LLT& adjoint() const { return *this; };
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
@@ -411,22 +413,15 @@ LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>
// Compute matrix L1 norm = max abs column sum.
m_l1_norm = RealScalar(0);
if (_UpLo == Lower) {
for (int col = 0; col < size; ++col) {
const RealScalar abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() +
m_matrix.row(col).head(col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm) {
m_l1_norm = abs_col_sum;
}
}
} else {
for (int col = 0; col < a.cols(); ++col) {
const RealScalar abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() +
m_matrix.row(col).tail(size - col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm) {
m_l1_norm = abs_col_sum;
}
}
// TODO move this code to SelfAdjointView
for (Index col = 0; col < size; ++col) {
RealScalar abs_col_sum;
if (_UpLo == Lower)
abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
else
abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
if (abs_col_sum > m_l1_norm)
m_l1_norm = abs_col_sum;
}
m_isInitialized = true;