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Addresses comments on Eigen pull request PR-174.

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* Get rid of code-duplication for real vs. complex matrices.
* Fix flipped arguments to select.
* Make the condition estimation functions free functions.
* Use Vector::Unit() to generate canonical unit vectors.
* Misc. cleanup.
This commit is contained in:
Rasmus Munk Larsen
2016-04-04 14:20:01 -07:00
parent 30242b7565
commit 86e0ed81f8
7 changed files with 177 additions and 267 deletions
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12
Eigen/src/Cholesky/LDLT.h
View File

@@ -198,7 +198,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
RealScalar rcond() const
{
eigen_assert(m_isInitialized && "LDLT is not initialized.");
return ConditionEstimator<LDLT<MatrixType, UpLo>, true >::rcond(m_l1_norm, *this);
return ReciprocalConditionNumberEstimate(m_l1_norm, *this);
}
template <typename Derived>
@@ -216,6 +216,12 @@ template<typename _MatrixType, int _UpLo> class LDLT
MatrixType reconstructedMatrix() const;
/** \returns the decomposition itself to allow generic code to do
* ldlt.transpose().solve(rhs).
*/
const LDLT<MatrixType, UpLo>& transpose() const { return *this; };
const LDLT<MatrixType, UpLo>& adjoint() const { return *this; };
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
@@ -454,14 +460,14 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputTyp
if (_UpLo == Lower) {
for (int col = 0; col < size; ++col) {
const RealScalar abs_col_sum = m_matrix.col(col).tail(size - col).cwiseAbs().sum() +
m_matrix.row(col).tail(col).cwiseAbs().sum();
m_matrix.row(col).head(col).cwiseAbs().sum();
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).tail(col).cwiseAbs().sum() +
const RealScalar abs_col_sum = m_matrix.col(col).head(col).cwiseAbs().sum() +
m_matrix.row(col).tail(size - col).cwiseAbs().sum();
if (abs_col_sum > m_l1_norm) {
m_l1_norm = abs_col_sum;

12
Eigen/src/Cholesky/LLT.h
View File

@@ -142,7 +142,7 @@ template<typename _MatrixType, int _UpLo> class LLT
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
return ConditionEstimator<LLT<MatrixType, UpLo>, true >::rcond(m_l1_norm, *this);
return ReciprocalConditionNumberEstimate(m_l1_norm, *this);
}
/** \returns the LLT decomposition matrix
@@ -169,6 +169,12 @@ template<typename _MatrixType, int _UpLo> class LLT
return m_info;
}
/** \returns the decomposition itself to allow generic code to do
* llt.transpose().solve(rhs).
*/
const LLT<MatrixType, UpLo>& transpose() const { return *this; };
const LLT<MatrixType, UpLo>& adjoint() const { return *this; };
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
@@ -409,14 +415,14 @@ LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>
if (_UpLo == Lower) {
for (int col = 0; col < size; ++col) {
const RealScalar abs_col_sum = m_matrix.col(col).tail(size - col).cwiseAbs().sum() +
m_matrix.row(col).tail(col).cwiseAbs().sum();
m_matrix.row(col).head(col).cwiseAbs().sum();
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).tail(col).cwiseAbs().sum() +
const RealScalar abs_col_sum = m_matrix.col(col).head(col).cwiseAbs().sum() +
m_matrix.row(col).tail(size - col).cwiseAbs().sum();
if (abs_col_sum > m_l1_norm) {
m_l1_norm = abs_col_sum;