Addresses comments on Eigen pull request PR-174.

* 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.

2 files changed, 18 insertions, 6 deletions

diff --git a/Eigen/src/Cholesky/LDLT.h b/Eigen/src/Cholesky/LDLT.h
index 9753c84d8..80f18977c 100644
--- a/Eigen/src/Cholesky/LDLT.h
+++ b/Eigen/src/Cholesky/LDLT.h
@@ -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;
diff --git a/Eigen/src/Cholesky/LLT.h b/Eigen/src/Cholesky/LLT.h
index b55c5bebf..94da1d52d 100644
--- a/Eigen/src/Cholesky/LLT.h
+++ b/Eigen/src/Cholesky/LLT.h
@@ -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;