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authorGravatar Jitse Niesen <jitse@maths.leeds.ac.uk>2013-12-31 18:06:28 +0000
committerGravatar Jitse Niesen <jitse@maths.leeds.ac.uk>2013-12-31 18:06:28 +0000
commita58325ac2f7be7326be358ac51c4f0eebcf7fbf9 (patch)
tree130705198df8c3dd5e7504e4ae16dc1efa50776b /Eigen
parent4cd4be97a7165e6e45ee60aee23b9342af03c491 (diff)
Minor corrections in QR docs.
Diffstat (limited to 'Eigen')
-rw-r--r--Eigen/QR4
-rw-r--r--Eigen/src/QR/FullPivHouseholderQR.h8
2 files changed, 7 insertions, 5 deletions
diff --git a/Eigen/QR b/Eigen/QR
index ac5b02693..8c7c6162e 100644
--- a/Eigen/QR
+++ b/Eigen/QR
@@ -15,7 +15,9 @@
*
* This module provides various QR decompositions
* This module also provides some MatrixBase methods, including:
- * - MatrixBase::qr(),
+ * - MatrixBase::householderQr()
+ * - MatrixBase::colPivHouseholderQr()
+ * - MatrixBase::fullPivHouseholderQr()
*
* \code
* #include <Eigen/QR>
diff --git a/Eigen/src/QR/FullPivHouseholderQR.h b/Eigen/src/QR/FullPivHouseholderQR.h
index e6ab172b3..44eaa1b1a 100644
--- a/Eigen/src/QR/FullPivHouseholderQR.h
+++ b/Eigen/src/QR/FullPivHouseholderQR.h
@@ -33,13 +33,13 @@ struct traits<FullPivHouseholderQRMatrixQReturnType<MatrixType> >
*
* \param MatrixType the type of the matrix of which we are computing the QR decomposition
*
- * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b Q and \b R
+ * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b P', \b Q and \b R
* such that
* \f[
- * \mathbf{A} \, \mathbf{P} = \mathbf{Q} \, \mathbf{R}
+ * \mathbf{P} \, \mathbf{A} \, \mathbf{P}' = \mathbf{Q} \, \mathbf{R}
* \f]
- * by using Householder transformations. Here, \b P is a permutation matrix, \b Q a unitary matrix and \b R an
- * upper triangular matrix.
+ * by using Householder transformations. Here, \b P and \b P' are permutation matrices, \b Q a unitary matrix
+ * and \b R an upper triangular matrix.
*
* This decomposition performs a very prudent full pivoting in order to be rank-revealing and achieve optimal
* numerical stability. The trade-off is that it is slower than HouseholderQR and ColPivHouseholderQR.