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authorGravatar Hauke Heibel <hauke.heibel@gmail.com>2010-08-05 08:56:19 +0200
committerGravatar Hauke Heibel <hauke.heibel@gmail.com>2010-08-05 08:56:19 +0200
commit3dd822586288ae14ef5cecef166a54c803d355a8 (patch)
tree322120e83e65b039f70c64531b1b726a8b47ab79
parent976d7c19e82d2b25640e8b7a95376ac09041ab80 (diff)
Added more detailed docs to the QR decompositions classes.
-rw-r--r--Eigen/src/QR/ColPivHouseholderQR.h8
-rw-r--r--Eigen/src/QR/FullPivHouseholderQR.h8
-rw-r--r--Eigen/src/QR/HouseholderQR.h9
3 files changed, 21 insertions, 4 deletions
diff --git a/Eigen/src/QR/ColPivHouseholderQR.h b/Eigen/src/QR/ColPivHouseholderQR.h
index cab13ff0d..f34658fa9 100644
--- a/Eigen/src/QR/ColPivHouseholderQR.h
+++ b/Eigen/src/QR/ColPivHouseholderQR.h
@@ -34,7 +34,13 @@
*
* \param MatrixType the type of the matrix of which we are computing the QR decomposition
*
- * This class performs a rank-revealing QR decomposition using Householder transformations.
+ * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b Q and \b R
+ * such that
+ * \f[
+ * \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.
*
* This decomposition performs column pivoting in order to be rank-revealing and improve
* numerical stability. It is slower than HouseholderQR, and faster than FullPivHouseholderQR.
diff --git a/Eigen/src/QR/FullPivHouseholderQR.h b/Eigen/src/QR/FullPivHouseholderQR.h
index 201d63128..e228aeb44 100644
--- a/Eigen/src/QR/FullPivHouseholderQR.h
+++ b/Eigen/src/QR/FullPivHouseholderQR.h
@@ -34,7 +34,13 @@
*
* \param MatrixType the type of the matrix of which we are computing the QR decomposition
*
- * This class performs a rank-revealing QR decomposition using Householder transformations.
+ * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b Q and \b R
+ * such that
+ * \f[
+ * \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.
*
* 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.
diff --git a/Eigen/src/QR/HouseholderQR.h b/Eigen/src/QR/HouseholderQR.h
index fe316d2ee..f8d759772 100644
--- a/Eigen/src/QR/HouseholderQR.h
+++ b/Eigen/src/QR/HouseholderQR.h
@@ -36,8 +36,13 @@
*
* \param MatrixType the type of the matrix of which we are computing the QR decomposition
*
- * This class performs a QR decomposition using Householder transformations. The result is
- * stored in a compact way compatible with LAPACK.
+ * This class performs a QR decomposition of a matrix \b A into matrices \b Q and \b R
+ * such that
+ * \f[
+ * \mathbf{A} = \mathbf{Q} \, \mathbf{R}
+ * \f]
+ * by using Householder transformations. Here, \b Q a unitary matrix and \b R an upper triangular matrix.
+ * The result is stored in a compact way compatible with LAPACK.
*
* Note that no pivoting is performed. This is \b not a rank-revealing decomposition.
* If you want that feature, use FullPivHouseholderQR or ColPivHouseholderQR instead.