Squashed 'third_party/eigen/' changes from 61d72f6..cf794d3
Change-Id: I9b814151b01f49af6337a8605d0c42a3a1ed4c72
git-subtree-dir: third_party/eigen
git-subtree-split: cf794d3b741a6278df169e58461f8529f43bce5d
diff --git a/doc/TopicLinearAlgebraDecompositions.dox b/doc/TopicLinearAlgebraDecompositions.dox
index 8649cc2..d9db677 100644
--- a/doc/TopicLinearAlgebraDecompositions.dox
+++ b/doc/TopicLinearAlgebraDecompositions.dox
@@ -4,6 +4,7 @@
This page presents a catalogue of the dense matrix decompositions offered by Eigen.
For an introduction on linear solvers and decompositions, check this \link TutorialLinearAlgebra page \endlink.
+To get an overview of the true relative speed of the different decompositions, check this \link DenseDecompositionBenchmark benchmark \endlink.
\section TopicLinAlgBigTable Catalogue of decompositions offered by Eigen
@@ -113,10 +114,22 @@
<tr><th class="inter" colspan="9">\n Singular values and eigenvalues decompositions</th></tr>
<tr>
+ <td>BDCSVD (divide \& conquer)</td>
+ <td>-</td>
+ <td>One of the fastest SVD algorithms</td>
+ <td>Excellent</td>
+ <td>Yes</td>
+ <td>Singular values/vectors, least squares</td>
+ <td>Yes (and does least squares)</td>
+ <td>Excellent</td>
+ <td>Blocked bidiagonalization</td>
+ </tr>
+
+ <tr>
<td>JacobiSVD (two-sided)</td>
<td>-</td>
<td>Slow (but fast for small matrices)</td>
- <td>Excellent-Proven<sup><a href="#note3">3</a></sup></td>
+ <td>Proven<sup><a href="#note3">3</a></sup></td>
<td>Yes</td>
<td>Singular values/vectors, least squares</td>
<td>Yes (and does least squares)</td>
@@ -132,7 +145,7 @@
<td>Yes</td>
<td>Eigenvalues/vectors</td>
<td>-</td>
- <td>Good</td>
+ <td>Excellent</td>
<td><em>Closed forms for 2x2 and 3x3</em></td>
</tr>
@@ -249,13 +262,14 @@
<dt><b>Implicit Multi Threading (MT)</b></dt>
<dd>Means the algorithm can take advantage of multicore processors via OpenMP. "Implicit" means the algortihm itself is not parallelized, but that it relies on parallelized matrix-matrix product rountines.</dd>
<dt><b>Explicit Multi Threading (MT)</b></dt>
- <dd>Means the algorithm is explicitely parallelized to take advantage of multicore processors via OpenMP.</dd>
+ <dd>Means the algorithm is explicitly parallelized to take advantage of multicore processors via OpenMP.</dd>
<dt><b>Meta-unroller</b></dt>
<dd>Means the algorithm is automatically and explicitly unrolled for very small fixed size matrices.</dd>
<dt><b></b></dt>
<dd></dd>
</dl>
+
*/
}