From e798466871ceef80a5bd78eba460735fca829a8c Mon Sep 17 00:00:00 2001
From: Gael Guennebaud <g.gael@free.fr>
Date: Wed, 11 Apr 2018 10:46:11 +0200
Subject: bug #1538: update manual pages regarding BDCSVD.

---
 doc/TutorialLinearAlgebra.dox | 28 ++++++++++++++++++++++++----
 1 file changed, 24 insertions(+), 4 deletions(-)

(limited to 'doc/TutorialLinearAlgebra.dox')

diff --git a/doc/TutorialLinearAlgebra.dox b/doc/TutorialLinearAlgebra.dox
index cb92ceeae..a72724143 100644
--- a/doc/TutorialLinearAlgebra.dox
+++ b/doc/TutorialLinearAlgebra.dox
@@ -73,7 +73,7 @@ depending on your matrix and the trade-off you want to make:
         <td>ColPivHouseholderQR</td>
         <td>colPivHouseholderQr()</td>
         <td>None</td>
-        <td>++</td>
+        <td>+</td>
         <td>-</td>
         <td>+++</td>
     </tr>
@@ -85,6 +85,14 @@ depending on your matrix and the trade-off you want to make:
         <td>- -</td>
         <td>+++</td>
     </tr>
+    <tr class="alt">
+        <td>CompleteOrthogonalDecomposition</td>
+        <td>completeOrthogonalDecomposition()</td>
+        <td>None</td>
+        <td>+</td>
+        <td>-</td>
+        <td>+++</td>
+    </tr>
     <tr class="alt">
         <td>LLT</td>
         <td>llt()</td>
@@ -101,15 +109,24 @@ depending on your matrix and the trade-off you want to make:
         <td>+</td>
         <td>++</td>
     </tr>
+    <tr class="alt">
+        <td>BDCSVD</td>
+        <td>bdcSvd()</td>
+        <td>None</td>
+        <td>-</td>
+        <td>-</td>
+        <td>+++</td>
+    </tr>
     <tr class="alt">
         <td>JacobiSVD</td>
         <td>jacobiSvd()</td>
         <td>None</td>
-        <td>- -</td>
+        <td>-</td>
         <td>- - -</td>
         <td>+++</td>
     </tr>
 </table>
+To get an overview of the true relative speed of the different decompositions, check this \link DenseDecompositionBenchmark benchmark \endlink.
 
 All of these decompositions offer a solve() method that works as in the above example.
 
@@ -183,8 +200,11 @@ Here is an example:
 
 \section TutorialLinAlgLeastsquares Least squares solving
 
-The most accurate method to do least squares solving is with a SVD decomposition. Eigen provides one
-as the JacobiSVD class, and its solve() is doing least-squares solving.
+The most accurate method to do least squares solving is with a SVD decomposition.
+Eigen provides two implementations.
+The recommended one is the BDCSVD class, which scale well for large problems
+and automatically fall-back to the JacobiSVD class for smaller problems.
+For both classes, their solve() method is doing least-squares solving.
 
 Here is an example:
 <table class="example">
-- 
cgit v1.2.3