diff --git a/doc/SparseLinearSystems.dox b/doc/SparseLinearSystems.dox
index b7f5c600b..ba6a12035 100644
--- a/doc/SparseLinearSystems.dox
+++ b/doc/SparseLinearSystems.dox
@@ -6,34 +6,61 @@ In Eigen, there are several methods available to solve linear systems when the c
 
 \section TutorialSparseSolverList List of sparse solvers
 
-%Eigen currently provides a limited set of built-in solvers, as well as wrappers to external solver libraries.
-They are summarized in the following table:
+%Eigen currently provides a wide set of built-in solvers, as well as wrappers to external solver libraries.
+They are summarized in the following tables:
+
+\subsection TutorialSparseSolverList_Direct Built-in direct solvers
+
+<table class="manual">
+<tr><th>Class</th><th>Solver kind</th><th>Matrix kind</th><th>Features related to performance</th>
+    <th>License</th><th class="width20em"><p>Notes</p></th></tr>
+
+<tr><td>SimplicialLLT \n <tt>#include<Eigen/\link SparseCholesky_Module SparseCholesky\endlink></tt></td><td>Direct LLt factorization</td><td>SPD</td><td>Fill-in reducing</td>
+    <td>LGPL</td>
+    <td>SimplicialLDLT is often preferable</td></tr>
+
+<tr><td>SimplicialLDLT \n <tt>#include<Eigen/\link SparseCholesky_Module SparseCholesky\endlink></tt></td><td>Direct LDLt factorization</td><td>SPD</td><td>Fill-in reducing</td>
+    <td>LGPL</td>
+    <td>Recommended for very sparse and not too large problems (e.g., 2D Poisson eq.)</td></tr>
+
+<tr><td>SparseLU \n <tt>#include<Eigen/\link SparseLU_Module SparseLU\endlink></tt></td> <td>LU factorization </td>
+    <td>Square </td><td>Fill-in reducing, Leverage fast dense algebra</td>
+    <td>MPL2</td>
+    <td>optimized for small and large problems with irregular patterns </td></tr>
+
+<tr><td>SparseQR \n <tt>#include<Eigen/\link SparseQR_Module SparseQR\endlink></tt></td> <td> QR factorization</td>
+    <td>Any, rectangular</td><td> Fill-in reducing</td>
+    <td>MPL2</td>
+    <td>recommended for least-square problems, has a basic rank-revealing feature</td></tr>
+ </table>
+
+\subsection TutorialSparseSolverList_Iterative Built-in iterative solvers
+
+<table class="manual">
+<tr><th>Class</th><th>Solver kind</th><th>Matrix kind</th><th>Supported preconditioners, [default]</th>
+    <th>License</th><th class="width20em"><p>Notes</p></th></tr>
+
+<tr><td>ConjugateGradient \n <tt>#include<Eigen/\link IterativeLinearSolvers_Module IterativeLinearSolvers\endlink></tt></td> <td>Classic iterative CG</td><td>SPD</td>
+    <td>IdentityPreconditioner, [DiagonalPreconditioner], IncompleteCholesky</td>
+    <td>MPL2</td>
+    <td>Recommended for large symmetric problems (e.g., 3D Poisson eq.)</td></tr>
+
+<tr><td>LeastSquaresConjugateGradient \n <tt>#include<Eigen/\link IterativeLinearSolvers_Module IterativeLinearSolvers\endlink></tt></td><td>CG for rectangular least-square problem</td><td>Rectangular</td>
+    <td>IdentityPreconditioner, [LeastSquareDiagonalPreconditioner]</td>
+    <td>MPL2</td>
+    <td>Solve for min |A'Ax-b|^2 without forming A'A</td></tr>
+
+<tr><td>BiCGSTAB \n <tt>#include<Eigen/\link IterativeLinearSolvers_Module IterativeLinearSolvers\endlink></tt></td><td>Iterative stabilized bi-conjugate gradient</td><td>Square</td>
+    <td>IdentityPreconditioner, [DiagonalPreconditioner], IncompleteLUT</td>
+    <td>MPL2</td>
+    <td>To speedup the convergence, try it with the \ref IncompleteLUT preconditioner.</td></tr>
+</table>
+
+\subsection TutorialSparseSolverList_Wrapper Wrappers to external solvers
 
 <table class="manual">
 <tr><th>Class</th><th>Module</th><th>Solver kind</th><th>Matrix kind</th><th>Features related to performance</th>
     <th>Dependencies,License</th><th class="width20em"><p>Notes</p></th></tr>
-<tr><td>SimplicialLLT    </td><td>\link SparseCholesky_Module SparseCholesky \endlink</td><td>Direct LLt factorization</td><td>SPD</td><td>Fill-in reducing</td>
-    <td>built-in, LGPL</td>
-    <td>SimplicialLDLT is often preferable</td></tr>
-<tr><td>SimplicialLDLT   </td><td>\link SparseCholesky_Module SparseCholesky \endlink</td><td>Direct LDLt factorization</td><td>SPD</td><td>Fill-in reducing</td>
-    <td>built-in, LGPL</td>
-    <td>Recommended for very sparse and not too large problems (e.g., 2D Poisson eq.)</td></tr>
-<tr><td>ConjugateGradient</td><td>\link IterativeLinearSolvers_Module IterativeLinearSolvers \endlink</td><td>Classic iterative CG</td><td>SPD</td><td>Preconditionning</td>
-    <td>built-in, MPL2</td>
-    <td>Recommended for large symmetric problems (e.g., 3D Poisson eq.)</td></tr>
-<tr><td>LeastSquaresConjugateGradient</td><td>\link IterativeLinearSolvers_Module IterativeLinearSolvers \endlink</td><td>CG for rectangular least-square problem</td><td>Rectangular</td><td>Preconditionning</td>
-    <td>built-in, MPL2</td>
-    <td>Solve for min |A'Ax-b|^2 without forming A'A</td></tr>
-<tr><td>BiCGSTAB</td><td>\link IterativeLinearSolvers_Module IterativeLinearSolvers \endlink</td><td>Iterative stabilized bi-conjugate gradient</td><td>Square</td><td>Preconditionning</td>
-    <td>built-in, MPL2</td>
-    <td>To speedup the convergence, try it with the \ref IncompleteLUT preconditioner.</td></tr>
-<tr><td>SparseLU</td> <td>\link SparseLU_Module SparseLU \endlink </td> <td>LU factorization </td>
-    <td>Square </td><td>Fill-in reducing, Leverage fast dense algebra</td>
-    <td> built-in, MPL2</td> <td>optimized for small and large problems with irregular patterns </td></tr>
-<tr><td>SparseQR</td> <td>\link SparseQR_Module SparseQR \endlink</td> <td> QR factorization</td>
-    <td>Any, rectangular</td><td> Fill-in reducing</td>
-    <td>built-in, MPL2</td><td>recommended for least-square problems, has a basic rank-revealing feature</td></tr>
-<tr> <th colspan="7"> Wrappers to external solvers </th></tr>
 <tr><td>PastixLLT \n PastixLDLT \n PastixLU</td><td>\link PaStiXSupport_Module PaStiXSupport \endlink</td><td>Direct LLt, LDLt, LU factorizations</td><td>SPD \n SPD \n Square</td><td>Fill-in reducing, Leverage fast dense algebra, Multithreading</td>
     <td>Requires the <a href="http://pastix.gforge.inria.fr">PaStiX</a> package, \b CeCILL-C </td>
     <td>optimized for tough problems and symmetric patterns</td></tr>