Doc: add link to doc of sparse solver concept

5 files changed, 14 insertions, 0 deletions

diff --git a/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h b/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
index ff7f08c1c..b850630a3 100644
--- a/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
+++ b/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
@@ -23,6 +23,8 @@ namespace Eigen {
   *
   * \tparam _Scalar the type of the scalar.
   *
+  * \implsparsesolverconcept
+  *
   * This preconditioner is suitable for both selfadjoint and general problems.
   * The diagonal entries are pre-inverted and stored into a dense vector.
   *
@@ -114,6 +116,8 @@ class DiagonalPreconditioner
   *
   * \tparam _Scalar the type of the scalar.
   *
+  * \implsparsesolverconcept
+  *
   * The diagonal entries are pre-inverted and stored into a dense vector.
   * 
   * \sa class LeastSquaresConjugateGradient, class DiagonalPreconditioner
@@ -172,6 +176,8 @@ class LeastSquareDiagonalPreconditioner : public DiagonalPreconditioner<_Scalar>
 /** \ingroup IterativeLinearSolvers_Module
   * \brief A naive preconditioner which approximates any matrix as the identity matrix
   *
+  * \implsparsesolverconcept
+  *
   * \sa class DiagonalPreconditioner
   */
 class IdentityPreconditioner
diff --git a/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
index a34ee7628..76e86a94a 100644
--- a/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
+++ b/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
@@ -132,6 +132,8 @@ struct traits<BiCGSTAB<_MatrixType,_Preconditioner> >
   * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix.
   * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
   *
+  * \implsparsesolverconcept
+  *
   * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
   * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
   * and NumTraits<Scalar>::epsilon() for the tolerance.
diff --git a/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
index 8f33c446d..59092dc18 100644
--- a/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
+++ b/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
@@ -118,6 +118,8 @@ struct traits<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
   *               Default is \c Lower, best performance is \c Lower|Upper.
   * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
   *
+  * \implsparsesolverconcept
+  *
   * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
   * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
   * and NumTraits<Scalar>::epsilon() for the tolerance.
diff --git a/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
index b644163f1..10b9fcc18 100644
--- a/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
+++ b/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
@@ -67,6 +67,8 @@ Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
   * \class IncompleteLUT
   * \brief Incomplete LU factorization with dual-threshold strategy
   *
+  * \implsparsesolverconcept
+  *
   * During the numerical factorization, two dropping rules are used :
   *  1) any element whose magnitude is less than some tolerance is dropped.
   *    This tolerance is obtained by multiplying the input tolerance @p droptol 
diff --git a/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h b/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
index 1d819927e..b578b2a7f 100644
--- a/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
+++ b/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
@@ -119,6 +119,8 @@ struct traits<LeastSquaresConjugateGradient<_MatrixType,_Preconditioner> >
   * \tparam _MatrixType the type of the matrix A, can be a dense or a sparse matrix.
   * \tparam _Preconditioner the type of the preconditioner. Default is LeastSquareDiagonalPreconditioner
   *
+  * \implsparsesolverconcept
+  * 
   * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
   * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
   * and NumTraits<Scalar>::epsilon() for the tolerance.