diff --git a/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h b/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
index 5d9dbb56e..14ff1a25d 100644
--- a/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
+++ b/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
@@ -141,16 +141,14 @@ class GeneralizedSelfAdjointEigenSolver : public SelfAdjointEigenSolver<_MatrixT
       *
       * \returns    Reference to \c *this
       *
-      * If \p options contains Ax_lBx (the default), this function computes eigenvalues
-      * and (if requested) the eigenvectors of the generalized eigenproblem
-      * \f$ Ax = \lambda B x \f$ with \a matA the selfadjoint
-      * matrix \f$ A \f$ and \a matB the positive definite
-      * matrix \f$ B \f$. In addition, each eigenvector \f$ x \f$
-      * satisfies the property \f$ x^* B x = 1 \f$.
-      *
-      * In addition, the two following variants can be solved via \p options:
+      * Accoring to \p options, this function computes eigenvalues and (if requested)
+      * the eigenvectors of one of the following three generalized eigenproblems:
+      * - \c Ax_lBx: \f$ Ax = \lambda B x \f$
       * - \c ABx_lx: \f$ ABx = \lambda x \f$
       * - \c BAx_lx: \f$ BAx = \lambda x \f$
+      * with \a matA the selfadjoint matrix \f$ A \f$ and \a matB the positive definite
+      * matrix \f$ B \f$.
+      * In addition, each eigenvector \f$ x \f$ satisfies the property \f$ x^* B x = 1 \f$.
       *
       * The eigenvalues() function can be used to retrieve
       * the eigenvalues. If \p options contains ComputeEigenvectors, then the
@@ -158,17 +156,19 @@ class GeneralizedSelfAdjointEigenSolver : public SelfAdjointEigenSolver<_MatrixT
       * eigenvectors().
       *
       * The implementation uses LLT to compute the Cholesky decomposition
-      * \f$ B = LL^* \f$ and calls compute(const MatrixType&, bool) to compute
-      * the eigendecomposition \f$ L^{-1} A (L^*)^{-1} \f$. This solves the
+      * \f$ B = LL^* \f$ and computes the classical eigendecomposition
+      * of the selfadjoint matrix \f$ L^{-1} A (L^*)^{-1} \f$ if \p options contains Ax_lBx
+      * and of \f$ L^{*} A L \f$ otherwise. This solves the
       * generalized eigenproblem, because any solution of the generalized
       * eigenproblem \f$ Ax = \lambda B x \f$ corresponds to a solution
       * \f$ L^{-1} A (L^*)^{-1} (L^* x) = \lambda (L^* x) \f$ of the
-      * eigenproblem for \f$ L^{-1} A (L^*)^{-1} \f$.
+      * eigenproblem for \f$ L^{-1} A (L^*)^{-1} \f$. Similar statements
+      * can be made for the two other variants.
       *
       * Example: \include SelfAdjointEigenSolver_compute_MatrixType2.cpp
       * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType2.out
       *
-      * \sa SelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
+      * \sa GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
       */
     GeneralizedSelfAdjointEigenSolver& compute(const MatrixType& matA, const MatrixType& matB,
                                                int options = ComputeEigenvectors|Ax_lBx);
diff --git a/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h b/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
index 789937437..eed56786c 100644
--- a/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
+++ b/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
@@ -68,7 +68,7 @@
   * contains an example of the typical use of this class.
   *
   * To solve the \em generalized eigenvalue problem \f$ Av = \lambda Bv \f$ and
-  * the like see the class GeneralizedSelfAdjointEigenSolver.
+  * the likes, see the class GeneralizedSelfAdjointEigenSolver.
   *
   * \sa MatrixBase::eigenvalues(), class EigenSolver, class ComplexEigenSolver
   */
diff --git a/doc/snippets/SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.cpp b/doc/snippets/SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.cpp
index f05d67da3..bbb821e02 100644
--- a/doc/snippets/SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.cpp
+++ b/doc/snippets/SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.cpp
@@ -5,7 +5,7 @@ X = MatrixXd::Random(5,5);
 MatrixXd B = X * X.transpose();
 cout << "and a random postive-definite matrix, B:" << endl << B << endl << endl;
 
-SelfAdjointEigenSolver<MatrixXd> es(A,B);
+GeneralizedSelfAdjointEigenSolver<MatrixXd> es(A,B);
 cout << "The eigenvalues of the pencil (A,B) are:" << endl << es.eigenvalues() << endl;
 cout << "The matrix of eigenvectors, V, is:" << endl << es.eigenvectors() << endl << endl;
 
diff --git a/doc/snippets/SelfAdjointEigenSolver_compute_MatrixType2.cpp b/doc/snippets/SelfAdjointEigenSolver_compute_MatrixType2.cpp
index 4b0f11003..07c92a1e4 100644
--- a/doc/snippets/SelfAdjointEigenSolver_compute_MatrixType2.cpp
+++ b/doc/snippets/SelfAdjointEigenSolver_compute_MatrixType2.cpp
@@ -3,7 +3,7 @@ MatrixXd A = X * X.transpose();
 X = MatrixXd::Random(5,5);
 MatrixXd B = X * X.transpose();
 
-SelfAdjointEigenSolver<MatrixXd> es(A,B,false);
+GeneralizedSelfAdjointEigenSolver<MatrixXd> es(A,B,EigenvaluesOnly);
 cout << "The eigenvalues of the pencil (A,B) are:" << endl << es.eigenvalues() << endl;
 es.compute(B,A,false);
 cout << "The eigenvalues of the pencil (B,A) are:" << endl << es.eigenvalues() << endl;