Apply suggestions from code review

Apply documentation suggestions from Timo Heister for the compute_svd function.

Co-Authored-By: Timo Heister <timo.heister@gmail.com>

diff --git a/include/deal.II/lac/lapack_full_matrix.h b/include/deal.II/lac/lapack_full_matrix.h
index a539f43ec7799511e3302ad4a6610b01718dd545..8910add669a68d355a6f29907015c89c3db18c08 100644 (file)
--- a/include/deal.II/lac/lapack_full_matrix.h
+++ b/include/deal.II/lac/lapack_full_matrix.h
@@ -818,14 +818,14 @@ public:
    * LAPACKSupport::svd. 
    * 
    * The singular value decomposition factorizes the provided matrix (A) into 
-   * three parts U, sigma and the transpose of V (V^T), such that A = U sigma 
+   * three parts: U, sigma, and the transpose of V (V^T), such that A = U sigma 
    * V^T. Sigma is a MxN matrix which contains the singular values of A on 
    * the diagonal while all the other elements are zero. U is a MxM orthogonal 
    * matrix containing the left singular vectors corresponding to the singular
    * values of A. V is a NxN orthonal matrix containing the right singular 
    * vectors corresponding the the singular values of A.
    *  
-   * Note that the variable #svd_vt contains the tranposed of V.  
+   * Note that the variable #svd_vt contains the tranpose of V and can be accessed by get_svd_vt(), while U is accessed with get_svd_u().
    */
   void
   compute_svd();