* applied to matrix and right hand side of the linear system, while the
* distribute() function is applied to the solution vector.
*
- * Condensation is done in four steps: first the large matrix sparsity pattern
- * is created (e.g. using @ref{DoFHandler}@p{::create_sparsity_pattern}), then the
- * sparsity pattern of the condensed matrix is made out of the large sparsity
- * pattern and the constraints. After that the global matrix is assembled and
- * finally condensed. To do these steps, you have (at least) two possibilities:
+ *
+ * @sect3{Condensing matrices and sparsity patterns}
+ *
+ * Condensation of a matrix is done in four steps: first one builds the
+ * sparsity pattern (e.g. using
+ * @ref{DoFHandler}@p{::create_sparsity_pattern}); then the sparsity pattern
+ * of the condensed matrix is made out of the original sparsity pattern and
+ * the constraints; third, the global matrix is assembled; and fourth, the
+ * matrix is finally condensed. To do these steps, you have (at least) two
+ * possibilities:
+ *
* @begin{itemize}
* @item Use two different sparsity patterns and two different matrices: you
* may eliminate the lines and rows connected with a constraint and create
* a totally new sparsity pattern and a new system matrix. This has the
- * advantage that the resulting system of equations is free from artifacts
- * of the condensation process and is therefore faster in the solution process
- * since no unnecessary multiplications occur (see below). However, there are
+ * advantage that the resulting system of equations is smaller and free from
+ * artifacts of the condensation process and is therefore faster in the solution
+ * process since no unnecessary multiplications occur (see below). However, there are
* two major drawbacks: keeping two matrices at the same time can be quite
- * unacceptable in many cases, since these matrices may be several 10 or even
- * 100 MB large. Secondly, the condensation process is quite expensive, since
- * <em>all</em> entries of the matrix have to be copied, not only
+ * unacceptable if you're short of memory. Secondly, the condensation process is
+ * expensive, since <em>all</em> entries of the matrix have to be copied, not only
* those which are subject to constraints.
*
* @item Use only one sparsity pattern and one matrix: doing it this way, the