Decomposition.md

@@ -24,15 +24,15 @@ In a distributed-memory setting, where data are scattered across
 processors, two factors are important: equal division of work across
 processors and reduction of the communication overhead.
 A typical approach, is to formulate the problem as a 
-graph-partitioning problem: the physical domain is divided into sub-domains (_Domain decomposition_)
+graph-partitioning problem: the physical domain is divided into sub-sub-domains (_Domain decomposition_)
  (vertices of the graph), each of them  carrying a weight modelling the computational cost.
  The communication pattern between sub-domains is represented as links
-between the sub-domains (edges of the graph) with weights formalizing
+between the sub-sub-domains (edges of the graph) with weights formalizing
 the communication cost (_Generate graph model_). The requirement of balanced computation with
 minimal communication then translates to the optimization problem of
 finding a graph partitioning where each group contains the same sum of
 weights, and the sum of the cut edges is minimal (_Find optimal decomposition_). As final step
-the decomposition can be post-process further more to be for optimal, based on factors not considered by the optimization process, like merging vertex to create bigger sub-domains.
+the decomposition can be post-process further more to be for optimal, based on factors not considered by the optimization process, like merging vertex, or merging sub-sub-domain, to create bigger sub-domains.
  Even if a model decomposition it is not bind to a graph model it is true that until now is the main approach
 
 * [CartDecomposition](http://ppmcore.mpi-cbg.de/doxygen/openfpm_pdata/classCartDecomposition.html)