Commit b04ac0a0 by msurl

 ... ... @@ -11,11 +11,13 @@ $x \in \mathbb{R}$ is a vector describing possible solutions. If $x \in \mathbb{ Integer linear programms (ILPs) are linear programms with the additional restriction that all variables have to be integers:$x \in \mathbb{Z}$. The decision variant of an ILP is NP-complete. \\ In this thesis we use a more readable notation which does not completely sticks to this definition. We write down the inequalities in a sum notation. (Maybe an example?) But it is possible to transform them into the matrix notation. In this thesis we use a more readable notation which does not completely sticks to this definition. We write down the inequalities(and the objective function?) in a sum notation. (Maybe an example?) But it is possible to transform them into the matrix notation. \\ \\ Decision problems can be modelles with ILPs. Every variable$x_i \in \{0,1\}$denotes a possible decision. In our case we assign a decision variable for each vertex to decide if it is included in a dominating set or not. Decision problems(combinatorical optimisation problems?/ \textbf{Decisions}. Not Decision Problems) can be modelled with ILPs. Every variable$x_i \in \{0,1\}\$ denotes a possible decision(To include or not?). In our case we assign a decision variable for each vertex to decide if it is included in a dominating set or not. \\ \\ (Maybe to "formal". Maybe introduce the used notation right away and try to find textbook or publication which also uses this notation to cite) \pagebreak