In combinatorics and computer science, covering problems are computational problems that ask whether a certain combinatorial structure 'covers' another, or how large the structure has to be to do that. Covering problems are minimization problems and usually integer linear programs, whose dual problems are called packing problems.
The most prominent examples of covering problems are the set cover problem, which is equivalent to the hitting set problem, and its special cases, the vertex cover problem and the edge cover problem.
In the context of linear programming, one can think of any linear program as a covering problem if the coefficients in the constraint matrix, the objective function, and right-hand side are nonnegative. More precisely, consider the following general integer linear program:
Such an integer linear program is called a covering problem if for all and .
Intuition: Assume having types of object and each object of type has an associated cost of . The number indicates how many objects of type we buy. If the constraints are satisfied, it is said that is a covering (the structures that are covered depend on the combinatorial context). Finally, an optimal solution to the above integer linear program is a covering of minimal cost.
There are various kinds of covering problems in graph theory, computational geometry and more; see . Other stochastic related versions of the problem can be found.
For Petri nets, for example, the covering problem is defined as the question if for a given marking, there exists a run of the net, such that some larger (or equal) marking can be reached. Larger means here that all components are at least as large as the ones of the given marking and at least one is properly larger.
In some covering problems, the covering should satisfy some additional requirements. In particular, in the rainbow covering problem, each of the original objects has a "color", and it is required that the covering contains exactly one (or at most one) object of each color. Rainbow covering was studied e.
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thumb|280px|L'ensemble des sommets en bleu dans ce graphe est un stable maximal du graphe. En théorie des graphes, un stable – appelé aussi ensemble indépendant ou independent set en anglais – est un ensemble de sommets deux à deux non adjacents. La taille d'un stable est égale au nombre de sommets qu'il contient. La taille maximum d'un stable d'un graphe, noté I(G), est un invariant du graphe. Il peut être relié à d'autres invariants, par exemple à la taille de l'ensemble dominant maximum, noté dom(G).
thumb|upright=0.5|Optimisation linéaire dans un espace à deux dimensions (x1, x2). La fonction-coût fc est représentée par les lignes de niveau bleues à gauche et par le plan bleu à droite. L'ensemble admissible E est le pentagone vert. En optimisation mathématique, un problème d'optimisation linéaire demande de minimiser une fonction linéaire sur un polyèdre convexe. La fonction que l'on minimise ainsi que les contraintes sont décrites par des fonctions linéaires, d'où le nom donné à ces problèmes.
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