Maximum coverage problem
http://dbpedia.org/resource/Maximum_coverage_problem an entity of type: WikicatSetFamilies
En algorithmique, le problème de couverture maximale consiste à couvrir un nombre maximal d'éléments avec au plus k sous-ensembles mis à disposition. Ce problème algorithmique est NP-dur et il existe des algorithmes d'approximation pour le résoudre. C'est une variante du problème de couverture par ensembles.
rdf:langString
The maximum coverage problem is a classical question in computer science, computational complexity theory, and operations research.It is a problem that is widely taught in approximation algorithms. As input you are given several sets and a number . The sets may have some elements in common. You must select at most of these sets such that the maximum number of elements are covered, i.e. the union of the selected sets has maximal size. Formally, (unweighted) Maximum Coverage
rdf:langString
rdf:langString
Problème de couverture maximale
rdf:langString
Maximum coverage problem
xsd:integer
24221954
xsd:integer
1106927273
rdf:langString
En algorithmique, le problème de couverture maximale consiste à couvrir un nombre maximal d'éléments avec au plus k sous-ensembles mis à disposition. Ce problème algorithmique est NP-dur et il existe des algorithmes d'approximation pour le résoudre. C'est une variante du problème de couverture par ensembles.
rdf:langString
The maximum coverage problem is a classical question in computer science, computational complexity theory, and operations research.It is a problem that is widely taught in approximation algorithms. As input you are given several sets and a number . The sets may have some elements in common. You must select at most of these sets such that the maximum number of elements are covered, i.e. the union of the selected sets has maximal size. Formally, (unweighted) Maximum Coverage Instance: A number and a collection of sets .Objective: Find a subset of sets, such that and the number of covered elements is maximized. The maximum coverage problem is NP-hard, and can be approximated within under standard assumptions. This result essentially matches the approximation ratio achieved by the generic greedy algorithm used for maximization of submodular functions with a cardinality constraint.
xsd:nonNegativeInteger
10646