Polynomial-Time Data Reduction for the Subset Interconnection Design Problem - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue SIAM Journal on Discrete Mathematics Année : 2015

Polynomial-Time Data Reduction for the Subset Interconnection Design Problem

Résumé

The NP-hard Subset Interconnection Design problem, also known as Minimum Topic-Connected Overlay, is motivated by numerous applications including the design of scalable overlay networks and vacuum systems. It has as input a finite set V and a collection of subsets V 1 , V 2 ,. .. , Vm ⊆ V , and asks for a minimum-cardinality edge set E such that for the graph G = (V, E) all induced subgraphs G[V 1 ], G[V 2 ],. .. , G[Vm] are connected. We study Subset In-terconnection Design in the context of polynomial-time data reduction rules that preserve the possibility to construct optimal solutions. Our contribution is threefold: First, we show the incor-rectness of earlier polynomial-time data reduction rules. Second, we show linear-time solvability in case of a constant number m of subsets, implying fixed-parameter tractability for the parameter m. Third, we provide a fixed-parameter tractability result for small subset sizes and tree-like output graphs. To achieve our results, we elaborate on polynomial-time data reduction rules which also may be of practical use in solving Subset Interconnection Design.
Fichier principal
Vignette du fichier
Subset_Interconnection_Design-SIDMA.pdf (441.88 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

lirmm-01349211 , version 1 (27-07-2016)

Identifiants

Citer

Jiehua Chen, Christian Komusiewicz, Rolf Niedermeier, Manuel Sorge, Mathias Weller. Polynomial-Time Data Reduction for the Subset Interconnection Design Problem . SIAM Journal on Discrete Mathematics, 2015, 29 (1), pp.1-25. ⟨10.1137/140955057⟩. ⟨lirmm-01349211⟩
339 Consultations
350 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More