Computing and Exploiting Tree-Decompositions for Solving Constraint Networks

Jégou, Philippe; Ndiaye, Samba; Terrioux, Cyril

doi:10.1007/11564751_63

Cited by 23 publications

(33 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, our study is performed on random partial structured CSPs described in [7]. All these experimentations are performed on a Linux-based PC with a Pentium IV 3.2GHz and 1GB of memory.…”

Section: Experimental Study and Discussionmentioning

confidence: 99%

“…In the table, the letter M means that at least one instance cannot be solved because it requires more than 1GB of memory. We use MCS [9] to compute tree-decompositions because it obtains the best results in the study performed in [4] on triangulation algorithms to compute a good tree-decomposition w.r.t. CSP solving.…”

Section: Experimental Study and Discussionmentioning

confidence: 99%

“…These orders are in fact provided by the cluster order and the variable ordering inside each cluster. Firstly, we give the definition of a generalized tree-decomposition [7]. The set of directed k-covering tree-decompositions of a tree-decomposition (E, T ) of G with E 1 its root cluster and k a non nil positive integer, is defined by the set of tree-decompositions (E ′ , T ′ ) of G that verify:…”

Section: Classes Of Orders and Heuristicsmentioning

confidence: 99%

“…Theorem 3 [7] Let the enumerative order be in the Class 5, the time complexity of BTD is O(exp(2(w + + k))).…”

Section: Classes Of Orders and Heuristicsmentioning

confidence: 99%

“…Recent studies (e.g. [4]) integrate as quality parameter for a decomposition, its efficiency for solving the considered CSP. This paper deals with the question of an efficient use of the considered decompositions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Extension of Complexity Bounds and Dynamic Heuristics for Tree-Decompositions of CSP

Jégou

Ndiaye

Terrioux

2006

Principles and Practice of Constraint Programming - CP 2006

Self Cite

View full text Add to dashboard Cite

Abstract. This paper deals with methods exploiting tree-decomposition approaches for solving constraint networks. We consider here the practical efficiency of these approaches by defining five classes of variable orders more and more dynamic which guarantee time complexity bounds from O(exp(w + 1)) to O(exp(2(w + k))), with w the "tree-width" of a CSP and k a constant. Finally, we assess practically their relevance.

show abstract

“…So, our study is performed on random partial structured CSPs described in [7]. All these experimentations are performed on a Linux-based PC with a Pentium IV 3.2GHz and 1GB of memory.…”

Section: Experimental Study and Discussionmentioning

confidence: 99%

Section: Experimental Study and Discussionmentioning

confidence: 99%

Section: Classes Of Orders and Heuristicsmentioning

confidence: 99%

“…Theorem 3 [7] Let the enumerative order be in the Class 5, the time complexity of BTD is O(exp(2(w + + k))).…”

Section: Classes Of Orders and Heuristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Extension of Complexity Bounds and Dynamic Heuristics for Tree-Decompositions of CSP

Jégou

Ndiaye

Terrioux

2006

Principles and Practice of Constraint Programming - CP 2006

Self Cite

View full text Add to dashboard Cite

show abstract

Dynamic Heuristics for Branch and Bound on Tree‐Decomposition of Weighted CSPs

Jégou¹,

Ndiaye²,

Terrioux³

2007

Trends in Constraint Programming

Self Cite

View full text Add to dashboard Cite

This paper deals with methods exploiting tree-decomposition approaches for solving weighted constraint networks. We consider here the practical efficiency of these approaches by defining five classes of variable orders more and more dynamic which preserve the time complexity bound. For that, we define extensions of this theoretical time complexity bound to increase the dynamic aspect of these orders. We define a constant k allowing us to extend the classical bound from O(exp(w + 1)) firstly to O(exp(w + k)), and finally to O(exp(2(w + k))), where w denotes the "tree-width" of a Weighted CSP. Finally, we assess the defined theoretical extension of the time complexity bound from a practical viewpoint.

show abstract