TY - JOUR
T1 - Threshold Treewidth and Hypertree Width
AU - Ganian, Robert
AU - Schidler, André
AU - Sorge, Manuel
AU - Szeider, Stefan
N1 - Publisher Copyright:
©2022 AI Access Foundation. All rights reserved.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Treewidth and hypertree width have proven to be highly successful structural parameters in the context of the Constraint Satisfaction Problem (CSP). When either of these parameters is bounded by a constant, then CSP becomes solvable in polynomial time. However, here the order of the polynomial in the running time depends on the width, and this is known to be unavoidable; therefore, the problem is not fixed-parameter tractable parameterized by either of these width measures. Here we introduce an enhancement of tree and hypertree width through a novel notion of thresholds, allowing the associated decompositions to take into account information about the computational costs associated with solving the given CSP instance. Aside from introducing these notions, we obtain efficient theoretical as well as empirical algorithms for computing threshold treewidth and hypertree width and show that these parameters give rise to fixed-parameter algorithms for CSP as well as other, more general problems. We complement our theoretical results with experimental evaluations in terms of heuristics as well as exact methods based on SAT/SMT encodings.
AB - Treewidth and hypertree width have proven to be highly successful structural parameters in the context of the Constraint Satisfaction Problem (CSP). When either of these parameters is bounded by a constant, then CSP becomes solvable in polynomial time. However, here the order of the polynomial in the running time depends on the width, and this is known to be unavoidable; therefore, the problem is not fixed-parameter tractable parameterized by either of these width measures. Here we introduce an enhancement of tree and hypertree width through a novel notion of thresholds, allowing the associated decompositions to take into account information about the computational costs associated with solving the given CSP instance. Aside from introducing these notions, we obtain efficient theoretical as well as empirical algorithms for computing threshold treewidth and hypertree width and show that these parameters give rise to fixed-parameter algorithms for CSP as well as other, more general problems. We complement our theoretical results with experimental evaluations in terms of heuristics as well as exact methods based on SAT/SMT encodings.
UR - http://www.scopus.com/inward/record.url?scp=85138065341&partnerID=8YFLogxK
U2 - 10.1613/JAIR.1.13661
DO - 10.1613/JAIR.1.13661
M3 - Article
AN - SCOPUS:85138065341
SN - 1076-9757
VL - 74
SP - 1687
EP - 1713
JO - Journal Of Artificial Intelligence Research
JF - Journal Of Artificial Intelligence Research
ER -