Arc and path consistence revisited
Artificial Intelligence
On selecting a satisfying truth assignment (extended abstract)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
On the minimality and global consistency of row-convex constraint networks
Journal of the ACM (JACM)
Constraints, consistency and closure
Artificial Intelligence
Constraint satisfaction over connected row-convex constraints
Artificial Intelligence
Tractable tree convex constraint networks
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Combinatorial auctions with structured item graphs
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
On the tractability of smooth constraint satisfaction problems
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Properties of tree convex constraints
Artificial Intelligence
Solving connected row convex constraints by variable elimination
Artificial Intelligence
Fast algorithm for connected row convex constraints
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Decomposing combinatorial auctions and set packing problems
Journal of the ACM (JACM)
| Hi-index | 0.00 |
We identify tractable classes of constraints based on the following simple property of a constraint: "At every infeasible point, there exist two directions such that with respect to any other feasible point, moving along at least one of these two directions decreases a certain distance metric to it". We show that connected row convex (CRC) constraints, arc-consistent consecutive tree convex (ACCTC) constraints, etc fit this characterization, and are therefore amenable to extremely simple polynomial-time randomized algorithms--the complexities of which are shown to be much less than that of the corresponding (known) deterministic algorithms and the (generic) lower bounds for establishing path-consistency. On a related note, we also provide a simple polynomial-time deterministic algorithm for finding tree embeddings of variable domains (if they exist) for establishing tree convexity in path-consistent networks.