Abstract:We provide a full characterization of applicability of The Local Consistency Checking algorithm to solving the non-uniform Constraint Satisfaction Problems. This settles the conjecture of Larose and Zádori.
“…This paper sharpens a result of [1] that characterizes the applicability of local consistency algorithms for finite constraint languages.…”
Section: Introductionmentioning
confidence: 95%
“…All known tractable cases are solvable either by the few subpowers algorithm [16], by local consistency algorithms [1,14], or by a combination of these two. This paper sharpens a result of [1] that characterizes the applicability of local consistency algorithms for finite constraint languages.…”
We show that every constraint satisfaction problem over a fixed constraint language that has bounded relational width has also relational width (2, 3). Together with known results this gives a trichotomy for width: a constraint satisfaction problem has either relational width 1, or relational width (2, 3) (and no smaller width), or does not have bounded relational width.
“…This paper sharpens a result of [1] that characterizes the applicability of local consistency algorithms for finite constraint languages.…”
Section: Introductionmentioning
confidence: 95%
“…All known tractable cases are solvable either by the few subpowers algorithm [16], by local consistency algorithms [1,14], or by a combination of these two. This paper sharpens a result of [1] that characterizes the applicability of local consistency algorithms for finite constraint languages.…”
We show that every constraint satisfaction problem over a fixed constraint language that has bounded relational width has also relational width (2, 3). Together with known results this gives a trichotomy for width: a constraint satisfaction problem has either relational width 1, or relational width (2, 3) (and no smaller width), or does not have bounded relational width.
“…. , 9}, one unary relation R 1 = {2, 3}, one binary relation R 2 = {(1, 2), (2,3), (3,4), (4,8)} and one ternary relation R 3 = {(3, 6, 7), (4, 5, 9)}. The graph Inc(B) is shown on Fig.…”
Section: Example 4 (I)mentioning
confidence: 99%
“…For example in the caterpillar from Fig. 1, the extreme non-leaves are 2 and 4, the extreme pendant blocks are R 2 (1, 2), R 1 (2), R 2 (4,8), and R 3 (4,5,9), and the terminal elements are 1,2,4,5,8,9.…”
Section: Example 4 (I)mentioning
confidence: 99%
“…The most important duality probably is bounded treewidth duality which is equivalent to definability in Datalog (see [6,16]). Structures with this duality have been recently characterised in algebraic terms (see [2], also [5]). Two other well-understood dualities are finite duality [28,32,33] and tree duality [11,16].…”
The study of constraint satisfaction problems definable in various fragments of Datalog has recently gained considerable importance. We consider constraint satisfaction problems that are definable in the smallest natural recursive fragment of Datalog -monadic linear Datalog with at most one EDB per rule, and also in the smallest non-linear extension of this fragment. We give combinatorial and algebraic characterisations of such problems, in terms of homomorphism dualities and lattice operations, respectively. We then apply our results to study graph H-colouring problems.
We study the computational complexity of the general network satisfaction problem for a finite relation algebra A with a normal representation B. If B contains a non-trivial equivalence relation with a finite number of equivalence classes, then the network satisfaction problem for A is NP-hard. As a second result, we prove hardness if B has domain size at least three and contains no non-trivial equivalence relations but a symmetric atom a with a forbidden triple (a, a, a), that is, a ≤ a • a. We illustrate how to apply our conditions on two small relation algebras.
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