The computational complexity of simultaneous diophantine approximation problems
SIAM Journal on Computing
On the complexity of H-coloring
Journal of Combinatorial Theory Series B
Discrete Applied Mathematics
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
Simple and Fast Algorithms for Linear and Integer Programs with Two Variables Per Inequality
SIAM Journal on Computing
Tractable constraints on ordered domains
Artificial Intelligence
The complexity of mean payoff games on graphs
Theoretical Computer Science
Closure properties of constraints
Journal of the ACM (JACM)
Discrete Event Dynamic Systems
Constraints, consistency and closure
Artificial Intelligence
Introduction to the Maximum Solution Problem
Complexity of Constraints
Making bound consistency as effective as arc consistency
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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We define the class of the so-called monotone constraint satisfaction problems (MON-CSP). MON-CSP forms a subclass of the class of min-closed (respectively, max-closed) constraint satisfaction problems of Jeavons and Cooper (Artificial Intelligence 79 (1995) 327). We prove that for all problems in the class MON-CSP there exists a very fast and very simple algorithm for testing feasibility. We then show that a number of well-known results from the literature are special cases of MON-CSP: (1) Satisfiability of Horn formulae; (2) graph homomorphisms to directed graphs with an X@?-numbering; (3) monotone integer programming with two variables per inequality; (4) project scheduling under AND/OR precedence constraints. Our results provide a unified algorithmic approach to all these problems.