The constraint NValue counts the number of different values assigned to a vector of variables. Propagating generalized arc consistency on this constraint is NP-hard. We show that computing even the lower bound on the number of values is NP-hard. We therefore study different approximation heuristics for this problem. We introduce three new methods for computing a lower bound on the number of values. The first two are based on the maximum independent set problem and are incomparable to a previous approach based on intervals. The last method is a linear relaxation of the problem. This gives a tighter lower bound than all other methods, but at a greater asymptotic cost.

Filtering Algorithms for the NValue Constraint

KIZILTAN, ZEYNEP;
2005

Abstract

The constraint NValue counts the number of different values assigned to a vector of variables. Propagating generalized arc consistency on this constraint is NP-hard. We show that computing even the lower bound on the number of values is NP-hard. We therefore study different approximation heuristics for this problem. We introduce three new methods for computing a lower bound on the number of values. The first two are based on the maximum independent set problem and are incomparable to a previous approach based on intervals. The last method is a linear relaxation of the problem. This gives a tighter lower bound than all other methods, but at a greater asymptotic cost.
Proc of RCRA'05, the AI*IA Workshop on Experimental Analysis and Benchmark of Algorithms for Artificial Intelligence
3
12
C. Bessiere; E. Hebrard; B. Hnich; Z. Kiziltan; T. Walsh
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/64038
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