Abstract
Random instances are widely used as benchmarks in evaluating algorithms for finite-domain constraint satisfaction problems (CSPs). We present an analysis that shows why deciding satisfiability of instances from some distributions is challenging for current complete methods. For a typical random CSP model, we show that when constraints are not too tight almost all unsatisfiable instances have a structural property which guarantees that unsatisfiability proofs in a certain resolution-like system must be of exponential size. This proof system can efficiently simulate the reasoning of a large class of CSP algorithms which will thus have exponential running time on these instances.
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Mitchell, D.G. (2002). Resolution Complexity of Random Constraints. In: Van Hentenryck, P. (eds) Principles and Practice of Constraint Programming - CP 2002. CP 2002. Lecture Notes in Computer Science, vol 2470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46135-3_20
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DOI: https://doi.org/10.1007/3-540-46135-3_20
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