Abstract
We study the experimental consequences of a recent theoretical result by Achlioptas et al. that shows that conventional models of random problems are trivially insoluble in the limit. We survey the literature to identify experimental studies that lie within the scope of this result. We then estimate theoretically and measure experimentally the size at which problems start to become trivially insoluble. Our results demonstrate that most (but not all) of these experimental studies are luckily unaffected by this result. We also study an alternative model of random problems that does not suffer from this asymptotic weakness. We show that, at a typical problem size used in experimental studies, this model looks similar to conventional models. Finally, we generalize this model so that we can independently adjust the constraint tightness and density
Supported by EPSRC awards GR/L/24014 and GR/K/65706. The authors wish to thank other members of the APES research group. We are especially grateful to Ian Gent who derived the expected number of cliques in a random graph
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MacIntyre, E., Prosser, P., Smith, B., Walsh, T. (1998). Random Constraint Satisfaction: theory meets practice. In: Maher, M., Puget, JF. (eds) Principles and Practice of Constraint Programming — CP98. CP 1998. Lecture Notes in Computer Science, vol 1520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49481-2_24
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DOI: https://doi.org/10.1007/3-540-49481-2_24
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