Biased measures for random Constraint Satisfaction Problems: larger interaction range and asymptotic expansion
arXiv:2007.10303 · doi:10.1088/1742-5468/abb8c8
Abstract
We investigate the clustering transition undergone by an exemplary random constraint satisfaction problem, the bicoloring of -uniform random hypergraphs, when its solutions are weighted non-uniformly, with a soft interaction between variables belonging to distinct hyperedges. We show that the threshold for the transition can be further increased with respect to a restricted interaction within the hyperedges, and perform an asymptotic expansion of in the large limit. We find that , where the constant is strictly larger than for the uniform measure over solutions.
arXiv comment33 pages, 11 figures, minor corrections
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- Biased thermodynamics can explain the behaviour of smart optimization algorithms that work above the dynamical threshold
- Interacting Copies of Random Constraint Satisfaction Problems