Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems

18 Nov 2015Carlo BaldassiAlessandro IngrossoCarlo LucibelloLuca SagliettiRiccardo Zecchina

We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to efficiently sample solutions of random Constraint Satisfaction Problems (CSPs). First, we extend a recent result that, using a large-deviation analysis, shows that the geometry of the space of solutions of the Binary Perceptron Learning Problem (a prototypical CSP), contains regions of very high-density of solutions... (read more)

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