Self-Sustained Clusters as Drivers of Computational Hardness in $p$-spin Models

Abstract

While macroscopic properties of spin glasses have been thoroughly investigated, their manifestation in the corresponding microscopic configurations is much less understood. Cases where both descriptions have been provided, such as constraint satisfaction problems, are limited to their ground state properties. To identify the emerging microscopic structures with macroscopic phases at different temperatures, we study the $p$-spin model with $p=3$. We investigate the properties of self-sustained clusters, defined as variable sets where in-cluster induced fields dominate over the field induced by out-cluster spins, giving rise to stable configurations with respect to fluctuations. We compute the entropy of self-sustained clusters as a function of temperature and their sizes. In-cluster fields properties and the difference between in-cluster and out-cluster fields support the observation of slow-evolving spins in spin models. The findings are corroborated by observations in finite dimensional lattices at low temperatures.

References in corpus (4)

• Gibbs States and the Set of Solutions of Random Constraint Satisfaction Problems
• Clusters of solutions and replica symmetry breaking in random k-satisfiability
• On the freezing of variables in random constraint satisfaction problems
• The large deviations of the whitening process in random constraint satisfaction problems

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• Slow Spin Dynamics and Self-Sustained Clusters in Sparsely Connected Systems