Discrete combinatorial optimization has a central role in many scientific disciplines, however, for hard problems we lack linear time algorithms that would allow us to solve very large instances.

Random constraint satisfaction problems (CSPs) form a class of models in which a collection of discrete variables is subject to a set of randomly generated constraints. Inspired by paradigms in ...