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Constraint satisfaction problems (CSPs) lie at the
heart of many modern industrial and commercial tasks. An
important new collection of CSPs has recently been emerging
that differ from classical problems in that they impose constraints
on the class of algorithms that can be used to solve them.
In computer network applications, these constraints arise as
the variables within the CSP are located at physically distinct
devices that cannot communicate. At each instant, every variable
only knows if all its constraints are met or at least one is
not. Consequently, the CSP’s solution must be found using a
decentralized approach. Existing algorithms for solving CSPs
are either centralized or distributed, both of which fundamentally
violate these algorithmic constraints. In this article we
present the first algorithm for solving CSPs that satisfies these
new constraints. It is fully decentralized, making no use of
a centralized controller or message-passing between variables.
We prove that this algorithm converges with probability one to
a satisfying assignment whenever one exists. Surprisingly, we
experimentally demonstrate that the time the algorithm takes to
find a satisfying assignment is competitive with bothWalkSat and
Survey Propagation, two popular and efficient CSP solvers. That
is, despite its decentralized nature the algorithm is remarkably
fast. This raises new questions about the relationship between
information sharing and algorithm performance
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