  An algorithm for a constraint satisfaction problem is called robust if it
outputs an assignment satisfying at least $(1-g(\varepsilon))$-fraction of the
constraints given a $(1-\varepsilon)$-satisfiable instance, where
$g(\varepsilon) \rightarrow 0$ as $\varepsilon \rightarrow 0$. Guruswami and
Zhou conjectured a characterization of constraint languages for which the
corresponding constraint satisfaction problem admits an efficient robust
algorithm. This paper confirms their conjecture.
