Andrei Krokhin
Durham
Robust algorithms for constraint satisfaction problems
In a constraint satisfaction problem (CSP), the goal is to assign values
to a given set of variables subject to constraints on possible
combinations of values for certain subsets of variables.
Polynomial-time algorithms for (tractable) CSPs identify satisfiable
instances, and usually treat all unsatisfiable instance the same,
whether nearly satisfiable or very unsatisfiable. When can such
algorithms be made to also identify near-satisfiable instances? A
polynomial-time algorithm for a CSP is called robust, if, for any almost
satisfiable instance, it outputs an assignment that satisfies almost all
constraints (possibly with some loss). We will discuss a
characterisation (due to Barto and Kozik) of CSPs that admit robust
algorithms at all, and present our work (joint with V. Dalmau) towards
characterising CSPs that admit robust algorithms with relatively small
loss.