  We give a general approach for solving optimization problems on noisy minor
free graphs, where a \delta-fraction of edges and vertices are adversarially
corrupted. The noisy setting was first considered by Magen and Moharrami and
they gave a (1 + \delta)-estimation algorithm for the independent set problem.
Later, Chan and Har-Peled designed a local search algorithm that finds a (1 +
O(\delta))-approximate independent set. However, nothing was known regarding
other problems in the noisy setting. Our main contribution is a general
LP-based framework that yields a (1 + O(\delta log m log log m))-approximation
algorithm for noisy MAX-k-CSPs on m clauses.
