Title: Exact MAP Inference by Avoiding Fractional Vertices,
Abstract: Given a graphical model, one essential problem is MAP inference, that is,
finding the most likely configuration of states according to the model.
Although this problem is NP-hard, large instances can be solved in practice. A
major open question is to explain why this is true. We give a natural condition
under which we can provably perform MAP inference in polynomial time. We
require that the number of fractional vertices in the LP relaxation exceeding
the optimal solution is bounded by a polynomial in the problem size. This
resolves an open question by Dimakis, Gohari, and Wainwright. In contrast, for
general LP relaxations of integer programs, known techniques can only handle a
constant number of fractional vertices whose value exceeds the optimal
solution. We experimentally verify this condition and demonstrate how efficient
various integer programming methods are at removing fractional solutions.