Prophet Inequality for Bipartite Matching: Merits of Being Simple and Non Adaptive

We consider Bayesian online selection problem of a matching in bipartite graphs, i.e., online weighted matching problem with edge arrivals where online algorithm knows distributions of weights, that corresponds to the intersection of two matroids in Kleinberg and Weinberg [35] model. We consider a simple class of non adaptive vertex-additive policies that assign static prices to all vertices in the graph and accept each edge only if its weight exceeds the sum of the prices of the edge's endpoints. We show existence of a vertex-additive policy with the expected payoff of at least one third of the prophet's payoff and present gradient decent type algorithm that quickly converges to the desired vector of vertex prices. This improves the adaptive online policy of Kleinberg and Weinberg for the intersection of two matroids in two ways: our policy is non adaptive and has better approximation guarantee of 3 instead of previous guarantees 5.82 of Kleinberg and Weinberg and 2•e=5.43 of Feldman et al. [23] against the prophet. We give a complementary lower bound of 2.25 for any online algorithm in the bipartite matching setting.