Unconstrained Submodular Maximization with Constant Adaptive Complexity
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In this paper, we consider the unconstrained submodular maximization problem. We propose the first algorithm for this problem that achieves a tight (1/2-ε)-approximation guarantee using Õ(ε^-1) adaptive rounds and a linear number of function evaluations. No previously known algorithm for this problem achieves an approximation ratio better than 1/3 using less than Ω(n) rounds of adaptivity, where n is the size of the ground set. Moreover, our algorithm easily extends to the maximization of a non-negative continuous DR-submodular function subject to a box constraint and achieves a tight (1/2-ε)-approximation guarantee for this problem while keeping the same adaptive and query complexities.
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