1 research outputs found
Unconstrained Submodular Maximization with Constant Adaptive Complexity
In this paper, we consider the unconstrained submodular maximization problem.
We propose the first algorithm for this problem that achieves a tight
-approximation guarantee using
adaptive rounds and a linear number of function evaluations. No previously
known algorithm for this problem achieves an approximation ratio better than
using less than rounds of adaptivity, where 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 -approximation guarantee for this
problem while keeping the same adaptive and query complexities.Comment: Authors are listed in alphabetical orde