Approximation algorithm for submodular maximization under submodular cover
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:792-801, 2021.
We study a new optimization problem called submodular maximization under submodular cover (SMSC), which requires to find a fixed-size set such that one monotone submodular function $f$ is maximized subject to that another monotone submodular function $g$ is maximized approximately. SMSC is preferable to submodular function maximization when one wants to maximize two objective functions simultaneously. We propose an optimization framework for SMSC, which guarantees a constant-factor approximation. Our algorithm’s key idea is to construct a new instance of submodular function maximization from a given instance of SMSC, which can be approximated efficiently. Besides, if we are given an approximation oracle for submodular function maximization, our algorithm provably produces nearly optimal solutions. We experimentally evaluate the proposed algorithm in terms of sensor placement and movie recommendation using real-world data.