Adaptive Robust Submodular Optimization and Beyond

Shaojie Tang, Jing Yuan

Abstract: Constrained submodular maximization has been extensively studied in the recent years. In this paper, we study adaptive robust optimization with nearly submodular structure (ARONSS). Our objective is to randomly select a subset of items that maximizes the worst case value of several reward functions simultaneously. Our work differs from existing studies in two ways: (1) we study the robust optimization problem under the adaptive setting, i.e., one needs to adaptively select items based on the feedback collected from picked items, and (2) our results apply to a broad range of reward functions characterized by \(\epsilon \)-nearly submodular function. We first analyze the adaptivity gap of ARONSS and show that the gap between the best adaptive solution and the best non-adaptive solution is bounded. Then we propose an approximate solution to this problem when all reward functions are submodular. In particular, our algorithm achieves approximation ratio \((1-1/e)\) when considering a single matroid constraint. At last, we present two heuristics for the general case with nearly submodular functions. All proposed solutions are non-adaptive which are easy to implement.