Discrete Stochastic Submodular Maximization: Adaptive vs. Non-Adaptive vs. Offline

We consider the problem of stochastic monotone submodular function maximization, subject to constraints. We give results on adaptivity gaps, and on the gap between the optimal offline and online solutions. We present a procedure that transforms a decision tree (adaptive algorithm) into a non-adaptive chain. We prove that this chain achieves at least $τ$ times the utility of the decision tree, over a product distribution and binary state space, where $τ = \min_{i,j} \Pr[x_i=j]$. This proves an adaptivity gap of $1/τ$ (which is $2$ in the case of a uniform distribution) for the problem of stochastic monotone submodular maximization subject to state-independent constraints. For a cardinality constraint, we prove that a simple adaptive greedy algorithm achieves an approximation factor of $(1-1/e^τ)$ with respect to the optimal offline solution; previously, it has been proven that the algorithm achieves an approximation factor of $(1-1/e)$ with respect to the optimal adaptive online solution. Finally, we show that there exists a non-adaptive solution for the stochastic max coverage problem that is within a factor $(1-1/e)$ of the optimal adaptive solution and within a factor of $τ(1-1/e)$ of the optimal offline solution.