Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization

Given a set V, the problem of unconstrained submodular maximization with modular costs (USM-MC) asks for a subset S subset of V that maximizes f(S) - c(S), where f is a non-negative, monotone, and submodular function that gauges the utility of S, and c is a nonnegative and modular function that measures the cost of S. This problem finds applications in numerous practical scenarios, such as profit maximization in viral marketing on social media. This paper presents ROI-Greedy, a polynomial time algorithm for USM-MC that returns a solution S satisfying f (S) - c (S) >= f (S*) - c (S*) - ln f(S*)/c(S*) . c(S*), where S* is the optimal solution to USM-MC. To our knowledge, ROI-Greedy is the first algorithm that provides such a strong approximation guarantee. In addition, we show that this worst-case guarantee is tight, in the sense that no polynomial time algorithm can ensure f(S) - c(S) >= (1 + epsilon) . (f(S*) - c (S*) - ln f(S*)/c(S*) . c(S*) ), for any epsilon > 0. Further, we devise a non-trivial extension of ROI-Greedy to solve the profit maximization problem, where the precise value of f (S) for any set S is unknown and can only be approximated via sampling. Extensive experiments on benchmark datasets demonstrate that ROI-Greedy significantly outperforms competing methods in terms of the trade-off between efficiency and solution quality.

Automated summary

This paper introduces ROI-Greedy, an algorithm for solving the unconstrained submodular maximization with modular costs problem, which provides a strong approximation guarantee and outperforms competing methods in terms of efficiency and solution quality. Extensive experiments on benchmark datasets demonstrate the efficacy of ROI-Greedy in finding near-optimal solutions.