Stochastic Submodular Maximization

Arash Asadpour, Hamid Nazerzadeh, Amin Saberi

Abstract: We study stochastic submodular maximization problem with respect to a cardinality constraint. Our model can capture the effect of uncertainty in different problems, such as cascade effects in social networks, capital budgeting, sensor placement, etc. We study non-adaptive and adaptive policies and give optimal constant approximation algorithms for both cases. We also bound the adaptivity gap of the problem between 1.21 and 1.59.