Streaming Algorithms for Maximizing Monotone Submodular Functions Under a Knapsack Constraint

In this paper, we consider the problem of maximizing amonotone submodular function subject to a knapsack constraint in the streaming setting. In particular, the elements arrive sequentially and at any point of time, the algorithm has access only to a small fraction of the data stored in primary memory. For this problem, we propose a (0.363 epsilon)-approximation algorithm, requiring only a single pass through the data; moreover, we propose a (0.4- epsilon)-approximation algorithm requiring a constant number of passes through the data. The required memory space of both algorithms depends only on the size of the knapsack capacity and epsilon.