Multi-Pass Streaming Algorithms for Monotone Submodular Function Maximization

We consider maximizing a monotone submodular function under a cardinality constraint or a knapsack constraint in the streaming setting. In particular, the elements arrive sequentially and at any point of time, the algorithm has access to only a small fraction of the data stored in primary memory. We propose the following streaming algorithms taking O(ε^− 1) passes: (1) a (1 − e^− 1 − ε)-approximation algorithm for the cardinality-constrained problem, (2) a (0.5 − ε)-approximation algorithm for the knapsack-constrained problem. Both of our algorithms run deterministically in O^∗(n) time, using O^∗(K) space, where n is the size of the ground set and K is the size of the knapsack. Here the term O^∗ hides a polynomial of \(\log K\) and ε^− 1. Our streaming algorithms can also be used as fast approximation algorithms. In particular, for the cardinality-constrained problem, our algorithm takes \(O(n\varepsilon ^{-1} \log (\varepsilon ^{-1}\log K) )\) time, improving on the algorithm of Badanidiyuru and Vondrák that takes \(O(n \varepsilon ^{-1} \log (\varepsilon ^{-1} K) )\) time.