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 logK 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(nε− 1log(ε− 1logK)) time, improving on the algorithm of Badanidiyuru and Vondrák that takes O(nε− 1log(ε− 1K)) time.
- Approximation algorithms
- Streaming algorithms
- Submodular function maximization
ASJC Scopus subject areas
- Theoretical Computer Science
- Computational Theory and Mathematics