Approximability of Monotone Submodular Function Maximization under Cardinality and Matroid Constraints in the Streaming Model

Chien-Chung Huang (1) , Naonori Kakimura (2) , Simon Mauras (3) , Yuichi Yoshida (4)

1 DI-ENS - Département d'informatique - ENS Paris
2 Keio University [Tokyo]
3 IRIF (UMR_8243) - Institut de Recherche en Informatique Fondamentale
4 NII - National Institute of Informatics

Maximizing a monotone submodular function under various constraints is a classical and intensively studied problem. However, in the single-pass streaming model, where the elements arrive one by one and an algorithm can store only a small fraction of input elements, there is much gap in our knowledge, even though several approximation algorithms have been proposed in the literature. In this work, we present the first lower bound on the approximation ratios for cardinality and matroid constraints that beat 1 − 1 e in the single-pass streaming model. Let n be the number of elements in the stream. Then, we prove that any (randomized) streaming algorithm for a cardinality constraint with approximation ratio 2

Domaines: Algorithme et structure de données [cs.DS]

Chien-Chung Huang, Naonori Kakimura, Simon Mauras, Yuichi Yoshida. Approximability of Monotone Submodular Function Maximization under Cardinality and Matroid Constraints in the Streaming Model. 2021. ⟨hal-03099888⟩