Streaming Algorithms for Maximizing Monotone Submodular Functions Under a Knapsack Constraint

Chien Chung Huang, Naonori Kakimura, Yuichi Yoshida

研究成果: Article査読

5 被引用数 (Scopus)

抄録

In this paper, we consider the problem of maximizing a monotone 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 - ε) -approximation algorithm, requiring only a single pass through the data; moreover, we propose a (0.4 - ε) -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 ε.

本文言語English
ジャーナルAlgorithmica
DOI
出版ステータスAccepted/In press - 2019

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • コンピュータ サイエンスの応用
  • 応用数学

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