Improved Streaming Algorithms for Maximizing Monotone Submodular Functions under a Knapsack Constraint

Chien Chung Huang, Naonori Kakimura

研究成果: Article査読

抄録

In this paper, we consider the problem of maximizing a monotone submodular function subject to a knapsack constraint in a streaming setting. In such a setting, elements arrive sequentially and at any point in time, and the algorithm can store only a small fraction of the elements that have arrived so far. For the special case that all elements have unit sizes (i.e., the cardinality-constraint case), one can find a (0.5 - ε) -approximate solution in O(Kε- 1) space, where K is the knapsack capacity (Badanidiyuru et al. KDD 2014). The approximation ratio is recently shown to be optimal (Feldman et al. STOC 2020). In this work, we propose a (0.4 - ε) -approximation algorithm for the knapsack-constrained problem, using space that is a polynomial of K and ε. This improves on the previous best ratio of 0.363 - ε with space of the same order. Our algorithm is based on a careful combination of various ideas to transform multiple-pass streaming algorithms into a single-pass one.

本文言語English
ページ(範囲)879-902
ページ数24
ジャーナルAlgorithmica
83
3
DOI
出版ステータスPublished - 2021 3

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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