Streaming Algorithms for Maximizing Monotone Submodular Functions Under a Knapsack Constraint

Chien Chung Huang, Naonori Kakimura, Yuichi Yoshida

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
JournalAlgorithmica
DOIs
Publication statusAccepted/In press - 2019 Jan 1

Fingerprint

Submodular Function
Knapsack
Monotone Function
Approximation algorithms
Streaming
Data storage equipment
Approximation Algorithms

Keywords

  • Constant approximation
  • Multiple-pass streaming
  • Single-pass streaming
  • Submodular functions

ASJC Scopus subject areas

  • Computer Science(all)
  • Computer Science Applications
  • Applied Mathematics

Cite this

Streaming Algorithms for Maximizing Monotone Submodular Functions Under a Knapsack Constraint. / Huang, Chien Chung; Kakimura, Naonori; Yoshida, Yuichi.

In: Algorithmica, 01.01.2019.

Research output: Contribution to journalArticle

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