Improved streaming algorithms for maximizing monotone submodular functions under a knapsack constraint

Chien Chung Huang, Naonori Kakimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 (0.4 - ε) -approximation algorithm requiring only a single pass through the data. This improves on the currently best (0.363 - ε) -approximation algorithm. The required memory space depends only on the size of the knapsack capacity and ε.

Original languageEnglish
Title of host publicationAlgorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings
EditorsZachary Friggstad, Mohammad R. Salavatipour, Jörg-Rüdiger Sack
PublisherSpringer Verlag
Pages438-451
Number of pages14
ISBN (Print)9783030247652
DOIs
Publication statusPublished - 2019 Jan 1
Event16th International Symposium on Algorithms and Data Structures, WADS 2019 - Edmonton, Canada
Duration: 2019 Aug 52019 Aug 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11646 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Symposium on Algorithms and Data Structures, WADS 2019
CountryCanada
CityEdmonton
Period19/8/519/8/7

Keywords

  • Approximation algorithm
  • Streaming algorithm
  • Submodular functions

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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