Guess free maximization of submodular and linear sums

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Abstract

We consider the problem of maximizing the sum of a monotone submodular function and a linear function subject to a general solvable polytope constraint. Recently, Sviridenko et al. [16] described an algorithm for this problem whose approximation guarantee is optimal in some intuitive and formal senses. Unfortunately, this algorithm involves a guessing step which makes it less clean and significantly affects its time complexity. In this work we describe a clean alternative algorithm that uses a novel weighting technique in order to avoid the problematic guessing step while keeping the same approximation guarantee as the algorithm of [16].

Original languageAmerican English
Title of host publicationAlgorithms and Data Structures - 16th International Symposium, WADS 2019, Proceedings
EditorsZachary Friggstad, Mohammad R. Salavatipour, Jörg-Rüdiger Sack
PublisherSpringer Verlag
Pages380-394
Number of pages15
ISBN (Print)9783030247652
DOIs
StatePublished - 2019
Event16th International Symposium on Algorithms and Data Structures, WADS 2019 - Edmonton, Canada
Duration: 5 Aug 20197 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11646 LNCS

Conference

Conference16th International Symposium on Algorithms and Data Structures, WADS 2019
Country/TerritoryCanada
CityEdmonton
Period5/08/197/08/19

Keywords

  • Continuous greedy
  • Curvature
  • Submodular maximization

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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