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Weakly Submodular Function Maximization Using Local Submodularity Ratio

Authors Richard Santiago, Yuichi Yoshida

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  • 17 pages

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Author Details

Richard Santiago
  • ETH Zürich, Switzerland
Yuichi Yoshida
  • National Institute of Informatics, Tokyo, Japan


Part of this work was conducted during the first author’s visit to the National Institute of Informatics in Tokyo. The second authors was supported by JSPS KAKENHI Grant Number 18H05291.

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Richard Santiago and Yuichi Yoshida. Weakly Submodular Function Maximization Using Local Submodularity Ratio. In 31st International Symposium on Algorithms and Computation (ISAAC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 181, pp. 64:1-64:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)


Weak submodularity is a natural relaxation of the diminishing return property, which is equivalent to submodularity. Weak submodularity has been used to show that many (monotone) functions that arise in practice can be efficiently maximized with provable guarantees. In this work we introduce two natural generalizations of weak submodularity for non-monotone functions. We show that an efficient randomized greedy algorithm has provable approximation guarantees for maximizing these functions subject to a cardinality constraint. We then provide a more refined analysis that takes into account that the weak submodularity parameter may change (sometimes improving) throughout the execution of the algorithm. This leads to improved approximation guarantees in some settings. We provide applications of our results for monotone and non-monotone maximization problems.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
  • weakly submodular
  • non-monotone
  • local submodularity ratio


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