Improved Multi-Pass Streaming Algorithms for Submodular Maximization with Matroid Constraints

Authors Chien-Chung Huang, Theophile Thiery, Justin Ward

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Chien-Chung Huang
  • CNRS, DI ENS, Université PSL, Paris, France
Theophile Thiery
  • School of Mathematical Sciences, Queen Mary University of London, UK
Justin Ward
  • School of Mathematical Sciences, Queen Mary University of London, UK

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Chien-Chung Huang, Theophile Thiery, and Justin Ward. Improved Multi-Pass Streaming Algorithms for Submodular Maximization with Matroid Constraints. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 176, pp. 62:1-62:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


We give improved multi-pass streaming algorithms for the problem of maximizing a monotone or arbitrary non-negative submodular function subject to a general p-matchoid constraint in the model in which elements of the ground set arrive one at a time in a stream. The family of constraints we consider generalizes both the intersection of p arbitrary matroid constraints and p-uniform hypergraph matching. For monotone submodular functions, our algorithm attains a guarantee of p+1+ε using O(p/ε)-passes and requires storing only O(k) elements, where k is the maximum size of feasible solution. This immediately gives an O(1/ε)-pass (2+ε)-approximation for monotone submodular maximization in a matroid and (3+ε)-approximation for monotone submodular matching. Our algorithm is oblivious to the choice ε and can be stopped after any number of passes, delivering the appropriate guarantee. We extend our techniques to obtain the first multi-pass streaming algorithms for general, non-negative submodular functions subject to a p-matchoid constraint. We show that a randomized O(p/ε)-pass algorithm storing O(p³klog(k)/ε³) elements gives a (p+1+γ+O(ε))-approximation, where γ is the guarantee of the best-known offline algorithm for the same problem.

Subject Classification

ACM Subject Classification
  • Theory of computation → Streaming, sublinear and near linear time algorithms
  • Theory of computation → Approximation algorithms analysis
  • submodular maximization
  • streaming algorithms
  • matroid
  • matchoid


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