A New Approximation Guarantee for Monotone Submodular Function Maximization via Discrete Convexity

Authors Tasuku Soma , Yuichi Yoshida

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Tasuku Soma
  • The University of Tokyo, Tokyo, Japan
Yuichi Yoshida
  • National Institute of Informatics, Preferred Infrastructure, Tokyo, Japan

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Tasuku Soma and Yuichi Yoshida. A New Approximation Guarantee for Monotone Submodular Function Maximization via Discrete Convexity. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 99:1-99:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


In monotone submodular function maximization, approximation guarantees based on the curvature of the objective function have been extensively studied in the literature. However, the notion of curvature is often pessimistic, and we rarely obtain improved approximation guarantees, even for very simple objective functions. In this paper, we provide a novel approximation guarantee by extracting an M^{natural}-concave function h:2^E -> R_+, a notion in discrete convex analysis, from the objective function f:2^E -> R_+. We introduce a novel notion called the M^{natural}-concave curvature of a given set function f, which measures how much f deviates from an M^{natural}-concave function, and show that we can obtain a (1-gamma/e-epsilon)-approximation to the problem of maximizing f under a cardinality constraint in polynomial time, where gamma is the value of the M^{natural}-concave curvature and epsilon > 0 is an arbitrary constant. Then, we show that we can obtain nontrivial approximation guarantees for various problems by applying the proposed algorithm.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Combinatorial optimization
  • Submodular Function
  • Approximation Algorithm
  • Discrete Convex Analysis


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