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Comparing Apples and Oranges: Query Trade-off in Submodular Maximization

Author

Listed:
  • Niv Buchbinder

    (Statistics and Operations Research Department, Tel Aviv University, Tel Aviv 6997801, Israel)

  • Moran Feldman

    (Department of Mathematics and Computer Science, The Open University of Israel, Raanana 4353701, Israel)

  • Roy Schwartz

    (Department of Computer Science, Technion, Haifa 3200003, Israel)

Abstract

Fast algorithms for submodular maximization problems have a vast potential use in applicative settings, such as machine learning, social networks, and economics. Though fast algorithms were known for some special cases, only recently such algorithms were considered in the general case of maximizing a monotone submodular function subject to a matroid independence constraint. The known fast algorithm matches the best possible approximation guarantee, while trying to reduce the number of value oracle queries the algorithm performs. Our main result is a new algorithm for this general case that establishes a surprising trade-off between two seemingly unrelated quantities: the number of value oracle queries and the number of matroid independence queries performed by the algorithm. Specifically, one can decrease the former by increasing the latter, and vice versa, while maintaining the best possible approximation guarantee. Such a trade-off is very useful since various applications might incur significantly different costs in querying the value and matroid independence oracles. Furthermore, in case the rank of the matroid is O ( n c ), where n is the size of the ground set and c is an absolute constant smaller than 1, the total number of oracle queries our algorithm uses can be made to have a smaller magnitude compared to that needed by the current best known algorithm. We also provide even faster algorithms for the well-studied special cases of a cardinality constraint and a partition matroid independence constraint, both of which capture many real-world applications and have been widely studied both theoretically and in practice.

Suggested Citation

  • Niv Buchbinder & Moran Feldman & Roy Schwartz, 2017. "Comparing Apples and Oranges: Query Trade-off in Submodular Maximization," Mathematics of Operations Research, INFORMS, vol. 42(2), pages 308-329, May.
    • Handle: RePEc:inm:ormoor:v:42:y:2017:i:2:p:308-329
    DOI: 10.1287/moor.2016.0809
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    References listed on IDEAS

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    1. G. L. Nemhauser & L. A. Wolsey, 1978. "Best Algorithms for Approximating the Maximum of a Submodular Set Function," Mathematics of Operations Research, INFORMS, vol. 3(3), pages 177-188, August.
    2. CORNUEJOLS, Gérard & FISHER, Marshall L. & NEMHAUSER, George L., 1977. "Location of bank accounts to optimize float: An analytic study of exact and approximate algorithms," LIDAM Reprints CORE 292, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Nemhauser, G.L. & Wolsey, L.A., 1978. "Best algorithms for approximating the maximum of a submodular set function," LIDAM Reprints CORE 343, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. Cheng Lu & Wenguo Yang, 2024. "Fast deterministic algorithms for non-submodular maximization with strong performance guarantees," Journal of Global Optimization, Springer, vol. 89(3), pages 777-801, July.

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