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Techniques for Submodular Maximization

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Discrete Geometry and Optimization

Part of the book series: Fields Institute Communications ((FIC,volume 69))

Abstract

Maximization of a submodular function is a central problem in the algorithmic theory of combinatorial optimization. On the one hand, it has the feel of a clean and stylized problem, amenable to mathematical analysis, while on the other hand, it comfortably contains several rather different problems which are independently of interest from both theoretical and applied points of view. There have been successful analyses from the point of view of theoretical computer science, specifically approximation algorithms, and from an operations research viewpoint, specifically novel branch-and-bound methods have proven to be effective on broad subclasses of problems. To some extent, both of these points of view have validated what some practitioners have known all along: Local-search methods are very effective for many of these problems.

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Acknowledgements

Partially supported by NSF Grant CMMI–1160915.

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Authors and Affiliations

  1. IOE Department, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI, 48109-2117, USA

    Jon Lee

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  1. Jon Lee
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Correspondence to Jon Lee .

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  1. , Mathematics & Statistics, University of Calgary, University Drive NW, Calgary, T2N 1N4, Alberta, Canada

    Karoly Bezdek

  2. , Department of Computing, McMaster University, Main Street West 1280, Hamilton, L8S 4K1, Ontario, Canada

    Antoine Deza

  3. School of Engineering, Department of Management Science and Eng, Stanford University, Via Ortega 475, Stanford, 94305, California, USA

    Yinyu Ye

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Lee, J. (2013). Techniques for Submodular Maximization. In: Bezdek, K., Deza, A., Ye, Y. (eds) Discrete Geometry and Optimization. Fields Institute Communications, vol 69. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00200-2_10

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