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Submodular Minimization in the Context of Modern LP and MILP Methods and Solvers

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Experimental Algorithms (SEA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9125))

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Abstract

We consider the application of mixed-integer linear programming (MILP) solvers to the minimization of submodular functions. We evaluate common large-scale linear-programming (LP) techniques (e.g., column generation, row generation, dual stabilization) for solving a LP reformulation of the submodular minimization (SM) problem. We present heuristics based on the LP framework and a MILP solver. We evaluated the performance of our methods on a test bed of min-cut and matroid-intersection problems formulated as SM problems.

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Authors and Affiliations

  1. Department of Industrial and Operations Engineering, University of Michigan, 1205 Beal Ave., Ann Arbor, MI, 48109-2117, USA

    Andrew Orso, Jon Lee & Siqian Shen

Authors
  1. Andrew Orso
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  2. Jon Lee
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  3. Siqian Shen
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Corresponding author

Correspondence to Jon Lee .

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Editors and Affiliations

  1. Université Pierre et Marie Curie LIP6, Paris, France

    Evripidis Bampis

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Orso, A., Lee, J., Shen, S. (2015). Submodular Minimization in the Context of Modern LP and MILP Methods and Solvers. In: Bampis, E. (eds) Experimental Algorithms. SEA 2015. Lecture Notes in Computer Science(), vol 9125. Springer, Cham. https://doi.org/10.1007/978-3-319-20086-6_15

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  • DOI: https://doi.org/10.1007/978-3-319-20086-6_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20085-9

  • Online ISBN: 978-3-319-20086-6

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