Learning When to Use a Decomposition

  • Markus Kruber
  • Marco E. Lübbecke
  • Axel Parmentier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10335)

Abstract

Applying a Dantzig-Wolfe decomposition to a mixed-integer program (MIP) aims at exploiting an embedded model structure and can lead to significantly stronger reformulations of the MIP. Recently, automating the process and embedding it in standard MIP solvers have been proposed, with the detection of a decomposable model structure as key element. If the detected structure reflects the (usually unknown) actual structure of the MIP well, the solver may be much faster on the reformulated model than on the original. Otherwise, the solver may completely fail. We propose a supervised learning approach to decide whether or not a reformulation should be applied, and which decomposition to choose when several are possible. Preliminary experiments with a MIP solver equipped with this knowledge show a significant performance improvement on structured instances, with little deterioration on others.

Keywords

Mixed-integer programming Branch-and-price Column generation Automatic Dantzig-Wolfe decomposition Supervised learning 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Chair of Operations ResearchRWTH Aachen UniversityAachenGermany
  2. 2.CERMICS, École des Ponts ParistechUniversité Paris EstChamps sur MarneFrance

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