Skip to content

Integer Programming and Combinatoral Optimization

  • Bergner M
  • Caprara A
  • Furini F
  • et al.
N/ACitations
Citations of this article
134Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Dantzig-Wolfe decomposition is well-known to provide strong dual bounds for specially structured mixed integer programs (MIPs) in practice. However, the method is not implemented in any state-of-the-art MIP solver: it needs tailoring to the particular problem; the decomposition must be determined from the typical bordered block-diagonal matrix structure; the resulting column generation subproblems must be solved efficiently; etc. We provide a computational proof-of-concept that the process can be automated in principle, and that strong dual bounds can be obtained on general MIPs for which a solution by a decomposition has not been the first choice. We perform an extensive computational study on the 0-1 dynamic knapsack problem (without block-diagonal structure) and on general MIPLIB2003 instances. Our results support that Dantzig-Wolfe reformulation may hold more promise as a general-purpose tool than previously acknowledged by the research community. © 2011 Springer-Verlag.

Cite

CITATION STYLE

APA

Bergner, M., Caprara, A., Furini, F., Lübbecke, M. E., Malaguti, E., & Traversi, E. (2011). Integer Programming and Combinatoral Optimization. (O. Günlük & G. J. Woeginger, Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6655, pp. 39–51). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-20807-2

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free