Mathematical Programming Computation

, Volume 8, Issue 1, pp 113–132 | Cite as

Improving branch-and-cut performance by random sampling

  • Matteo Fischetti
  • Andrea Lodi
  • Michele Monaci
  • Domenico Salvagnin
  • Andrea Tramontani
Full Length Paper

Abstract

We discuss the variability in the performance of multiple runs of branch-and-cut mixed integer linear programming solvers, and we concentrate on the one deriving from the use of different optimal bases of the linear programming relaxations. We propose a new algorithm exploiting more than one of those bases and we show that different versions of the algorithm can be used to stabilize and improve the performance of the solver.

Keywords

Integer programming Performance variability 

Mathematics Subject Classification

90C10 Integer programming 90C11 Mixed Integer programming 90-08 Computational methods 

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Copyright information

© Springer-Verlag Berlin Heidelberg and The Mathematical Programming Society 2015

Authors and Affiliations

  • Matteo Fischetti
    • 1
  • Andrea Lodi
    • 2
  • Michele Monaci
    • 1
  • Domenico Salvagnin
    • 1
  • Andrea Tramontani
    • 3
  1. 1.DEIUniversity of PadovaPaduaItaly
  2. 2.DEIUniversity of BolognaBolognaItaly
  3. 3.CPLEX OptimizationIBMBolognaItaly

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