Mathematical Programming Computation

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

Improving branch-and-cut performance by random sampling

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


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.


Integer programming Performance variability 

Mathematics Subject Classification

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



This research was partially supported by MiUR, Italy (PRIN project “Mixed-Integer Nonlinear Optimization: Approaches and Applications”) and by the University of Padova (Progetto di Ateneo “Exploiting randomness in Mixed-Integer Linear Programming”). Thanks are due to two anonymous referees for their helpful comments; we also thank Hans D. Mittelmann for the use of the cluster at Arizona State University.


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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
    Email author
  • 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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