Improving branch-and-cut performance by random sampling

  • 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.

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Metadaten
Author:Matteo Fischetti, Andrea Lodi, Michele Monaci, Domenico Salvagnin, Andrea Tramontani
DOI:https://doi.org/10.1007/s12532-015-0096-0
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2015
Tag:Software; Theoretical Computer Science
Volume:8
Issue:1
Page Number:20
First Page:113
Last Page:132
Mathematical Programming Computation :MPC 2016 - Issue 1
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