Mathematical Programming Computation, Volume 8, Issue 1, March 2016
Improving branch-and-cut performance by random sampling
Matteo Fischetti, Andrea Lodi, Michele Monaci, Domenico Salvagnin, Andrea Tramontani
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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