Improving branch-and-cut performance by random sampling
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- Fischetti, M., Lodi, A., Monaci, M. et al. Math. Prog. Comp. (2016) 8: 113. doi:10.1007/s12532-015-0096-0
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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.