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MPC
2016
Springer

Improving branch-and-cut performance by random sampling

3 years 7 months ago
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. Keywords integer programming, performance variability
Matteo Fischetti, Andrea Lodi, Michele Monaci, Dom
Added 08 Apr 2016
Updated 08 Apr 2016
Type Journal
Year 2016
Where MPC
Authors Matteo Fischetti, Andrea Lodi, Michele Monaci, Domenico Salvagnin, Andrea Tramontani
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