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.
Full Text: PDF
Imprint and privacy statement
For the imprint and privacy statement we refer to the Imprint of ZIB.
© 2008-2022 by Zuse Institute Berlin (ZIB).