Improving branch-and-cut performance by random sampling

Matteo Fischetti, Andrea Lodi, Michele Monaci, Domenico Salvagnin, Andrea Tramontani
2015 Mathematical Programming Computation  
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.
doi:10.1007/s12532-015-0096-0 fatcat:jnrgly42rrhl5n3fvizgnpekta