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.
| Author: | Matteo Fischetti, Andrea Lodi, Michele Monaci, Domenico Salvagnin, Andrea Tramontani |
|---|---|
| DOI: | https://doi.org/10.1007/s12532-015-0096-0 |
| ISSN: | 1867-2949 |
| Parent Title (English): | Mathematical Programming Computation |
| Publisher: | Springer Science and Business Media LLC |
| Document Type: | Article |
| Language: | English |
| Year of Completion: | 2015 |
| Tag: | Software; Theoretical Computer Science |
| Volume: | 8 |
| Issue: | 1 |
| Page Number: | 20 |
| First Page: | 113 |
| Last Page: | 132 |
| Mathematical Programming Computation : | MPC 2016 - Issue 1 |

