Solving MIPs via Scaling-based Augmentation
Abstract
Augmentation methods for mixed-integer (linear) programs are a class of primal solution approaches in which a current iterate is augmented to a better solution or proved optimal. It is well known that the performance of these methods, i.e., number of iterations needed, can theoretically be improved by scaling methods. We extend these results by an improved and extended convergence analysis, which shows that bit scaling and geometric scaling theoretically perform similarly well in the worst case for 0/1 polytopes as well as show that in some cases geometric scaling can outperform bit scaling arbitrarily. We also investigate the performance of implementations of these methods, where the augmentation directions are computed by a MIP solver. It turns out that the number of iterations is usually low. While scaling methods usually do not improve the performance for easier problems, in the case of hard mixed-integer optimization problems they allow to compute solutions of very good quality and are often superior.
Cite
@article{arxiv.1509.03206,
title = {Solving MIPs via Scaling-based Augmentation},
author = {Pierre Le Bodic and Jeffrey W. Pavelka and Marc E. Pfetsch and Sebastian Pokutta},
journal= {arXiv preprint arXiv:1509.03206},
year = {2015}
}
Comments
v1 is 30 pages long and contains the article itself. v2 is 82 pages long as we have appended the "online supplement" which reports detailed experiments results v3 is 82 pages long and contains an updated version of the article, as well as the same "online supplement" as in v2