An experimental study of minimum mean cycle algorithms

We study algorithms for the minimum mean cycle problem, a parametric version of shortest path feasibility (SPF). The three basic approaches to the problem are cycle-based, binary search, and tree-based. The first two use an SPF algorithm as a subroutine, while the latter uses a parametric approach. When implementing the SPF-based methods, one has a choice of SPF algorithms and incremental optimization strategies. There are also several ways to handle precision issues. This leads to dozens of variants, which we systematically compare. Our experimental setup is more comprehensive than in previous studies. In our experiments, the tree-based method and two implementations of the cycle-based method outperformed other approaches, including binary search.