Lower Bounds on Kemeny Rank Aggregation with Non-Strict Rankings.

Rank aggregation has many applications in computer science, operations research, and group decision-making. This paper introduces lower bounds on the Kemeny aggregation problem when the input rankings are non-strict (with and without ties). It generalizes some of the existing lower bounds for strict rankings to the case of non-strict rankings, and it proposes shortcuts for reducing the run time of these techniques. More specifically, we use Condorcet criterion variations and the Branch & Cut method to accelerate the lower bounding process.