Global Sensitivity Analysis for the Linear Assignment Problem
Not on the map. Why?
Abstract (arXiv)
In this paper, the following question is addressed: given a linear assignment problem, how much can the all of the individual assignment weights be perturbed without changing the optimal assignment? The extension of results involving perturbations in just one edge or one row/column are presented. Algorithms for the derivation of these bounds are provided. We also show how these bounds may be used to prevent assignment churning in a multi-vehicle guidance scenario.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 24 May 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1.
1 paper.
- 2023DAG Learning on the Permutahedroncited by 9
- 2023DAG Learning on the Permutahedron27 Jan 2023