← 研踪 PaperTrailarXiv:2611.16138 · v1
Individually Fair Facility Location
首次提交 2026-11-12 09:02
版本更新 2026-11-12 09:02
雷达观测 2026-11-13 14:16
摘要
We study individually fair facility location, where each client must connect to an open facility within a prescribed radius and the objective is to minimize total opening and connection cost. We show that preserving the radii changes the approximability of metric facility location fundamentally. For every fixed radius violation below three, no deterministic polynomial-time algorithm achieves a sublogarithmic cost factor unless $\mathrm{P}=\mathrm{NP}$. The hardness holds with uniform opening costs and uniform radii. It contradicts the strict-fairness constant-factor guarantee recently claimed by Liu, Xu, and Zhang [Theory of Computing Systems, Vol. 70 (2026)]. We then establish a positive result for arbitrary client radii. We give a randomized polynomial-time algorithm that has cost at most $1.725$ times the strictly fair optimum and serves every client within three times its fair radius (in the Appendix we show how this can be de-randomized). We use the approach of Byrka, Srinivasan and Swamy [IPCO 2010]. The facility-location radius factor three is therefore optimal among deterministic polynomial-time algorithms with constant cost factor, unless $\mathrm{P}=\mathrm{NP}$. Experiments on real-world data indicate that the worst case radius bound is rarely realized in practice: the algorithm serves over 99.8% of clients within their stated radius, and never beyond 1.5 times it. Our code is available at https://github.com/milindmaiti/IFFL_Implementation.
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Individually Fair Facility Location
We study individually fair facility location, where each client must connect to an open facility within a prescribed radius and the objective is to minimize total opening and connection cost. We show that preserving the radii changes the approximability of metric facility location fundamentally. For every fixed radius violation below three, no deterministic polynomial-time algorithm achieves a sublogarithmic cost factor unless $\mathrm{P}=\mathrm{NP}$. The hardness holds with uniform opening costs and uniform radii. It contradicts the strict-fairness constant-factor guarantee recently claimed by Liu, Xu, and Zhang [Theory of Computing Systems, Vol. 70 (2026)]. We then establish a positive result for arbitrary client radii. We give a randomized polynomial-time algorithm that has cost at most $1.725$ times the strictly fair optimum and serves every client within three times its fair radius (in the Appendix we show how this can be de-randomized). We use the approach of Byrka, Srinivasan and Swamy [IPCO 2010]. The facility-location radius factor three is therefore optimal among deterministic polynomial-time algorithms with constant cost factor, unless $\mathrm{P}=\mathrm{NP}$. Experiments on real-world data indicate that the worst case radius bound is rarely realized in practice: the algorithm serves over 99.8% of clients within their stated radius, and never beyond 1.5 times it. Our code is available at https://github.com/milindmaiti/IFFL_Implementation.
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