Dynamic Facility Location via Exponential Clocks
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Abstract (arXiv)
The dynamic facility location problem is a generalization of the classic facility location problem proposed by Eisenstat, Mathieu, and Schabanel to model the dynamics of evolving social/infrastructure networks. The generalization lies in that the distance metric between clients and facilities changes over time. This leads to a trade-off between optimizing the classic objective function and the "stability" of the solution: there is a switching cost charged every time a client changes the facility to which it is connected. While the standard linear program (LP) relaxation for the classic problem naturally extends to this problem, traditional LP-rounding techniques do not, as they are often sensitive to small changes in the metric resulting in frequent switches. We present a new LP-rounding algorithm for facility location problems, which yields the first constant approximation algorithm for the dynamic facility location problem. Our algorithm installs competing exponential clocks on the clients and facilities, and connect every client by the path that repeatedly follows the smallest clock in the neighborhood. The use of exponential clocks gives rise to several properties that distinguish our approach from previous LP-roundings for facility location problems. In particular, we use no clustering and we allow clients to connect through paths of arbitrary lengths. In fact, the clustering-free nature of our algorithm is crucial for applying our LP-rounding approach to the dynamic problem.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Nov 2014.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 2.
5 papers.
- 2022A Universal Error Measure for Input Predictions Applied to Online Graph Problemscited by 14
- 2021Facility Reallocation on the Linecited by 1
- 2021Efficient Online Learning for Dynamic k-Clusteringcited by 1
- 2026Learning-Augmented Algorithms for k-median via Online Learningcited by 0
- 2026TensorDex: A Compact, Tensor-Centric Storage System for Modern AI Modelscited by 0
- 2026TensorDex: A Compact, Tensor-Centric Storage System for Modern AI Models18 Apr 2026
- 2026Learning-Augmented Algorithms for k-median via Online Learning18 Mar 2026
- 2022A Universal Error Measure for Input Predictions Applied to Online Graph Problems25 May 2022
- 2021Efficient Online Learning for Dynamic k-Clustering8 Jun 2021
- 2021Facility Reallocation on the Line23 Mar 2021