Fully Scalable MPC Algorithms for Euclidean Clustering: Constant Approximation in Constant Rounds
- DOI
- 10.48550/arxiv.2604.00954
- Published
- 2026
- Container
- Not recorded
- Publisher
- arXiv
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.48550/arxiv.2604.00954,
title = {Fully Scalable MPC Algorithms for Euclidean Clustering: Constant Approximation in Constant Rounds},
author = {Czumaj, Artur and Jiang, Shaofeng H. -C. and Jin, Yaonan and Lou, Jianing and Wang, Weicheng},
year = {2026},
doi = {10.48550/arxiv.2604.00954},
url = {https://doi.org/10.48550/arxiv.2604.00954}
}RIS
TY - JOUR TI - Fully Scalable MPC Algorithms for Euclidean Clustering: Constant Approximation in Constant Rounds AU - Czumaj, Artur AU - Jiang, Shaofeng H. -C. AU - Jin, Yaonan AU - Lou, Jianing AU - Wang, Weicheng PY - 2026 DO - 10.48550/arxiv.2604.00954 UR - https://doi.org/10.48550/arxiv.2604.00954 ER -
APA
Artur, C., -C., J. S. H., Yaonan, J., Jianing, L., & Weicheng, W. (2026). Fully Scalable MPC Algorithms for Euclidean Clustering: Constant Approximation in Constant Rounds. https://doi.org/10.48550/arxiv.2604.00954
Source records
- datacite · retrieved 2026-11-09T10:52:24.136Z