,
Asaf Petruschka
,
Nir Petruschka
Creative Commons Attribution 4.0 International license
Metric decompositions are a fundamental tool in the design of algorithms involving distances. We study fast algorithms for sampling from probabilistic metric decompositions of n-point sets in 𝓁_∞ and 𝓁₂ spaces of high dimension d. For 𝓁_∞, we design a padded-decomposition algorithm that runs in time Õ(nd²), which is near-linear in n, and achieves padding parameter Õ(log n). Our algorithm constructs a new sparse neighborhood cover that is based on geometric properties of 𝓁_∞ [Indyk, JCSS'01], and utilizes recent reductions between covers and decompositions [Conroy and Filtser, STOC'25]. For 𝓁₂, we design a separating-decomposition algorithm that achieves near optimal separation Õ(√{log n}) in almost-linear time n^{1+o(1)}. Our bounds improve over known algorithms with similar running time by a factor Ω(√{log n}), and the techniques have additional applications to spanners and nearest-neighbor search.
@InProceedings{krauthgamer_et_al:LIPIcs.ESA.2026.95,
author = {Krauthgamer, Robert and Petruschka, Asaf and Petruschka, Nir},
title = {{Fast Metric Decompositions in High Dimension}},
booktitle = {34th Annual European Symposium on Algorithms (ESA 2026)},
pages = {95:1--95:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-445-1},
ISSN = {1868-8969},
year = {2026},
volume = {388},
editor = {Bille, Philip and Pettie, Seth and Storandt, Sabine},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2026.95},
URN = {urn:nbn:de:0030-drops-272315},
doi = {10.4230/LIPIcs.ESA.2026.95},
annote = {Keywords: Metric Spaces, Separating Decomposition, Padded Decomposition}
}