Online probabilistic metric embedding: A general framework for bypassing inherent bounds

Yair Bartal, Nova Fandina, Seeun William Umboh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


Probabilistic metric embedding into trees is a powerful technique for designing online algorithms. The standard approach is to embed the entire underlying metric into a tree metric and then solve the problem on the latter. The overhead in the competitive ratio depends on the expected distortion of the embedding, which is logarithmic in n, the size of the underlying metric. For many online applications, such as online network design problems, it is natural to ask if it is possible to construct such embeddings in an online fashion such that the distortion would be a polylogarithmic function of k, the number of terminals. Our first main contribution is answering this question negatively, exhibiting a lower bound of Ω(log k log Φ), where Φ is the aspect ratio of the set of terminals, showing that a simple modification of the probabilistic embedding into trees of Bartal (FOCS 1996), which has expected distortion of O(log k log Φ), is nearly-tight. Unfortunately, this may result in a very bad (polynomial) dependence in terms of k. Our second main contribution is a general framework for bypassing this limitation. We show that for a large class of online problems this online probabilistic embedding can still be used to devise an algorithm with O(min{log k log(kλ), log3 k}) overhead in the competitive ratio, where k is the current number of terminals, and λ is a measure of subadditivity of the cost function, which is at most r, the current number of requests. In particular, this implies the first algorithms with competitive ratio polylog(k) for online subadditive network design (buy-at-bulk network design being a special case), and polylog(k, r) for online group Steiner forest.

Original languageAmerican English
Title of host publication31st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2020
EditorsShuchi Chawla
PublisherAssociation for Computing Machinery
Number of pages20
ISBN (Electronic)9781611975994
StatePublished - 2020
Event31st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2020 - Salt Lake City, United States
Duration: 5 Jan 20208 Jan 2020

Publication series

NameProceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms


Conference31st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2020
Country/TerritoryUnited States
CitySalt Lake City

Bibliographical note

Publisher Copyright:
Copyright © 2020 by SIAM


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