Chasing nested convex bodies nearly optimally

Sébastien Bubeck, Bo'az Klartag, Yin Tat Lee, Yuanzhi Li, Mark Sellke

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


The convex body chasing problem, introduced by Friedman and Linial [FL93], is a competitive analysis problem on any normed vector space. In convex body chasing, for each timestep t ∈ N, a convex body Kt ⊆ Rd is given as a request, and the player picks a point xt ∈ Kt. The player aims to ensure that the total distance moved PTt=01 ||xt−xt+1|| is within a bounded ratio of the smallest possible offline solution. In this work, we consider the nested version of the problem, in which the sequence (Kt) must be decreasing. For Euclidean spaces, we consider a memoryless algorithm which moves to the so-called Steiner point, and show that in an appropriate sense it is exactly optimal among memoryless algorithms. For general finite dimensional normed spaces, we combine the Steiner point and our recent algorithm in [ABC+19] to obtain a new algorithm which is nearly optimal for all `pd spaces with p ≥ 1, closing a polynomial gap.

Original languageEnglish
Title of host publication31st Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2020
EditorsShuchi Chawla
PublisherAssociation for Computing Machinery
Number of pages13
ISBN (Electronic)9781611975994
StatePublished - 2020
Externally publishedYes
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


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