Nested Convex Bodies are Chaseable
Not on the map. Why?
Abstract (arXiv)
In the Convex Body Chasing problem, we are given an initial point v0 in Rd and an online sequence of n convex bodies F1, ..., Fn. When we receive Fi, we are required to move inside Fi. Our goal is to minimize the total distance travelled. This fundamental online problem was first studied by Friedman and Linial (DCG 1993). They proved an Omega(√d) lower bound on the competitive ratio, and conjectured that a competitive ratio depending only on d is possible. However, despite much interest in the problem, the conjecture remains wide open. We consider the setting in which the convex bodies are nested: F1 supset ... supset Fn. The nested setting is closely related to extending the online LP framework of Buchbinder and Naor (ESA 2005) to arbitrary linear constraints. Moreover, this setting retains much of the difficulty of the general setting and captures an essential obstacle in resolving Friedman and Linial's conjecture. In this work, we give the first f(d)-competitive algorithm for chasing nested convex bodies in Rd.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Jul 2017.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 0; 2025: 0; 2026: 4.
8 papers.
- 2023Optimal Algorithms for Online Convex Optimization with Adversarial Constraintscited by 34
- 2021Revisiting Smoothed Online Learningcited by 29
- 2023Sequential Strategic Screeningcited by 8
- 2022Smoothed Online Convex Optimization Based on Discounted-Normal-Predictorcited by 6
- 2026Convex Optimization with Nested Evolving Feasible Setscited by 4
- 2026Nested Convex-Body Chasing for Online Optimization with Evolving Feasible Setscited by 1
- 2026Stable Movement for Nondual Lipschitz Convex Optimization: Efficiency and Nearly Optimal Oracle Ratescited by 1
- 2026Convex Optimization with Nested Evolving Feasible Sets (CONES) under Time-Varying Loss Functionscited by 0
- 2026Stable Movement for Nondual Lipschitz Convex Optimization: Efficiency and Nearly Optimal Oracle Rates17 Sep 2026
- 2026Convex Optimization with Nested Evolving Feasible Sets (CONES) under Time-Varying Loss Functions10 Sep 2026
- 2026Nested Convex-Body Chasing for Online Optimization with Evolving Feasible Sets29 Aug 2026
- 2026Convex Optimization with Nested Evolving Feasible Sets8 May 2026
- 2023Optimal Algorithms for Online Convex Optimization with Adversarial Constraints29 Oct 2023
- 2023Sequential Strategic Screening31 Jan 2023
- 2022Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor2 May 2022
- 2021Revisiting Smoothed Online Learning13 Feb 2021