A Nearly-Linear Bound for Chasing Nested Convex Bodies
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Abstract (arXiv)
Friedman and Linial introduced the convex body chasing problem to explore the interplay between geometry and competitive ratio in metrical task systems. In convex body chasing, at each time step t ∈ mathbbN, the online algorithm receives a request in the form of a convex body Kt subseteq ℝd and must output a point xt ∈ Kt. The goal is to minimize the total movement between consecutive output points, where the distance is measured in some given norm. This problem is still far from being understood, and recently Bansal et al. gave an algorithm for the nested version, where each convex body is contained within the previous one. We propose a different strategy which is O(d log d)-competitive algorithm for this nested convex body chasing problem, improving substantially over previous work. Our algorithm works for any norm. This result is almost tight, given an Omega(d) lower bound for the ℓ∞.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jun 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 5.
9 papers.
- 2023Optimal Algorithms for Online Convex Optimization with Adversarial Constraintscited by 34
- 2021Revisiting Smoothed Online Learningcited by 29
- 2022Chasing Convex Bodies and Functions with Black-Box Advicecited by 19
- 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
- 2026Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertaintycited by 0
- 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
- 2026Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertainty13 Aug 2026
- 2026Convex Optimization with Nested Evolving Feasible Sets8 May 2026
- 2023Optimal Algorithms for Online Convex Optimization with Adversarial Constraints29 Oct 2023
- 2022Chasing Convex Bodies and Functions with Black-Box Advice23 Jun 2022
- 2022Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor2 May 2022
- 2021Revisiting Smoothed Online Learning13 Feb 2021